Rozprawy doktorskie: „Power quality disturbances”

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Parsons, Antony Cozart. "Automatic location of transient power quality disturbances /". Digital version accessible at:, 1999. http://wwwlib.umi.com/cr/utexas/main.

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Dwijaya, Saputra I. "Detection of power disturbances for power quality monitoring using mathematical morphology". Thesis, University of Liverpool, 2017. http://livrepository.liverpool.ac.uk/3009798/.

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In power quality monitoring, determining the type of power quality disturbance occurring in the power system is important. Some disturbances such as, voltage dip, momentary interruption, voltage swell, or oscillatory transients in power systems may result internal-function or failure in the operation of some devices. Knowing the location where the disturbances occur in the system can yield an effective and efﬁcient result when an appropriate method is applied in the attempt to solve the power quality issues. Some traditional strategies such as, wavelet or Fast Fourier transforms have been applied to detect and locate power quality disturbances, suffer from the complexity of the algorithm and the calculation load. In this thesis, mathematical morphology has been investigated for this purpose due to the merits of robustness and the simple calculations needed. In this thesis, some novel strategies using mathematical morphology are presented to ﬁnd the time location of the disturbances, that is deﬁned as the start and end points when the disturbance occur in the time domain. The ﬁrst method was using morphology gradient, top-hat transform, and Skeletonization to identify the time location of the disturbances and noise in the system, and then plotting the results in 3D for pattern recognition. This Skeletonization is also combined with Morphology Edge Detection to ﬁnd the accurate time location of disturbances in the system for both noise free signals and signals with noise. The overall result shows the reduction of the error was signiﬁcant compared to the result of morphology edge detection strategy. Another novel strategy is presented by converting a signal to an image then applying image processing techniques, which are then evaluated using a control chart to ﬁnd the time location of any disturbances. This conversion strategy is also applied for detecting the times of power quality disturbances uses short data samples of the signal (4 samples), so that it can be implemented as a real time detection strategy. The results show an accurate strategy in detecting disturbances. Half Multi-resolution Morphology Gradients (HMMG) based on multi-resolution morphology gradients (MMG) is also presented as a novel strategy and it operates in level 1 only, reducing the processing and increase the speed of detection of disturbance. The results show accurate detection when disturbances occur in the system. Other applications of MM are also presented such as a new alternative method in estimating the frequency in a signal based on top-hat and bottom-hat transforms with the results showing the ability of this method to handle low frequencies when the signal is a noise free signal. Neural networks are also implemented with MM for the identiﬁcation and classiﬁcation of disturbances. All the novel strategies using Skeletonization, signal/image conversion and HMMG for disturbances detection were then evaluated using a real dataset and an experimental dataset. Overall results show that this three methods can detect disturbances accurately.

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Settipalli, Praveen. "AUTOMATED CLASSIFICATION OF POWER QUALITY DISTURBANCES USING SIGNAL PROCESSING TECHNIQUES AND NEURAL NETWORKS". UKnowledge, 2007. http://uknowledge.uky.edu/gradschool_theses/430.

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This thesis focuses on simulating, detecting, localizing and classifying the power quality disturbances using advanced signal processing techniques and neural networks. Primarily discrete wavelet and Fourier transforms are used for feature extraction, and classification is achieved by using neural network algorithms. The proposed feature vector consists of a combination of features computed using multi resolution analysis and discrete Fourier transform. The proposed feature vectors exploit the benefits of having both time and frequency domain information simultaneously. Two different classification algorithms based on Feed forward neural network and adaptive resonance theory neural networks are proposed for classification. This thesis demonstrates that the proposed methodology achieves a good computational and error classification efficiency rate.

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4

Axelberg, Peter. "On Tracing Flicker Sources and Classification of Voltage Disturbances". Doctoral thesis, Högskolan i Borås, Institutionen Ingenjörshögskolan, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-3416.

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Developments in measurement technology, communication and data storage have resulted in measurement systems that produce large amount of data. Together with the long existing need for characterizing the performance of the power system this has resulted in demand for automatic and efficient information-extraction methods. The objective of the research work presented in this thesis was therefore to develop new robust methods that extract additional information from voltage and current measurements in power systems. This work has contributed to two specific areas of interest.The first part of the work has been the development of a measurement method that gives information how voltage flicker propagates (with respect to a monitoring point) and how to trace a flicker source. As part of this work the quantity of flicker power has been defined and integrated in a perceptionally relevant measurement method. The method has been validated by theoretical analysis, by simulations, and by two field tests (at low-voltage and at 130-kV level) with results that matched the theory. The conclusion of this part of the work is that flicker power can be used for efficient tracing of a flicker source and to determine how flicker propagates.The second part of the work has been the development of a voltage disturbance classification system based on the statistical learning theory-based Support Vector Machine method. The classification system shows always high classification accuracy when training data and test data originate from the same source. High classification accuracy is also obtained when training data originate from one power network and test data from another. The classification system shows, however, lower performance when training data is synthetic and test data originate from real power networks. It was concluded that it is possible to develop a classification system based on the Support Vector Machine method with “global settings” that can be used at any location without the need to retrain. The conclusion is that the proposed classification system works well and shows sufficiently high classification accuracy when trained on data that originate from real disturbances. However, more research activities are needed in order to generate synthetic data that have statistical characteristics close enough to real disturbances to replace actual recordings as training data.

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Ando, Junior Oswaldo Hideo. "Desenvolvimento de uma metodologia para identificar e quantificar distúrbios da qualidade da energia elétrica". reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2009. http://hdl.handle.net/10183/18419.

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Este trabalho apresenta uma metodologia para análise e monitoração da qualidade da energia elétrica, através da identificação e quantificação dos distúrbios eletromagnéticos. A metodologia utiliza técnicas de processamento digital de sinais, possibilitando a construção de filtros digitais, a detecção de eventos e a estimativa da freqüência dos sinais elétricos analisados. Os principais distúrbios da qualidade definidos pelas normas da ANEEL e do ONS são quantificados através do algoritmo proposto. O programa desenvolvido foi testado usando formas de ondas com distúrbios previamente conhecidos para sua validação. Analisando formas de onda obtidas de medições em campo verificou-se a robustez do algoritmo frente a ruídos e outros fenômenos vinculados à qualidade da energia presentes em medições reais. Esta pesquisa apresenta um programa eficaz e prático que pode ser utilizado no desenvolvimento de um novo equipamento de medição dos distúrbios da QEE. Os resultados obtidos através da análise de dados (sintetizados e medições de campo) validaram o programa proposto.
This dissertation presents a methodology for detection and quantification of power quality disturbances. Digital signal processing (DSP) is applied to simulated and measured disturbances. The use of DSP enables the construction of digital filters for the detection of events, and the estimation of the frequency of voltage and current signals. The disturbances set by ANEEL and ONS standards are quantified by the proposed algorithm. The program developed was tested using simulated disturbance for its validation. Field measurements were used to assess the robustness of the algorithm against signal noise and other disturbances. This research presents effective and practical software that can be used to develop a new device for measuring the disturbances of Power Quality. The results obtained through the analysis of data (simulated and field measurements) validated the proposed algorithm.

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Rosado, Sebastian Pedro. "Analysis of Electric Disturbances from the Static Frequency Converter of a Pumped Storage Station". Thesis, Virginia Tech, 2001. http://hdl.handle.net/10919/34448.

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The present work studies the disturbances created in the electric system of a pumped storage power plant, which is an hydraulic generation facility where the machines can work as turbines or pumps, by the operation of a static frequency converter (SFC). The SFC is used for starting the synchronous machines at the station when in the pump mode. During the starting process several equipment is connected to the SFC being possible to get affected by the disturbances generated. These disturbances mainly include the creation of transient overvoltages during the commutation of the semiconductor devices of the SFC and the introduction of harmonics in the network currents and voltages. This work analyzes the possible effects of the SFC operation over the station equipment based on computer simulations. For this purpose, the complete system was modeled and the starting process simulated in a computer transient simulator program. The work begins with a general review of the effects of electric disturbances over high voltage equipment and in particular of the disturbances generated by power electronics conversion equipment. Then the models for the different kind of equipment present in the system are discussed and formulated. The control system that governs the operation of the SFC during the starting process is analyzed later as well as the operation conditions. Once the model of the system is set up, the harmonic analysis of the electric network is done by frequency domain and time domain methods. Time domain methods are also employed for the analysis of the commutation transient produced by the SFC operation. Finally, the simulation results are used to evaluate the impact of the SFC operation on the station equipment, especially on the generator step up transformer.
Master of Science

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Alshahrani, Saeed Sultan. "Detection, classification and control of power quality disturbances based on complementary ensemble empirical mode decomposition and artificial neural networks". Thesis, Brunel University, 2017. http://bura.brunel.ac.uk/handle/2438/15872.

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Moses, Paul S. "Operation and performance of three-phase asymmetric multi-leg power transformers subjected to nonlinear and dynamic electromagnetic disturbances". Thesis, Curtin University, 2012. http://hdl.handle.net/20.500.11937/1529.

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Three-phase power transformers continue to be an important fixture in modern power systems since their initial development in the 1880s. While transformer design has fundamentally remained the same, the operating environment has significantly changed. This is apparent through new flexible network operations (e.g., integration of renewable energy sources), growing network complexities (e.g., deployment of micro-grids, smart grids, etc.) and increasing use of nonlinear power electronic equipment (e.g., power converters and motor drives). Thus the issue of power quality in power systems has become an important consideration to utilities and industries as the performance of electrical machines and devices could be adversely affected. This doctoral thesis focuses on the performance of three-phase power transformers under various nonlinear and dynamic electromagnetic disturbances in distorted power networks.The first part of this work is devoted to the development and improvement of nonlinear electromagnetic models of three-phase multi-leg transformer cores for the study of steady-state and transient electromagnetic disturbances. This is mainly achieved by developing new detailed magnetic models for ferromagnetic nonlinearities (e.g., hysteresis) as well as considering core asymmetry and magnetic couplings of core-leg fluxes in three-phase multi-leg iron-core structures. These combined effects have not been considered in conventional electromagnetic transient studies of transformers and are shown in this work for the first time to have a significant impact on predicted steady-state and transient electromagnetic behaviour.In subsequent parts of this thesis, the developed models are applied to the examination of selected nonlinear electromagnetic phenomena such as transformer operation in harmonically distorted power systems (e.g., terminal voltage distortions and nonlinear loads), dc bias caused by geomagnetically induced currents, ferroresonance, and no-load magnetisation and inrush current effects. Furthermore, based on the new modelling approaches, improved methods are presented for estimating transformer aging with wider applicability to three-phase transformers considering load and source imbalances with harmonic distortions.With the advent of newly emerging smart grids, the last part of this thesis is devoted to exploring future transformer operation in new smart grid operating conditions such as plug-in electric vehicle charging. Transformer loading patterns with random uncoordinated PEV charging compared to coordinated charging activity in smart grids is investigated. The investigation highlights the notion of harnessing future smart grid technologies to better manage transformer health and performance.

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Al, Abed Isrà. "Measurement System at Large Bandwidth for Quality Evaluation of Electric Energy up to 150 kHz". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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Nowadays, Power Quality monitoring is an essential service many utilities perform for their industrial and other key commercial customers. Monitoring power quality has become specifically more effective in recent years due to the advent of the technology and software. Not only can a monitoring system provide information such as quality of the power and the causes of power system disturbances, but it is also able to identify problem conditions throughout the system prior they cause widespread customer complaints, equipment malfunctions, and even equipment failures. Power Quality problems are not necessarily limited to the utility power system. Many surveys have shown that most problems are localized within customer facilities. Essential requirements for a successful monitoring system include extensive data processing capabilities, easily understood reporting, and universal sharing of information.

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Oubrahim, Zakarya. "On electric grid power quality monitoring using parametric signal processing techniques". Thesis, Brest, 2017. http://www.theses.fr/2017BRES0102/document.

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Cette thèse porte sur la surveillance des perturbations de la qualité de l’énergie d’un réseau électrique via des techniques paramétriques de traitement du signal. Pour élaborer nos algorithmes de traitement du signal, nous avons traité les problèmes d’estimation des différentes grandeurs du réseau électrique triphasé et de classification des perturbations de la qualité d'énergie. Pour ce qui est du problème d’estimation, nous avons développé une technique statistique basée sur le maximum de vraisemblance. La technique proposée exploite la nature multidimensionnelle des signaux électriques. Elle utilise un algorithme d’optimisation pour minimiser la fonction de vraisemblance. L’algorithme utilisé permet d’améliorer les performances d’estimation tout en étant d’une faible complexité calculatoire en comparaison aux algorithmes classiques. Une analyse plus poussée de l’estimateur proposé a été effectuée. Plus précisément, ses performances sont évaluées sous un environnement incluant entre autres la pollution harmonique et interharmonique et le bruit. Les performances sont également comparées aux exigences de la norme IEEE C37.118.2011. La problématique de classification dans les réseaux électriques triphasés a plus particulièrement concerné les perturbations que sont les creux de tension et les surtensions. La technique de classification proposée consiste globalement en deux étapes : 1) une pré-classification du signal dans l’une des 4 préclasses établis et en 2) une classification du type de perturbation à l’aide de l’estimation des composants symétriques.Les performances du classificateur proposé ont été évaluées, entre autres, pour différentes nombre de cycles, de SNR et de THD. L’estimateur et le classificateur proposés ont été validés en simulation et en utilisant les données d’un réseau électrique réel du DOE/EPRI National Database of Power System Events. Les résultats obtenus illustrent clairement l’efficacité des algorithmes proposés quand à leur utilisation comme outil de surveillance de la qualité d’énergie
This thesis deals with electric grid monitoring of power quality (PQ) disturbances using parametric signal processing techniques. The first contribution is devoted to the parametric spectral estimation approach for signal parameter extraction. The proposed approach exploits the multidimensional nature of the electrical signals.For spectral estimation, it uses an optimization algorithm to minimize the likelihood function. In particular, this algorithm allows to improve the estimation accuracy and has lower computational complexity than classical algorithms. An in-depth analysis of the proposed estimator has been performed. Specifically, the estimator performances are evaluated under noisy, harmonic, interharmonic, and off-nominal frequency environment. These performances are also compared with the requirements of the IEEE Standard C37.118.2011. The achieved results have shown that the proposed approach is an attractive choice for PQ measurement devices such as phasor measurement units (PMUs). The second contribution deals with the classification of power quality disturbances in three-phase power systems. Specifically, this approach focuses on voltage sag and swell signatures. The proposed classification approach is based on two main steps: 1) the signal pre-classification into one of 4 pre-classes and 2) the signature type classification using the estimate of the symmetrical components. The classifier performances have been evaluated for different data length, signal to noise ratio, interharmonic, and total harmonic distortion. The proposed estimator and classifier are validated using real power system data obtained from the DOE/EPRI National Database of Power System Events. The achieved simulations and experimental results clearly illustrate the effectiveness of the proposed techniques for PQ monitoring purpose

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Carvalho, Flavio Gomes de. "Caracterização do conteúdo harmônico em ambientes residenciais: estudo de caso". Universidade Federal da Paraíba, 2015. http://tede.biblioteca.ufpb.br:8080/handle/tede/7556.

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In this work a characterization of harmonic content in residential electrical systems through a set of measurements over various periods of time is performed. For each reported case it was a statement of charges that make up the installation and an individual analysis of each of them as a way to assess their influence on the harmonic behavior of the installation as a whole.
Neste trabalho é realizada uma caracterização do conteúdo harmônico em instalações elétricas residenciais por meio de um conjunto de medições realizadas por diversos períodos de tempo. Para cada caso avaliado fez-se um levantamento das cargas que compõem a instalação e uma análise individual de cada uma delas como forma de avaliar sua influência no comportamento harmônico da instalação como um todo.

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Arruda, Elcio Franklin de. "Análise de distúrbios relacionados com a qualidade da energia elétrica utilizando a transformada Wavelet". Universidade de São Paulo, 2003. http://www.teses.usp.br/teses/disponiveis/18/18133/tde-25102007-090916/.

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O presente trabalho visa a utilização da transformada Wavelet no monitoramento do sistema elétrico no que diz respeito a problemas de qualidade da energia com o intuito de detectar, localizar e classificar os mesmos. A transformada Wavelet tem surgido na literatura como uma nova ferramenta para análise de sinais, utilizando funções chamadas Wavelet mãe para mapear sinais em seu domínio, fornecendo informações simultâneas nos domínios tempo e freqüência. A transformada Wavelet é realizada através de filtros decompondo-se um dado sinal em análise multiresolução. Por esta, obtém-se a detecção e a localização de distúrbios relacionados com a qualidade da energia decompondo-se o sinal em dois outros que representam uma versão de detalhes (correspondente as altas freqüências do sinal) e uma versão de aproximação (correspondente as baixas freqüências do sinal). A versão de aproximação é novamente decomposta obtendo-se novos sinais de detalhes e aproximações e assim sucessivamente. Sendo assim, os distúrbios podem ser detectados e localizados no tempo em função do seu conteúdo de freqüência. Estas informações fornecem também características únicas pertinentes a cada distúrbio, permitindo classificá-los. Desta forma, propõe-se neste trabalho o desenvolvimento de um algoritmo classificador automático de distúrbios relacionados com a qualidade da energia baseado unicamente nas decomposições obtidas da análise multiresolução.
The aim of the present dissertation is to apply the Wavelet transform to analyze power quality problems, detecting, localizing and classifying them. The topic Wavelet transform, has appeared in the literature as a new tool for the analysis of signals, using functions called mother Wavelet to map signals in its domain, supplying information in the time and frequency domain, simultaneously. Wavelet transform is accomplished through filters decomposing a provided signal in multiresolution analysis. The detection and localization of disturbances are obtained by decomposing a signal into two other signals that represent, a detailed version (high frequency signals) and a smoothed version (low frequency signals). The smoothed version is decomposed again, and new detailed and smoothed signals are obtained. This process is repeated as many times as necessary and the disturbances can be detected and localized in the time as a function of its level frequency. This information also supplies characteristics to each disturbance, allowing classifying them. Thus, this research presents a way to develop an automatic classifying algorithm of power quality disturbances, based only on multiresolution analysis.

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Arruda, Bruno Willian de Souza. "Classificação de distúrbios de energia elétrica baseada em sistemas imunológicos artificiais". Universidade Federal da Paraíba, 2015. http://tede.biblioteca.ufpb.br:8080/handle/tede/7557.

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Nowadays, electricity assumes an essential role in the sustainability of modern society. The requirement and consumer demand for power quality are growing along with the advancement of technology and the increasing use of non-linear loads. This paper presents an application of artificial immune systems, focusing on the clonal selection algorithm, for power quality disturbances classification. The algorithm uses an initial population of antibodies to generate high affinity memory cells capable of recognizing antigenic electrical disturbances during each half cycle of the fundamental frequency voltage signal. The results demonstrate the algorithm's ability to classify disturbances such as sag, swell, outage and harmonics, with 100% efficiency rating. Another important feature of this approach is that it can be embedded, since the online stage classification has a low computational complexity with processing time around 103 μs. Based on comparative study with other studies, the results showed up best.
Na atualidade, a energia elétrica assume um papel imprescindível para a sustentabilidade da sociedade moderna. Com o avanço da tecnologia e a utilização cada vez maior de cargas não-lineares, são crescentes a exigência e a demanda dos consumidores em relação à qualidade de energia elétrica. Neste trabalho é apresentada uma aplicação de sistemas imunológicos artificiais, tendo como foco o algoritmo de seleção clonal, para classificação de distúrbios de energia elétrica. O algoritmo utiliza uma população inicial de anticorpos para gerar células de memória de alta afinidade antigênica capazes de reconhecer distúrbios de energia elétrica a cada meio ciclo da frequência fundamental do sinal de tensão. Os resultados obtidos demonstram a capacidade do algoritmo em classificar distúrbios, tais como afundamentos, elevações, interrupções e harmônicos, com 100% de eficiência de classificação. Outra característica importante desta abordagem é que ela pode ser embarcada, uma vez que a fase online de classificação apresenta baixa complexidade computacional com tempo de processamento em torno de 103 μs. Baseado no estudo comparativo com outros trabalhos, os resultados obtidos apresentaram-se melhores.

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Phan, Anh Tuan. "Power Systems Model Developments for Power Qality Monitoring : Application to Fundamental Frequency and Unbalance Estimation". Thesis, Mulhouse, 2016. http://www.theses.fr/2016MULH8692/document.

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Les énergies renouvelables, l’énergie sous la forme électrique et son transport à l’aide de réseaux électriques intelligents représentent aujourd’hui des enjeux majeurs car ils ont de grands impacts environnementaux et sociétaux. Ainsi, la production, le transport et la gestion de l’énergie électrique, continuent toujours à susciter un intérêt croissant. Pour atteindre ces objectifs, plusieurs verrous technologiques doivent être levés. Au-delà des questions liées aux architectures des réseaux électriques, aux modèles, aux outils de dimensionnement, à la formalisation de caractéristiques et d’indicateurs, aux contraintes et aux critères, à la gestion et à la production décentralisée, la qualité de la puissance électrique est centrale pour la fiabilité de l’ensemble du système de distribution. Les perturbations affectent la qualité des signaux électriques et peuvent provoquer des conséquences graves sur les autres équipements connectés au réseau. Les travaux de cette thèse s’inscrivent dans ce contexte et de fait ils sont orientés vers le développement de modèles, d’indicateurs et de méthodes de traitement des signaux dédiés à la surveillance en temps-réel des performances des réseaux de distribution électrique.Cette thèse analyse la qualité de la puissance électrique, en prenant en compte plusieurs caractéristiques bien connues ainsi que leur pertinence. Les modèles des systèmes électriques et les méthodes de traitement du signal pour estimer leurs paramètres sont étudiés pour des applications en temps-réel de surveillance, de diagnostic et de contrôle sous diverses conditions. Parmi tous, la fréquence fondamentale est l’un des paramètres les plus importants pour caractériser un système de distribution électrique. En effet, sa valeur qui est censée être une constante, varie en permanence et reflète la dynamique de l’énergie électrique disponible. La fréquence peut également être affectée par certaines productions d’énergie renouvelable et peut être influencée par des mauvaises synchronisations de certains équipements. En outre, la puissance absorbée par les charges ou produite par des sources est généralement différente d’une phase à l’autre. Évidemment, la plupart des installations électriques existantes avec plusieurs phases, qu’elles soient résidentielles ou industrielles, travaillent dans des conditions déséquilibrées. Identifier les composantes symétriques de tension est dans ce cas un moyen pertinent pour quantifier le déséquilibre entre les phases d’un système électrique.De nouvelles représentations de type espace d’état et modélisant des systèmes électriques sont proposées pour estimer la fréquence fondamentale et pour identifier les composantes symétriques de tension des systèmes électriques triphasés et déséquilibrés. Le premier modèle d’espace d’état proposé considère la fréquence fondamentale comme connue ou obtenue par un autre estimateur. En contrepartie, il fournit les autres paramètres caractérisant le système électrique. Un second modèle d’état-espace est introduit. Il est original dans le sens où il ne nécessite aucune connaissance de la fréquence fondamentale. Une de ses variables d’état est directement reliée à la fréquence et permet donc de la déduire. En outre, ce nouvel espace d’état est parfaitement capable de représenter des systèmes électriques à trois phases équilibrés et non équilibrés. [...]
Renewable energy, electricity and smart grids are core subjects as they have great environmental and societal impacts. Thus, generating, transporting and managing electric energy, i.e., power, still continue to drive a growing interest. In order to properly achieve these goals, several locks must be removed. Beyond issues related to the distribution architecture, the formalization of models, sizing tools, features and indicators, constraints and criteria, decentralized generation and energy management, power quality is central for the whole grid’s reliability. Disturbances affect the power quality and can cause serious impact on other equipment connected to the grid. The work of this thesis is part of this context and focuses on the development of models, indicators, and signal processing methods for power quality monitoring in time-varying power distribution systems.This thesis analyzes the power quality including several well-known features and their relevance. Power system models and signal processing methods for estimating their parameters are investigated for the purpose of real-time monitoring, diagnostic and control tasks under various operating conditions. Among all, the fundamental frequency is one of the most important parameters of a power distribution system. Indeed, its value which is supposed to be a constant varies continuously and reflects the dynamic availability of electric power. The fundamental frequency can also be affected by renewable energy generation and by nasty synchronization of some devices. Moreover, the power absorbed by loads or produced by sources is generally different from one phase to the other one. Obviously, most of the existing residential and industrial electrical installations with several phases work under unbalanced conditions. Identifying the symmetrical components is therefore an efficient way to quantify the imbalance between the phases of a grid. New state-space representations of power systems are proposed for estimating the fundamental frequency and for identifying the voltage symmetrical components of unbalanced three-phase power systems. A first state-space representation is developed by supposing the fundamental frequency to be known or to be calculated by another estimator. In return, it provides other parameters and characteristics from the power system. Another original state-space model is introduced which does not require the fundamental frequency. Here, one state variable is a function of the frequency which can thus be deduced. Furthermore this new state-space model is perfectly are able to represent a three-phase power system in both balanced and unbalanced conditions. This not the case of lots of existing models. The advantage of the proposed state-space representation is that it gives directly access to physical parameters of the system, like the frequency and the amplitude and phase values of the voltage symmetrical components. Power systems parameters can thus be estimated in real-time by using the new state-space with an online estimation process like an Extended Kalman Filter (EKF). The digital implementation of the proposed methods presents small computational requirement, elegant recursive properties, and optimal estimations with Gaussian error statistics.The methods have been implemented and validated through various tests respecting real technical constraints and operating conditions. The methods can be integrated in active power filtering schemes or load-frequency control strategies to monitor power systems and to compensate for electrical disturbances

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Carlsson, Fredrik. "On impacts and ride-through of voltage sags exposing line-operated AC-machines and metal processes". Doctoral thesis, KTH, Electrical Systems, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3681.

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During the last decade, power quality has been recognised asa global problem. Among different types of power qualityproblems, voltage sags have been identified to be one of themost severe problems for different process industries. The mostcommon reason to voltage sags is lightning strikes in powerlines. Protection equipment, usually located at switchyards,disconnect faulted power lines as soon as possible, which isapproximately 100 ms. Thus, the duration of voltage sags areapproximately 100 ms. The sensitivity to voltage sags ofelectrical equipment in process industries can be observed asfor instance malfunction, automatic turnoff or damages.

This thesis gives an overview of three metals processes withfocus on the sensitivity to voltage sags and interruptions. Theinherent energy in the process is used to find the sensitivity.This energy may also be used to obtain "ride-through" for theprocesses. The three metals processes are a blast furnaceprocess, a hot rolling mill process and a cold rolling millprocess. The main attention in this thesis is paid to the blastfurnace process, which is powered by a line-operatedsynchronous machine.

The thesis shows that the protection equipment forelectrical machines can be adjusted to avoid unnecessaryshutdowns. It is also explained why there are high torque andcurrents during voltage sags as well as after voltage sags. Itis shown that the first peak torque and current during thevoltage sags is almost proportional to the voltage change, thatis the voltage magnitude before the voltage sag minus thevoltage magnitude during the voltage sag. The first peak torqueand current after the voltage sag depends sinusoidal-like onthe duration of the voltage sag and almost proportional to thevoltage change during the voltage sag. There is no fluxsaturation during voltage sags, however after voltage sagssaturation is very likely to occur. The thesis explains why andalso how the flux is changed during and after voltage sags.

The duration of voltage sags is in many cases set by theprotection equipment located in switchyards. It is shown thatthe durations of voltage sags can be changed to durations thatwill cause less peak torque and current after voltage sags forline-operated AC-machines. It is also shown how this istheoretically achieved.

Keywords:Rolling mill, Blast furnace, Power Quality,Synchronous machine, Asynchronous machine, Voltage sag, Voltageinterruption, Ride-through, Process disturbances, Simulation,Modelling

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Cândido, Marcos Rogério. "Aplicação da transformada Wavelet na análise da qualidade de energia em fornos elétricos a arco". Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/3/3143/tde-09022009-181024/.

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Neste trabalho, desenvolveu-se um novo método para a detecção e classificação dos distúrbios que afetam a qualidade de energia elétrica em sistemas elétricos industriais na presença de fornos elétricos a arco. Durante o processo de fusão dos fornos elétricos a arco, ocorrem diversos eventos que afetam o sistema elétrico ao qual estão inseridos, tendo como características: forma de onda do sinal de corrente altamente desequilibradas e com grande distorção devido aos harmônicos, efeitos de cintilação; bem como afundamento e elevação nos sinais de tensão. O método ora proposto foi aplicado a sinais reais, permitindo a detecção e classificação dos distúrbios múltiplos na forma de onda do sinal de tensão, proveniente da operação dos fornos elétricos a arco. Para tal, foi usada como base do algoritmo, uma técnica baseada na Transformada Wavelet, aplicada aos sinais não-estacionários de uma instalação industrial com três fornos elétricos a arco.
A new method for the detection and classification of the disturbances that affect the electric power quality in industrial electric systems with electric arc furnaces was developed in this work. During the fusion process of the electric arc furnaces, may occur several events that affect the electric system to which it is inserted may occur, having as characteristic: waveform of the signal of current highly unbalanced and with great distortion due to the harmonic, scintillation effects; as well as sag and swell in the voltage signals.The method proposed was applied to real signals, allowing the detection and classification of the multiple disturbances in the waveform of the voltage signal originating from the operation of the electric arc furnace. For this purpose, a technique based on Wavelet Transform will be used and applied to the not-stationary signals of an industrial installation with three electric arc furnaces.

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Borges, Fábbio Anderson Silva. "Extração de características combinadas com árvore de decisão para detecção e classificação dos distúrbios de qualidade da energia elétrica". Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/18/18153/tde-01102013-104201/.

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Este trabalho apresenta uma metodologia de detecção e classificação de distúrbios relacionados à qualidade da energia elétrica. A detecção é feita utilizando-se somente uma regra para inferir na presença ou não do distúrbio em uma janela analisada. Para a classificação é proposto um método baseado em árvore de decisão. A árvore recebe como entrada as características do sinal extraídas tanto no domínio do tempo como no domínio da frequência, sendo a última obtida pela Transformada de Fourier. Destaca-se que toda a metodologia de extração de características foi idealizada como tentativa de se reduzir ao máximo o esforço computacional das tarefas de detecção e classificação de distúrbios. Em suma, verifica-se que os resultados obtidos são satisfatórios para a proposta desta pesquisa.
This work presents a methodology for detection and classification of disturbance related to the electric power quality. The detection is performed using only one rule to infer in the presence or not of the disturbance in a window analyzed. For the classification is proposed a method based on decision tree. The tree receives as input features of the extracted signal both in time domain and in the frequency domain, being the last obtained by Fourier transform. It is emphasized that all the features extraction methodology was idealized as an attempt to reduce to the maximum the computational effort for the tasks of detection and classification of disturbances. In short, it is possible to verify that the results obtained are satisfactory for the purpose of this research.

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Borges, Fábbio Anderson Silva. "Método híbrido baseado no algoritmo k-means e regras de decisão para localização das fontes de variações de tensões de curta duração no contexto de Smart Grid". Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/18/18153/tde-04102017-105849/.

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No contexto de Smart Grids, determinar a correta localização das fontes causadoras de Variação de Tensão de Curta Duração (VTCD) não é uma tarefa simples, devido à curta duração destes eventos e também, por sua rápida propagação nas redes de distribuição de energia elétrica. Neste sentido, esse trabalho apresentou um método híbrido recursivo baseado em ferramentas da área de aprendizado de máquinas (algoritmo de agrupamento e base de regras), o qual é capaz de localizar as fontes de VTCD, a partir da análise dos das características dos distúrbios disponibilizadas pelos smart meters instalados no sistema. Assim, o trabalho destinouse ao desenvolvimento de uma plataforma em hardware para aquisição, detecção e classificação dos distúrbios, através de um Sistema Operacional de Tempo Real. Em seguida o algoritmo de agrupamento (k-means) agrupou os dados dos medidores de forma a definir dois clusters, onde um deles correspondeu aos medidores que estão longe da região que ocorreu o distúrbio e o outro, correspondeu aos medidores que estavam localizados próximos da região de ocorrência do distúrbio. Na segunda etapa, um sistema baseado em regras determinou qual dos clusters abrangeu o nó de origem. No entanto, quando o algoritmo determinou uma região muito grande, essa região é introduzida recursivamente, como entrada da metodologia desenvolvida, para refinar a região de localização. O sistema resultante foi capaz de estimar a região de localização com uma taxa de acerto acima de 90%. Assim, o método teve sua concepção adequada ao empregado nos centros de controle e operações de concessionárias de energia elétrica, visando apoiar a decisão do corpo técnico para que ações corretivas fossem estabelecidas de forma assertiva.
In the Smart Grids context, the correct location of short-duration voltage variations sources is not a trivial task, because of the short duration of these events and for rapid propagation in the distribution feeder. In this sense, aiming to develop a recursive hybrid method based on machine learning area tools (clustering algorithm and rule base) that is able to locate the sources of short-duration voltage variations, it was used data from smart meters installed along the distribution feeder. The recursive hybrid method, as input, received the disturbance characteristics provided by the meters installed in the system. Thus, this thesis aimed to development of a measurement hardware for signal acquisition, detection, classification through a realtime operating system. Then, k-means clustering algorithm grouped the meters data in order to define two clusters, where one of them corresponded to the meters that were distant from the region that occurred the disturbance and the other one corresponded to the meters, which were located near to the disturbance occurrence region. In a second step, a rule-based system determined which of the clusters corresponded to the source node. When the algorithm determined a very large region, that region was recursively introduced as input of the developed methodology to decrease its size. The resulting system was able to estimate the location region with a accuracy above 90%. Therefore, this method showed a suitable design for employment by operation control centers of power sector concessionaires, aiming to support technical staff decision to stablish assertive corrective actions.

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Souza, Silvio Aparecido de. "Utilização da Transformada de Fourier Janelada para caracterização de distúrbios na qualidade da energia elétrica". Universidade de São Paulo, 2004. http://www.teses.usp.br/teses/disponiveis/18/18133/tde-12052017-104820/.

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Este trabalho apresenta um estudo da aplicação da Transformada de Fourier Janelada à Qualidade da Energia Elétrica. Esta abordagem procura, pela implementação de um algoritmo computacional, detectar, localizar e classificar eventuais distúrbios que ocorrem em um determinado Sistema Elétrico. Da situação atual, tem-se que variações nas formas de ondas dos sinais de tensão como elevação, afundamento e interrupção de tensão, oscilações transitórias e ruídos, são freqüentes, chamando a atenção para a qualidade da energia elétrica fornecida pelas concessionárias. A análise de tais fenômenos, que normalmente é descrita no domínio do tempo (resolução de equação diferencial) ou no domínio da freqüência (através da Transformada de Fourier), pode agora ser analisada simultaneamente em ambos os domínios do tempo e da freqüência, dispondo-se da Transformada de Fourier Janelada. As janelas utilizadas para esta finalidade foram as de Hanning, retangular e a de Kaiser. Para esta análise em específico, a simulação dos diversos distúrbios ocorridos no sistema de distribuição foi realizada através do software ATP - Alternative Transients Program - cujas características seguem corretamente um sistema real da concessionária CPFL - Companhia Paulista de Força. Os testes efetuados mostraram que a Transformada de Fourier Janelada possui uma grande potencialidade quanto à sua aplicação na avaliação da qualidade da energia elétrica.
This dissertation presents a study of Windowed Fourier Transform applied to Power Quality. By the implementation of a computational algorithm, this approach aims to detect, locate and classify disturbances that may occur in Power Systems. Variations in voltage waveforms, such as sag, swell, interruption, oscillatory transient and noise have became frequent in electric systems, attracting the attention to the power quality supplied. The analysis of such phenomena, which is usually described either in the time domain (differential equation resolution) or in the frequency domain (Fourier Transform), can now be analyzed simultaneously in both domains: time and frequency, by the windowed Fourier Transform. The windows used to provide this information are the Hanning, rectangular and Kaiser. The simulation of the diverse disturbances occurred in the distribution system was accomplished by means of ATP software - Alternative Transients Program - whose characteristics correctly follow a real distribution system of CPFL electric utility. The tests show the windowed Fourier Transform has a great potentiality when applied to evaluate the power quality.

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Santos, Crisluci Karina Souza. "Classifica??o de dist?rbios na rede el?trica usando redes neurais e wavelets". Universidade Federal do Rio Grande do Norte, 2008. http://repositorio.ufrn.br:8080/jspui/handle/123456789/15119.

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Made available in DSpace on 2014-12-17T14:54:49Z (GMT). No. of bitstreams: 1 CrisluciKSS.pdf: 1753956 bytes, checksum: 06fc893387f3832c2cc344c281169f6d (MD5) Previous issue date: 2008-10-13
Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior
Post dispatch analysis of signals obtained from digital disturbances registers provide important information to identify and classify disturbances in systems, looking for a more efficient management of the supply. In order to enhance the task of identifying and classifying the disturbances - providing an automatic assessment - techniques of digital signal processing can be helpful. The Wavelet Transform has become a very efficient tool for the analysis of voltage or current signals, obtained immediately after disturbance s occurrences in the network. This work presents a methodology based on the Discrete Wavelet Transform to implement this process. It uses a comparison between distribution curves of signals energy, with and without disturbance. This is done for different resolution levels of its decomposition in order to obtain descriptors that permit its classification, using artificial neural networks
An?lises p?s-despacho de sinais oriundos de registradores de perturba??es fornecem muitas vezes informa??es importantes para identifica??o e classifica??o de dist?rbios nos sistemas, visando a uma gest?o mais eficiente do fornecimento de energia el?trica. Para auxiliar nessa tarefa, faz-se necess?rio recorrer a t?cnicas de processamento de sinais, a fim de automatizar o diagn?stico sobre os tipos de dist?rbio presentes nos sinais registrados. A transformada wavelet constitui-se em uma ferramenta matem?tica bastante eficaz na an?lise de sinais de tens?o ou corrente, obtidos imediatamente ap?s a ocorr?ncia de dist?rbios na rede. Este trabalho apresenta uma metodologia baseada na transformada wavelet discreta e na compara??o de curvas de distribui??o da energia de sinais, com e sem dist?rbio, para diferentes n?veis de resolu??o de sua decomposi??o, com o objetivo de obter descritores que permitam a sua classifica??o atrav?s do uso de redes neurais artificiais

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Filho, Odilon Delmont. "Um algoritmo para detecção, localização e classificação de distúrbios na qualidade da energia elétrica utilizando a transformada wavelet". Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/18/18154/tde-15062007-074110/.

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A Qualidade da energia elétrica é caracterizada pela disponibilidade da energia através de uma forma de onda senoidal pura, sem alterações na amplitude e freqüência. No entanto situações transitórias em sistemas de potência são comuns e estas podem provocar inúmeras interferências indesejáveis. Neste contexto, este trabalho tem como objetivo desenvolver um algoritmo para detectar, localizar no tempo e classificar diversos distúrbios que ocorrem no sistema elétrico através da aplicação da transformada wavelet (TW). Foi realizado um estudo teórico desde a origem até os recentes avanços sobre a TW. Para a detecção e localização no tempo foi utilizada apenas a TW. Com relação à classificação foram comparadas três ferramentas matemáticas: TW, TRF (Transformada Rápida de Fourier) e RNA (Redes Neurais Artificiais). Através do software ATP (Alternative Transients Program) foi modelado um sistema de distribuição, cujas características seguem um sistema real. Todos os distúrbios de tensão gerados e analisados puderam ser detectados e localizados no tempo através da técnica de análise multiresolução. Em relação à classificação, foi realizada uma comparação entre a TW, a TRF e RNA com resultados satisfatórios, destacando dentre elas a TRF e a RNA. Pode-se concluir que os resultados obtidos através do algoritmo mostraram-se eficientes tanto no aspecto da detecção, localização e classificação, assim como na estimação da amplitude do distúrbio e da duração do distúrbio.
A perfect power supply would be one that is always available, maintaining the supply voltage and frequency within certain limits, and supplying pure noise free sinusoidal waveform. Nevertheless, transient events are usual in power systems, resulting in several interferences. The purpose of this study is for detecting, locating in time and to classifying with wavelet transform (WT) several disturbances that occur on power systems. A WT theoretical revision, referring to the first mention in wavelet up to the recent research advances is presented. Only WT was used in order to detect and locate in time the power system disturbances. For classification, three mathematical tools were compared: WT, FFT (Fast Fourier Transform) and ANN (Artificial Neural Networks). A distribution System, with identical characteristics as the real distribution system, was performed with ATP software (Alternative Transients Program). The results showed that multiresolution analysis technique is able to detect and locate all the generated and analyzed voltage disturbances. For classification the results were similar for the WT, FFT and ANN, however FFT and ANN results presented a better performance. The results conclude that the WT algorithm is efficient at detecting, localizing and classifying power system disturbances, as well as, at estimating the amplitude and duration of the voltage disturbance.

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Filho, Odilon Delmont. "Utilização da transformada Wavelet para caracterização de distúrbios na qualidade da energia elétrica". Universidade de São Paulo, 2003. http://www.teses.usp.br/teses/disponiveis/18/18133/tde-14012005-153030/.

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Este trabalho apresenta um estudo sobre transformada Wavelet aplicada à qualidade da energia elétrica com o intuito de detectar, localizar e classificar eventuais distúrbios que ocorrem no sistema elétrico. Inicialmente é apresentada uma introdução sobre qualidade da energia, mostrando fatos, evoluções e explicando o conceito dos principais fenômenos que interferem na qualidade da energia do sistema elétrico brasileiro, devido, principalmente, à grande demanda de aparelhos eletrônicos produzidos atualmente. Em seguida é mostrada uma revisão dos principais métodos e modelos aplicados atualmente no mundo a respeito do assunto. A transformada Wavelet vem como uma grande ajuda nesta área de análise de sinais, já que é capaz de extrair simultaneamente informações de tempo e freqüência, diferentemente da transformada de Fourier. A simulação dos diversos distúrbios ocorridos no sistema foi realizada através do software ATP (Alternative Transients Program), cujas características seguem corretamente um sistema de distribuição real da concessionária CPFL. Os distúrbios de tensão gerados e analisados foram detectados e localizados através da técnica de Análise Multiresolução e, posteriormente, classificados, utilizando para isto o método da Curva de Desvio Padrão
This dissertation presents a study of Wavelet transform applied to power quality in order to detect, locate and classify disturbances that may occur in the power system. Initially an introduction of power quality is presented, showing facts, evolutions and explaining the concept of the main phenomena that interfere the on power quality of the brazilian power system, due to, mainly, a great demand for electronic devices produced nowadays. A revision of the main methods and models currently applied in the world regarding this subject is also show. The Wavelet transform comes as a great support in the area of signal assessment, as it can extract information about time and frequency simultaneously, differently from the Fourier transform. The simulation of the diverse disturbances occurred in the system was accomplished through ATP software (Alternative Transients Program), whose characteristics correctly follow a system of real distribution of CPFL eletric utility. The generated and analyzed voltage disturbances were detected and located by Multiresolution Analysis technique and later classified by the method of the Standard Deviation

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Lin, Zhemin. "The economic losses of power quality disturbance : different perspectives of cost models". Thesis, University of Strathclyde, 2012. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=18700.

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An assessment of the economic impact of power quality disturbances can be performed from the perspective of either electricity customers or power grid owners, depending on the consequences considered by either side. Examples of economic losses due to power quality issues for power grid owners may include compensation and loss of customer royalties, while for electricity customers, the losses may come from damaged products, disrupted industrial process and loss of revenue. In this thesis, the economic losses due to power quality issues are mainly discussed from the customer-oriented perspective. To assess the customer-oriented economic losses of a power system, a cost model is required to describe the characteristics of economic losses in mathematical terms. Some cost models for power quality disturbances are already in existence. Many of these models represent economic losses due to a single factor. However, in this thesis, the following two additional points are included: (a) Powe r quality disturbances always have a short term economic impact on customers, which is not covered in most cost models; (b) Economic losses due to power quality disturbances are actually determined by multiple factors rather than a single factor. The cost models developed in this thesis take the effects of multiple factors into account. This thesis has developed a set of new cost models to evaluate the multiple-factor-dependent potential economic losses due to power quality disturbances. These proposed cost models are specifically designed to calculate short term economic losses while considering customer and time varying impact factors. A time varying coefficient to quantify the effects of time of occurrence for different types of power quality disturbance is also proposed. With the use of the time varying coefficient, the differences in economic losses at different times of occurrence can be accurately represented. In this thesis, all of the proposed cost models are demonstrated individually in different power quality disturbance scenarios and a simple distribution system is used to illustrate the applications of these proposed cost models in a system. The results show the valid applications as well as the advantages of the proposed short term cost models.

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Andrade, Luciano Carli Moreira de. "Transformada Wavelet e técnicas de inteligência computacional aplicadas à identificação, compressão e armazenamento de sinais no contexto de qualidade da energia elétrica". Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/18/18154/tde-09082017-081609/.

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A presença de distúrbios na energia elétrica fornecida aos consumidores pode causar a diminuição no tempo de vida útil dos equipamentos, mal funcionamento ou até mesmo sua perda. Desse modo, ferramentas capazes de realizar a detecção, localização, classificação, compressão e o armazenamento de sinais de forma automática e organizada são essenciais para garantir um processo de monitoramento adequado ao sistema elétrico de potência como um todo. Dentre as ferramentas comumente aplicadas às tarefas supramencionadas, pode-se destacar a Transformada Wavelet (TW) e as Redes Neurais Artificiais (RNAs). Contudo, ainda não foi estabelecida uma metodologia para obtenção e validação da TW e seu nível de decomposição, bem como da arquitetura e da topologia de RNAs mais apropriadas às tarefas supracitadas. O principal fato que levou a esta constatação deve-se à análise da literatura correlata, onde é possível notar o uso de distintas TW e RNAs. Neste contexto, a primeira contribuição desta pesquisa foi o projeto e desenvolvimento de um método eficiente de segmentação de sinais com distúrbios associados à Qualidade da Energia Elétrica (QEE). O método desenvolvido se beneficia das propriedades da TW de identificação temporal de descontinuidades em sinais. A segunda contribuição é o desenvolvimento de um algoritmo automático que, por meio do método de segmentação desenvolvido e de classificação por RNAs, indique as melhores ferramentas (Wavelets e RNAs) para as tarefas de segmentação, extração de características e classificação de distúrbios de QEE. Esse algoritmo foi desenvolvido com base nos recursos dos Algoritmos Evolutivos (AEs) e adotou RNAs do tipo Perceptron Multicamadas, pois, esta arquitetura pode ser considerada consagrada no que se refere à classificação de padrões. Por fim, a terceira contribuição é relativa ao desenvolvimento de um procedimentos baseados em AEs, a fim de se aprimorar métodos de compressão de dados que preservem as informações relevantes nos sinais de QEE. Assim, é importante mencionar que os resultados dessa pesquisa poderão determinar mecanismos automáticos a serem utilizados no processo de registro, tratamento e armazenamento de informações que serão importantes para se manter um banco de dados (histórico) atualizado nas concessionárias de energia, a partir do qual, índices e um melhor mapeamento e entendimento de todos os distúrbios relacionados à QEE poderão ser melhor entendidos e solucionados.
The presence of disturbances in the electrical power supplied to consumers can decrease the lifetime of the equipment, cause malfunction or even their breakdown. Thus, tools able to perform detection, localization, classification, compression and storage of signals automatically and organized manner are essential to ensure adequate monitoring process to electric power systems as a whole. Among the tools commonly applied to the tasks mentioned above, one can highlight the Wavelet Transform (WT) and Artificial Neural Networks (ANN). However, the WT has not been established yet and nor its level of decomposition, as well as the most appropriate ANN architecture and topology to the tasks already mentioned. The main fact that has led to this finding is due to the review of related literature, where it is possible to note the use of distinct WT and ANN. Therefore, the first contribution of this research was the design and development of an efficient method of segmentation of signals associate to Power Quality (PQ) disturbances. The developed method take advantage of WT properties of temporal identification of signal discontinuities. The second contribution is the development of an automatic algorithm that, through the segmentation method developed and classification by ANN, indicates the best tools (Wavelets and ANN) for the tasks of segmentation, extraction of characteristics and classification of QEE disturbances. This algorithm was developed based on the resources of the Evolutionary Algorithms and it adopts Multi-layered Perceptron type ANN, once this architecture can be considered consecrated with regard to the pattenrs classification. Finally, the third contribution is related to the development of EA based procedures in order to improve data compression methods that preserve the relevant information in the PQ signals. Thus, it is important to mention that the results of this research may determine automatic mechanisms to be used in the process of recording, processing and storing information that will be important in order to maintain an up-to-date (historical) database in the utilities, from which , indexes and a better mapping and understanding of all PQ related disturbances can be better understood and solved.

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Сиченко, Віктор Григорович, Виктор Григорьевич Сыченко, Victor G. Sichenko i Viktor H. Sychenko. "Розвиток наукових основ підвищення електромагнітної сумісності підсистем електричної тяги постійного струму залізничного транспорту". Thesis, Видавництво Дніпропетровського національного університету залізничного транспорту імені академіка В. Лазаряна, 2011. http://eadnurt.diit.edu.ua/jspui/handle/123456789/816.

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Сиченко, В. Г. Розвиток наукових основ підвищення електромагнітної сумісності підсистем електричної тяги постійного струму залізничного транспорту : авт. дис. д-ра т. н.: 05.22.09 / В. Г. Сиченко ; Дніпропетр. нац. ун-т залізн. трансп. ім. акад. В. Лазаряна. - Д., 2011.
UA: АНОТАЦІЯ Дисертаційну роботу присвячено вирішенню науково-технічної проблеми – підвищенню електромагнітної сумісності підсистем електричної тяги постійного струму в умовах впровадження швидкісного руху, що покращить техніко- економічні показники перевізного процесу, його енергоефективність та безпеку. В результаті еспериментальних досліджень розроблено математичні моделі завад, що дозволить точніше проектувати та моделювати пристрої енергетичних каналів тягового електропостачання постійного струму. Отримала подальший розвиток методологія активної фільтрації, що призводить до підвищення електромагнітної сумісності підсистем електричної тяги постійного струму як з суміжними пристроями залізничної інфраструктури, так і з лініями зовнішнього електропостачання при зменшенні габаритних розмірів та встановленої потужності застосовуваного обладнання. Очікуваний економічний ефект від впровадження запропонованих рішень перевищує 4 млн. грн. Отримані наукові результати дозволяють надати рекомендації щодо проектування нових енергоефективних електромагнітносумісних перетворювачів комбінованої ідеології для модернізації тягових підстанцій та силових активних фільтрів на підстанціях з застарілим обладнанням. RU: АННОТАЦИЯ Диссертационная работа посвящена решению актуальной научно- технической проблемы - повышению электромагнитной совместимости подсистем электрической тяги постоянного тока в условиях внедрения скоростного движения, которое улучшит технико-экономические показатели перевозочного процесса, его энергоэффективность и безопасность. Рассматривая ЭМС, как один из показателей качества функционирования системы тягового электроснабжения, необходимо охватывать весь комплекс технических средств, которые задействованы в процессе передачи и потребления электроэнергии и учитывать, что система тягового электроснабжения представляет собой сложную электродинамическую распределенную систему, которая характеризуется стохастическим характером изменения параметров функционирования. Эти параметры изменяются в пространстве, плоскости и времени, изменяя электромагнитную обстановку в комплексе система тягового электроснабжения-смежные устройства (линии внешнего энергоснабжения, автоблокировки и продольного электроснабжения, линии связи, информационные каналы передачи данных, каналы телеуправления и телесигнализации, рельсовые цепи). Основой этого влияния является распространение кондуктивних помех через разнообразные гальванические связи. В результате еспериментальних исследований установлено, что питающие напряжения 110 кВ и 35 кВ превышают предельно допустимые значения. Уровень напряжения на шинах 10 кВ, от которых питаются тяговые трансформаторы, также превышает номинальное значение, но на уровне, в основном, нормально допустимого значения отклонения. Несимметрия напряжения, как на шинах 110 кВ, так и на шинах 35-10 кВ, находится в допустимых пределах. Качество электроэнергии за показателем искажения синусоидальности напряжения на шинах 10 кВ тяговых подстанций с 6-пульсовыми выпрямителями не отвечает требованиям стандарта. Коефициент несимметрии напряжения по обратной последовательности в линиях автоблокировки (АБ) значительно более высокий (более чем в 3 раза) в сравнении с линиями продольного электроснабжения (ПЭ), что побуждает ставить вопрос необходимости симетрирования напряжений в линиях АБ. Коэффициент искажения синусоидальности напряжения в линии АБ на тяговой подстанции с 12- пульсовой схемой выпрямления значительно превышает предельно допустимое значение и почти вдвое выше за KU в линии ПЭ. При этом KU и на тяговой подстанции с 6-пульсовой схемой выпрямления выше по сравнению с линией ПЭ. То есть, существующая идеология питания линии АБ с применением двойной трансформации, выполняя задание ограничения токов короткого замыкания и гальванической развязки, в сущности резко ухудшает качество электрической энергии, которая формирует условия ухудшения электромагнитной совместимости. Исследованиями также установлено, что изменение нагрузки тяговой подстанции практически не осуществляет влияния на показатели качества электрической энергии системы внешнего электроснабжения. Режимы напряжения как в системе тягового электроснабжения, так и в системе внешнего электроснабжения определяются случайными факторами и имеют слабую статистическую связь между собой. Разработанные математические модели помех, позволят точнее проектировать и моделировать устройства энергетических каналов тягового электроснабжения постоянного тока. Отмечается, что энергетические каналы (ЭК) тягового электроснабжения должны обеспечивать надежность и бесперебойность питания, стойкость к непредсказуемым влияниям и высокую энергоэффективность. На современном этапе, кроме указанного, они должны быть электромагнитносовместимыми с окружающей средой на всех уровнях передачи, превращения и потребления электрической энергии. Указанные процессы обеспечиваются рядом разнообразных устройств, образовывающих, собственно, ЭК: линии электропередачи, трансформаторы, преобразователи, инфраструктура тяговой сети и потребители электрической энергии. Рассматриваются направления усовершенствования ЭК и возможные варианты их схемной реализации. Перспективными являются структуры децентрализованого питания и разработка систем питания с промежуточным звеном повышенной частоты. Основными устройствами энергетических каналов системы тягового электроснабжения являются тяговые преобразователи. Современный выпрямитель является сложным, многофункциональным устройством и выполняется на принципах комбинированной идеологии с расширенными функциями, в том числе осуществление функции активной фильтрации. В работе получила дальнейшее развитие методология активной фильтрации, которая приводит к повышению электромагнитной совместимости подсистем электрической тяги постоянного тока как со смежными устройствами железнодорожной инфраструктуры, так и с линиями внешнего электроснабжения при уменьшении габаритных размеров и установленной мощности применяемого оборудования. Ожидаемый экономический эффект от внедрения предложенных решений превышает 4 млн. грн. Полученные научные результаты позволяют предоставить рекомендации для проектирования новых энергоэффективных электромагнитносовместимых преобразователей комбинированной идеологии для модернизации тяговых подстанций и силовых активных фильтров на подстанциях с устаревшим оборудованием. EN: THE SUMMARY Thesis is dedicated to solve scientific and technical problems - improve electromagnetic compatibility of alternative current electric traction subsystems in a highspeed lines that will make possible to improve technical and economic characteristics of transportation process, its efficiency and security. On the basis of experimental studies the author developed mathematical models of disturbances, that will make possible to design and simulate devices of power traction DC supply systems in a more accurate way. The author developed the methodology of active filter, that leads to improve the electromagnetic compatibility of electric DC traction subsystems with adjoining devices of railway infrastructure and with external power supply systems reducing the overall dimensions and installed capacity of the applicable equipment. Expected economic effect of proposed approach exceeded 4 million grn. Proposed results can provide propositions about designing of new energy converters on the basis of combined ideology for modernization of traction substations and power active filters of substations with aging equipment.

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Looja, Tuladhar R. "Control of Custom Power System using Active Disturbance Rejection Control". Cleveland State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=csu1438913443.

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Manmek, Thip Electrical Engineering &amp Telecommunications Faculty of Engineering UNSW. "Real-time power system disturbance identification and its mitigation using an enhanced least squares algorithm". Awarded by:University of New South Wales. Electrical Engineering and Telecommunications, 2006. http://handle.unsw.edu.au/1959.4/26233.

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This thesis proposes, analyses and implements a fast and accurate real-time power system disturbances identification method based on an enhanced linear least squares algorithm for mitigation and monitoring of various power quality problems such as current harmonics, grid unbalances and voltage dips. The enhanced algorithm imposes less real-time computational burden on processing the system and is thus called ???efficient least squares algorithm???. The proposed efficient least squares algorithm does not require matrix inversion operation and contains only real numbers. The number of required real-time matrix multiplications is also reduced in the proposed method by pre-performing some of the matrix multiplications to form a constant matrix. The proposed efficient least squares algorithm extracts instantaneous sine and cosine terms of the fundamental and harmonic components by simply multiplying a set of sampled input data by the pre-calculated constant matrix. A power signal processing system based on the proposed efficient least squares algorithm is presented in this thesis. This power signal processing system derives various power system quantities that are used for real-time monitoring and disturbance mitigation. These power system quantities include constituent components, symmetrical components and various power measurements. The properties of the proposed power signal processing system was studied using modelling and practical implementation in a digital signal processor. These studies demonstrated that the proposed method is capable of extracting time varying power system quantities quickly and accurately. The dynamic response time of the proposed method was less than half that of a fundamental cycle. Moreover, the proposed method showed less sensitivity to noise pollution and small variations in fundamental frequency. The performance of the proposed power signal processing system was compared to that of the popular DFT/FFT methods using computer simulations. The simulation results confirmed the superior performance of the proposed method under both transient and steady-state conditions. In order to investigate the practicability of the method, the proposed power signal processing system was applied to two real-life disturbance mitigation applications namely, an active power filter (APF) and a distribution synchronous static compensator (D-STATCOM). The validity and performance of the proposed signal processing system in both disturbance mitigations applications were investigated by simulation and experimental studies. The extensive modelling and experimental studies confirmed that the proposed signal processing system can be used for practical real-time applications which require fast disturbance identification such as mitigation control and power quality monitoring of power systems

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Aguiar, Eduardo Pestana de. "Sistema de inferência Fuzzy para classificação de distúrbios em sinais elétricos". Universidade Federal de Juiz de Fora (UFJF), 2011. https://repositorio.ufjf.br/jspui/handle/ufjf/4149.

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A presente dissertação tem como objetivo discutir o uso de técnicas de otimização baseadas no gradiente conjugado e de informações de segunda ordem para o treinamento de sistemas de inferência fuzzy singleton e non-singleton. Além disso, as soluções computacionais derivadas são aplicadas aos problemas de classificação de distúrbios múltiplos e isolados em sinais elétricos. Os resultados computacionais, obtidos a partir de dados sintéticos de distúrbios em sinais de tensão, indicam que os sistemas de inferência fuzzy singleton e non-singleton treinados pelos algoritmos de otimização considerados apresentam maior velocidade de convergência e melhores taxas de classificação quando comparados com aqueles treinados pelo algoritmo de otimização baseada em informações de primeira ordem e é bastante competitivo em relação à rede neural artificial perceptron multicamadas - multilayer perceptron (MLP) e ao classificador de Bayes.
This master dissertation aims to discuss the use of optimization techniques based on the conjugated gradient and on second order information for the training of singleton or non-singleton fuzzy inference systems. In addition, the computacional solutions obtained are applied to isolated a multiple disturbances classification problems in electric signals. Computational results obtained from synthetic data from disturbances in electric signals indicate that singleton or non-singleton fuzzy inference systems trained by the considered optimization algorithms present greater convergence speed and better classification rates when compared to those data trained by an optimization algorithm based on first order information and is quite competitive with multilayer perceptron neural network and Bayesian classifier.

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Oliveira, José Mário Menescal de. "Efeitos da operação do gerador de indução no comportamento do gerador síncrono operando em um sistema isolado alimentando cargas não lineares". Universidade Federal de Goiás, 2018. http://repositorio.bc.ufg.br/tede/handle/tede/8878.

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This thesis demonstrates the effects of harmonic pollution in a salient pole synchronous generator and an induction generator operating in parallel on an isolated system, supplying a non-linear load. The main contributions of this research-study consist of identifying and quantifying the oscillations that non-linear load cause on the electric variables of synchronous and induction generators, such as, the electromagnetic conjugate that presents oscillations of sixth harmonic due to the distorted currents.
Este trabalho mostra os efeitos da poluição harmônica em um gerador síncrono de polos salientes e um gerador de indução operando em paralelo em um sistema isolado suprindo carga não linear. As principais contribuições deste trabalho consistem em identificar e quantificar as oscilações que a carga não linear utilizada provoca nas variáveis elétricas dos geradores síncronos e dos geradores de indução, tal como, o conjugado eletromagnético que apresenta oscilações de sexto harmônico devido as correntes distorcidas.

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Lima, Marcelo Antonio Alves. "Uma nova metodologia para análise da qualidade da energia elétrica sob condições de ocorrência de múltiplos distúrbios". Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/18/18154/tde-14112013-102931/.

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Um Sistema Elétrico de Potência (SEP) está susceptível à presença de diversas fontes de distúrbios que prejudicam a Qualidade da Energia Elétrica (QEE). Desta forma, as suas tensões e/ou correntes podem conter m´múltiplos distúrbios com ocorrência simultânea. Este trabalho apresenta uma metodologia para decomposição do sinal medido em componentes que estimem as formas de onda dos distúrbios individuais quando da ocorrência de m´múltiplos distúrbios, com o posterior reconhecimento de cada um deles. A Análise de Componentes Independentes (ICA) é utilizada como principal ferramenta na etapa de decomposição dos distúrbios. A ICA é originalmente uma t´técnica aplicada em análise multivariada de dados, o que significa que ela necessita de medições realizadas por múltiplos sensores dispostos em diferentes posições de um sistema. No entanto, este trabalho propõe a sua aplicação tendo disponível apenas um sinal medido. Para tanto, são propostos dois métodos para produzir a diversidade necessária para a t´técnica funcionar adequadamente. É demonstrado que ambos os métodos equivalem a um banco de filtros lineares adaptativos capaz de realizar a separação não-supervisionada de múltiplos distúrbios independentes e que sejam espectralmente disjuntos. Por fim, é proposto um sistema de classificação que utiliza Redes Neurais Artificiais (RNAs) para identificar os distúrbios decompostos pela etapa anterior. A metodologia completa é avaliada por meio de testes utilizando dados sintéticos e reais, alcançando resultados altamente satisfatórios para decomposição de sinais contendo múltiplos distúrbios e taxas de acerto globais dos classificadores superiores a 97%
The power system is susceptible to the presence of several sources of disturbances that harm the power quality. In this sense, its voltages and/or currents may contain multiple disturbances with simultaneous occurrence. This work presents a methodology that decomposes the measured signal in components which estimate the waveforms of the individual disturbances followed by their recognition when a multiple disturbance situation occurs. The Independent Component Analysis (ICA) is the main tool in the disturbance decomposition stage. The ICA is originally a technique applied in multivariate data analysis, which means that it requires measurements from multiple sensors allocated in different positions of the system. However, this work proposes its application for a single measured signal available. For this, two methods were developed in order to provide the required diversity to the ICA technique. It is demonstrated that both methods are equivalent to an adaptive linear filter bank capable to perform an unsupervised separation of multiple independent disturbances, if they are spectrally disjoint. A classification system based on artificial neural networks is proposed to identify the disturbances decomposed by the previous stage. The complete system is tested using synthetic and actual data, presenting highly satisfactory results for the decomposition of signals containing multiple disturbances, and precision for the classification task above 97%

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BHAGAT, NEERAJ KUMAR. "DETECTIONOF POWER QUALITY DISTURBANCES". Thesis, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18945.

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Automated and quick fault detection has received quite a lot of importance and some comprehensive studies have been done because of interlinking of varieties of disturbances in the power system. It takes ideal sinusoidal signal as training data aiming to recognize the other different types of faults, it generally involves two problems, i.e., selection and matching between the training and the testing data. Many studies have either studied the two independently or only focusing on selection part with less focus on the matching part of the algorithm. In this paper we propose the algorithm of transfer subspace learning to address the problem of matching which is of considerable importance as how good be the selection if the matching to particular fault is not accurate it will not give desired results. In the experiment we calculate the projection matrix and maximum mean discrepancy matrix to identify the type of fault which has occurred. The experiment so performed on the industrial data verifies our experiment to be workable in the real world situations

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Kumar, Raj. "Assessment and mitigation of power quality disturbances". Thesis, 2016. http://localhost:8080/xmlui/handle/12345678/7153.

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Hoang, TA. "Wavelet-based techniques for classification of power quality disturbances". Thesis, 2003. https://eprints.utas.edu.au/20549/1/whole_HoangTuanAnh2003_thesis.pdf.

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The quality of power supply has become an important issue for electricity utilities and their customers. In recent years there has been a rising incidence of damage attributed to the power quality supplied to the customers of electric utilities. Meanwhile, there has been a rapid increase in the already widespread use of electronic equipment and modem power electronic devices. These trends have both decreased the quality of power on the electric grid and increased the equipment's sensitivity to power quality disturbances. In order to improve the quality of the power supply, identifying the type and source of troublesome disturbances is an essential task. Existing automatic disturbance classification methods have replaced the traditional visual inspection of the disturbance waveforms. However, they are not reliable because those methods rely on the classification capability of large neural networks operating on inputs derived by simply pre-processing the disturbance signals with discrete wavelet transforms [134,135,136,137,138]. Long and redundant feature vectors both take a long time to train the network and result in a reduced classification rate. In this thesis, we aim to develop an efficiency method that automatically classifies power quality disturbances by using wavelet transform techniques to generate short and nonredundant feature vector. Because of the wide range of power quality disturbances and their characteristic waveforms, ranging from very simple stationary and deterministic harmonics to highly transient and stochastic waveforms, different and appropriate analysis techmques are needed to achieve the overall classification objective. It is well known that the traditional Fourier analysis is ideal for analysing steady state signal. Although it is very powerful, Fourier analysis does not have the temporal resolution needed to cope with sharp changes and discontinuities in signals. Recent years have witnessed a proliferation in the applications of wavelet transforms to signal analysis in a wide variety of fields, from geo-physics to telecommunications to bio-medical engineering. This has occurred because wavelet analysis provides dual localisations in both the time and the frequency domains. Moreover, wavelet analysis allows the flexibility of choosing a wavelet that suits a particular application. Especially by using the simple and flexible lifting scheme, we can construct a time-variant or space-variant wavelet - known as second-generation wavelet. The second-generation wavelet analysis makes optimal use of the correlation between neighbouring signal samples and between neighbouring frequency components to construct 'local' wavelets, which adapt to the local characteristics of the signal. Common types of wavelet schemes are the orthonormal or biorthonormal wavelet transforms that are typically used in compression and coding applications. This is due to the fact that those schemes can be implemented with fast algorithms and they are non-redundant representations of a signal. Unfortunately, they suffer the limitation of not being translation invariant; a totally different set of transformed coefficients is obtained when the same signal is shifted. This is the major concern in pattern recognition applications. There exist a number of wavelet schemes that have the shift invariance property in their multiresolution representations. In this thesis, the local maxima and the matching pursuit techniques are presented as the two most appropriate techniques for power quality solutions. This is because the two techniques can efficiently decompose a signal and have the ability to precisely measure power quality disturbance characteristics so that they represent the disturbances by a compact, time-invariance feature vector. The final task of classification is the selection of an appropriate classifier for use with the feature vector. There are two main approaches of pattern recognition: one is parametric and the other is non-parametric [129]. Parametric approaches can be either deterministic or statistical. The statistical parametric approach requires a good assumption about the statistical distribution of the data. On the other hand, the nonparametric approach, known as the neural network approach, does not require any statistical assumption about the data. In our statistical approach, we use a two-layer network structure with locally tuned nodes in the hidden layer, known as Radial Basis Function (RBF) network [106,120,121]. The network has only a local learning capability and a limited learning inference from the training data, but trains quickly as the training of the two layers is decoupled. In an RBF network, the crucial concern is the selection of cluster centres and their widths. However, current techniques give suboptimum positions of cluster centres and their widths, thus limiting the classification rate. To improve the p~rformance of an RBF network, we propose to modify the structure of the RBF network by "' introducing the weight matrix to the input layer (in contrast to the direct connection of the input to the hidden layer of a conventional RBF) so that the training space in the RBF network is adaptively separated by the resultant decision boundaries and class regions. During training iterations, cluster centres, their widths and the input layer weights are optimally determined together and concurrently adjusted to maximise the discriminant between classes, thus minimising the classification error. In this way the network has the ability to deal with complicated problems, which have a high degree of interference in the training data, and achieves a higher classification rate over the current classifiers using RBF. For the classification of different types of disturbances that may be present on a power supply, in this thesis we show that our automatic classification techniques achieve superior recognition rates over current techniques. This improvement is done in two steps. The first improvement is the extraction of disturbance features using appropriate signal processing tools from which we obtain an efficiency and translation invariant feature vector. The second improvement is the designing of an appropriate classifier which maximises the inter-class discriminant function.

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34

Choudhury, Debasis. "Characterization of Power Quality Disturbances using Signal Processing and Soft Computing Techniques". Thesis, 2013. http://ethesis.nitrkl.ac.in/4745/1/210EE2101.pdf.

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The power quality of the electric power has become an important issue for the electric utilities and their customers. In order to improve the quality of power, electric utilities continuously monitor power delivered at customer sites. Thus automatic classification of distribution line disturbances is highly desirable. The detection and classification of the power quality (PQ) disturbances in power systems are important tasks in monitoring and protection of power system network. Most of the disturbances are non-stationary and transitory in nature hence it requires advanced tools and techniques for the analysis of PQ disturbances. In this work a hybrid technique is used for characterizing PQ disturbances using wavelet transform and fuzzy logic. A no of PQ events are generated and decomposed using wavelet decomposition algorithm of wavelet transform for accurate detection of disturbances. It is also observed that when the PQ disturbances are contaminated with noise the detection becomes difficult and the feature vectors to be extracted will contain a high percentage of noise which may degrade the classification accuracy. Hence a Wavelet based de-noising technique is proposed in this work before feature extraction process. Two very distinct features common to all PQ disturbances like Energy and Total Harmonic Distortion (THD) are extracted using discrete wavelet transform and is fed as inputs to the fuzzy expert system for accurate detection and classification of various PQ disturbances. The fuzzy expert system not only classifies the PQ disturbances but also indicates whether the disturbance is pure or contains harmonics. A neural network based Power Quality Disturbance (PQD) detection system is also modeled implementing Multilayer Feedforward Neural Network (MFNN).

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35

Lu, Chen-Wen, i 呂振文. "A Study of Power Quality Disturbances-Monitoring Instrumentation and Measurement Technology". Thesis, 2003. http://ndltd.ncl.edu.tw/handle/83298149758117506585.

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博士
國立成功大學
電機工程學系碩博士班
92
Electrical power signal measurement and analysis are two important aspects in power quality. In this dissertation, an application of virtual instrument approach for probing the data of power quality disturbances is first proposed. In the proposed instrumentation system, a data acquisition interface is implemented along with digital algorithms developed by LabVIEW tools. The proposed system can be applied to investigate different disturbances that include voltage harmonics, voltage flickers and three-phase voltage unbalances. Through a certain period of disturbance monitoring and recording, the trend-of-variation and the related statistical information of disturbances would be examined and analyzed. Results of simulated and actual data help confirm the feasibility of the method. As for measurement techniques, a continuous wavelet transform-based approach is proposed to assist the measurement of voltage flickering. A wavelet-direct demodulation (WDD) method is derived that is also compared with the fast Fourier transform covering frequency-domain direct demodulation (FDD) and indirect demodulation (IDD) methods, where the computation performances of each method were presently assessed based on the flicker-frequency response and system frequency deviation. Besides, to validate the practicality of the proposed method, the utility data measured near the arc furnace were also evaluated to support the method. Furthermore, by embodying the B-spline wavelet function, this dissertation also investigate the detection of the notch and spike signals. In addition to localizing the start and end point of the notch and spike signals, the proposed method also grasped the depth, width and area parameters of the encountered signals. Test results further solidify the proposed method in good agreement. Finally, as for the analysis of measured data, the estimation of the stochastic flicker characteristics for an electric arc furnace over a complete heat is examined thoroughly in this dissertation. These characteristics include stationarity, normality and correlation. Meanwhile, several fundamental statistical features were assessed at a different sampling periods. Test results for a typical AC arc furnace indicate that (1) most flicker characteristics are stationary or weakly stationary during different periods of a heat cycle, but most of their probability density functions are not normally distributed, (2) the voltage and current fluctuations in the same phase are highly correlated, (3) the flicker converges at a value with a ±5% deviations from the value based on a basic sampling period, if the sampling period is decreased below 12 seconds.

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36

Behera, Satyajit, i Sudhanshu Sekhar Send. "Characterization of Various Power Quality Disturbances Based on Signal Processing and Artificial Intelligence Scheme". Thesis, 2015. http://ethesis.nitrkl.ac.in/7638/1/200.pdf.

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These days the electrical power quality has become a vital issue for the utilities and the consumers. Use of non-linear and sensitive loads add gradual deterioration of power quality. To improve power quality, automatic classification of power quality disturbances(PQDs) is much essential, which are also important for protection of transmission system network. Disturbances are mostly transient and temporary, thereby necessitate suitable method to analyze PQDs. In this paper a combined technique in the form of wavelet transform(WT) in association with fuzzy expert system is used for characterizing PQ disturbances. A no. of PQ signals are developed and decomposed using WT method for nearly exact detection of disturbances. Energy and Total Harmonic Distortion (THD) of all PQ disturbances are extracted through discrete wavelet transform (DWT) and are used in the fuzzy expert system to detect and classify different disturbances accurately. The fuzzy system used classifies the disturbances and confirms the presence of harmonics.

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37

Ianniello, Giacomo. "Power quality measurement methods aimed at disturbances detection and instrumentation susceptibility assessment". Tesi di dottorato, 2013. http://www.fedoa.unina.it/9380/1/Ianniello_Giacomo_25.pdf.

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In this work several important aspects of power quality are discussed, and measurement methods aimed at disturbances detection are proposed. A first aspect, investigated throughout laboratory tests, concerns the effects of poor power quality on measurement instrumentation. Susceptibility studies have been carried out considering the disturbances referred into standard CEI EN 50160. Controlled power quality disturbances have been injected in instrumentation and both reliability and accuracy issues have been checked in the test. A second aspect taken into account is that related to the instrumentation needed for the analysis of the power quality. An original contribution that consists in the design and implementation of a distributed low cost Arm based network analyzer has been presented. The network analyzer is able to measure the power quality characteristics by each appliance. Furthermore, the proposed distributed analyzer includes a web server that allows to collect the statistics of each meter. A client can connect to the server to analyze meter group measurement results. Several details related to suitable strategies to increase measurement resolution have also been considered. In particular a pre processing scheme to enhance resolution of data acquisition systems mainly in presence of band bass input signals has been investigated. This solution permits to acquire a seamless data stream with improved vertical resolution. An important feature is that this solution shows efficient in terms of hardware requirements and processing time. Finally, since in power quality assessment harmonic analysis plays a key role, modeling and analysis of the functioning of power systems in time varying conditions has been studied with the purpose of understanding how harmonic analysis could be used effectively in these scenarios. In particular the power systems have been described trough simplified approaches by recognizing load conditions that can be considered stationary within limited time intervals. A data segmentation approach to distinguish the time intervals in which stationary load conditions can be recognized has been proposed and assessed.

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38

Kuan, Yu-ching, i 關羽慶. "Classification of Power Quality Disturbances Using Dynamic Time Warping and Particle Swarm Optimization". Thesis, 2005. http://ndltd.ncl.edu.tw/handle/86315406144823615319.

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碩士
國立雲林科技大學
電機工程系碩士班
93
Power system confronts power quality disturbances that are hard to avoid. Before assembling a protective device or finding a way of improvement, first the disturbance types need to be identified. Then, we can know what disturbances belong and why power system hitches. And we can get better power quality. This paper presents two approaches based on dynamic time warping and particle swarm optimization for classification of power quality disturbances. The first approach is based on heuristic rules and dynamic time warping for classification of power quality disturbances. In the classification process, the Walsh transform and fast Fourier transform are first used to get feature parameters for the input signals. Then, the vector quantization is used to speed up the dynamic time warping operation. Moreover, in order to reduce the dynamic time warping computational cost and increase the classification accuracy, the heuristic rules are introduced. Finally the effectiveness of the proposed approach is demonstrated by disturbance classification. The second approach is based on particle swarm optimization for classification of power quality disturbances. The test signal and reference pattern are matched with particle swarm optimization. It is changed that the characteristics of particle swarm optimization for get better results. It is concluded from the results that the method is very effective for classification power quality disturbances.

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39

Chen, Cheng-I., i 陳正一. "Evaluation and Improvement of Signal Processing Techniques for Measurement of Power Quality Disturbances". Thesis, 2009. http://ndltd.ncl.edu.tw/handle/74719997168502201637.

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博士
國立中正大學
電機工程所
97
With the widespread use of nonlinear loads in the power system, the power quality disturbances are increasingly present. These disturbances may introduce operational problems of power system equipments. Therefore, improving the power quality has become a great concern for both utilities and their customers. The frequency-domain methods have been widely used for the signal processing because of its computational efficiency. In addition, most power meters adopt the FFT-based algorithms to analyze the power signals. However, the FFT-based algorithms are less accurate if the system frequency varies and the frequency resolution decreases. The analytic results will show errors caused by the leakage and picket-fence effects. Therefore, many conventional analysis algorithms are necessary to improve for the detection of time-varying signals. For accurately and efficiently monitoring the power quality disturbances, such as harmonics, interharmonics, voltage flickers, sags, swells, and interruptions, this dissertation proposes several remedial strategies to improve the drawbacks of the commonly used signal processing algorithms in the literature. Besides, this dissertation applies LabVIEW and the dedicated hardware to design a simple virtual instrument and an educational platform for conveniently measuring power quality signals and the engineering education. The performance of improved algorithms is validated by testing the synthesized and actual signals.

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40

Rentala, D. B., i M. Das. "A Novel method to Detect and Evaluate Power Quality disturbances using Hilbert Phase Shifting and CORDIC Algorithm". Thesis, 2014. http://ethesis.nitrkl.ac.in/5520/1/110EE0645-11.pdf.

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Power Quality (PQ) is an umbrella term that encompasses all the aspects connected with amplitude, phase and frequency of the voltage and current waveforms existing in a power circuit. Adverse Power quality environments refer to the transient conditions developing in the power circuit and eventually affecting the load and the source. Power Quality Monitoring refers to the task of detecting the disturbances in the system which leads to the deterioration of the Power Quality and the immediate execution of measures to compensate the same. The various disturbances that adversely affect the quality of power include voltage sags, voltage swells, voltage fluctuation, transient oscillations, harmonics and inter- harmonics. The work aims at finding a unified and comprehensive method to detect and evaluate each of the Power Quality disturbances. The detection is facilitated through the Hilbert Phase shifting mechanism and the same detection output is used for the accurate evaluation of the disturbances based on CORDIC Algorithm. The methods developed are hindered by the presence of Noise in the system; hence emphasis has been laid to suppress noise for the efficient working of the algorithm which bases its working on Hilbert’s Phase shifting property. The noise suppression is basically achieved by employing Mathematical Morphological filters and applying them before the signal is subjected to the detection and evaluation algorithm. The evaluation is based purely on the phase determining property of CORDIC (Coordinate rotation digital computer) Algorithm. A suitable model has been determined to efficiently account all the disturbances.

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41

Swain, Sudarshan. "Grid Synchronization Control Schemes for a Three Phase Grid connected Photovoltaic System with Power Quality Disturbances". Thesis, 2019. http://ethesis.nitrkl.ac.in/10052/1/2019_PhD-SSudarshan_514EE1010_Grid.pdf.

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Synchronizing PV system with grid encounters a number of control challenges for maintaining the grid codes. These include various power quality problems such as voltage and current harmonics, voltage sag and swell, and grid frequency fluctuation. In this thesis it is intended to design suitable grid synchronization control scheme for a three-phase single stage grid connected PV system (TPSSGCPVS) considering power quality disturbances. The proposed control schemes are implemented both by simulation in MATLAB/Simulink followed by experimentation on a prototype TPSSGCPVS developed in the laboratory. Firstly, the thesis focuses on the design of a Self-Tuning Filter-Proportional Integral (STF-PI) grid synchronization control scheme for TPSSGCPVS. The Self-Tuning Filter (STF) extracts the fundamental voltage of the distorted Point of Common Coupling (PCC) voltage and load current without any change in phase and amplitude of the fundamental component. To verify the effectiveness of the proposed STF-PI control scheme, a comparative analysis on its performance with that of the Improved Linear Sinusoidal Tracer-PI (ILST-PI) control scheme is pursued. Form the stability analysis, it is observed that the STF-PI control scheme has a wider range of stability region as compared to the ILST-PI control scheme. Simulations are performed by implementing these control schemes on a PV system considering the power quality disturbances. Subsequently, the proposed STF-PI control scheme is implemented in real-time on a prototype TPSSGCPVS developed in the laboratory. From both the simulation and experimental results obtained, it is verified that the proposed STF-PI control scheme provides effective grid synchronization of the PV system. Along with maximum PV power injection into the grid, the proposed STF-PI control scheme provides efficient harmonics compensation under PCC voltage distortion, load current distortion, load fault, PCC voltage sag and swell. From the obtained results, it is observed that using STF-PI control scheme, the grid current is maintained sinusoidal by reducing the harmonics as compared to ILST-PI control scheme. The current is injected into the grid at Unit Power Factor (UPF) by reducing the reactive current component to almost zero. The grid currents are maintained balanced and sinusoidal with reduced distortion despite load fault. The THD of the grid current is reduced from 26.7% to 4 % using the above STF-PI control scheme, satisfying the limits prescribed by the IEEE 519 grid code. The THD of the grid current is reduced to 4 % using STF-PI control scheme even under PCC voltage sag and swell conditions. It is observed that, with a fixed cut-off frequency of STF, the THD of the grid current varies in real time as grid frequency varies. In order to further reduce the THD and reduce the THD variation, Extended Kalman Filtering (EKF) and Iterated EKF (IEKF) algorithms are employed for grid synchronization of a PV system. EKF and IEKF are used to estimate the fundamental sinusoidal component of the PCC voltage. IEKF uses an iterative loop to reduce the mean square error and increases the convergence speed of the grid current. From the simulation results it is observed that the grid current reaches the steady state faster using IEKF-PI control scheme than using EKF-PI and STF-PI control schemes. The THD of the grid current in real-time is reduced to the lowest value of 3.5% using the proposed IEKF-PI control scheme than the corresponding values of 3.6% and 4% respectively yielded in case of EKF-PI and STF-PI control schemes. Even by changing the grid frequency, the grid current is maintained sinusoidal using IEKF-PI control scheme. THD variation is minimized using IEKF and EKF-PI control schemes than STF-PI control scheme. As IEKF uses the Jacobin matrix for linearization, the estimation accuracy is limited to first order approximation of the Taylor series. Unscented transformation is a nonlinear transformation, which propagates the mean and covariance through a nonlinear function. A set of sigma points is chosen to preserve the nonlinear nature of the system. Firstly, an Unscented Kalman (UKF) is proposed to further reduce the THD of the grid current and reduce the THD variation. In UKF, the sigma points are determined by finding the square root of the error covariance, obtained using Cholesky decomposition. In order to apply Cholesky decomposition, the error covariance matrix must be positive semi definite. The loss of the positive definiteness may result in stopping the UKF to run continuously or even cause divergence. To resolve the difficulties encountered in UKF, a Square Root Cubature Kalman Filter (SRCKF)-PI grid synchronization control scheme is proposed. From the obtained results, it is observed that the variation in THD of the grid current is minimized by both UKF-PI and SRCKF-PI control schemes as compared to IEKF-PI control scheme. The THD of the grid current is reduced to a lowest value to 3.2 % using SRCKF-PI control scheme than the corresponding values of 3.3% yielded by UKF-PI control scheme. Form the obtained results with all the proposed control schemes for grid synchronization of the PV system, it is observed that these are able to maintain the grid codes by reducing the THD below 5%. However, the SRCKF algorithm essentially transmits the square root factors of the predictive and posterior error covariance in order to eliminate the square root operation thus providing the best convergence speed which minimizes the settling time. The estimation of the fundamental component of the PCC voltage using the cubature points in SRCKF algorithm provides increased estimation accuracy. As a result, the THD of the grid current is thus minimized to a value of 3.2 % using SRCKF-PI control scheme. It is thus concluded that amongst all the proposed controllers, SRCKF-PI control scheme exhibits the superior grid synchronization control performance with power quality disturbances.

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42

Kuan, Chien-Hsun, i 管建勛. "Feature Selection for Identification and Classification of Power Quality Disturbances Based on Particle Glowworm Swarm Optimization (PGSO) Algorithm". Thesis, 2015. http://ndltd.ncl.edu.tw/handle/38aw24.

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碩士
中原大學
電機工程研究所
103
This study proposes an optimization feature selection scheme, which combines a glowworm swarm optimization (GSO) algorithm with a particle swarm optimization (PSO), namely particle glowworm swarm optimization (PGSO). The proposed PGSO-based scheme optimizes the smoothing parameters of LOOCV of probabilistic neural network (PNN). The least influenced features, rarely degrading the cross-validation accuracy, are removed from the candidate features by using the optimal smoothing parameters to reconstruct the optimal feature vectors set. This paper illustrates time-frequency and time-time relationships of 13 types of power quality disturbance (PQD) by using S-transform (ST) and TT-transform (TT) in the conditions of no noise, SNR=30dB, SNR=25dB, SNR=20dB and SNR=15dB. By observing the ST and TT contours, 6 types of time characteristic curves and 5 types of frequency characteristic curves are depicted. According to the time-frequency and the time-time relationships, 62 candidate features are calculated. The results show that the classification accuracies and runtimes of classifiers by using to the back propagation neural network (BPNN) is superior to that by using the optimal feature vectors set obtained by the proposed PGSO-based scheme, even in the environment with various noise interference.

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43

Ringrose, MJ. "Studies of power quality : disturbance recognition". Thesis, 2003. https://eprints.utas.edu.au/11455/1/Ringrose_whole_thesis.pdf.

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Power quality is becoming increasingly important in power systems. The proliferation of modem power electronic devices has both decreased the quality of power on the electric grid and increased the equipment's sensitivity to power quality disturbances. In order to maintain an acceptable level of power quality in a power system, power quality monitoring devices have been designed. These devices are purchased by power utilities and customers in increasing numbers for the purpose of power quality trend logging and power quality disturbance capturing. Many power quality monitors will provide some information on the type of a power quality disturbance that has been recorded, however a reliable system for automatically classifying the full range of disturbance types is yet to be formalised. Recent advances in signal processing and artificial intelligence have put this goal within reach. A system for the automatic classification of recorded power quality disturbances is developed in this thesis. The system uses a variety of mathematical tools in order to produce an accurate and robust classification method. These tools include Fourier transforms, wavelet transforms, artificial neural networks, and fuzzy logic. Transient disturbances are analysed using the wavelet transform general modulus maxima technique and classified into their respective disturbance classes using a neuro-fuzzy pattern recognition scheme. The remaining disturbance types are analysed using Fourier transforms and classified into their respective disturbance classes using a simple decision making scheme. Also investigated in this thesis is a method for monitoring the harmonic output from nonlinear loads in a power system using a reduced number of harmonic monitoring stations. The method utilises a state estimation scheme with measurements from installed harmonic monitoring stations combined with pseudo-measurements provided by artificial neural networks.

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44

Panda, Swastik Sovan. "Power Quality Disturbance Detection and Classification". Thesis, 2016. http://ethesis.nitrkl.ac.in/8274/1/2016_BT_112EE0247_Power_Quality.pdf.

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Power quality (PQ) monitoring is an essential service that many utilities perform for their industrial and larger commercial customers. Detecting and classifying the different electrical disturbances which can cause PQ problems is a difficult task that requires a high level of engineering knowledge. The vast majority of the disturbances are non-stationary and transitory in nature subsequently it requires advanced instruments and procedures for the examination of PQ disturbances. In this work a hybrid procedure is utilized for describing PQ disturbances utilizing wavelet transform and fuzzy logic. A no of PQ occasions are produced and decomposed utilizing wavelet decomposition algorithm of wavelet transform for exact recognition of disturbances. It is likewise watched that when the PQ disturbances are contaminated with noise the identification gets to be troublesome and the feature vectors to be separated will contain a high amount of noise which may corrupt the characterization precision. Consequently a Wavelet based denoising system is proposed in this work before feature extraction process. Two extremely distinct features basic to all PQ disturbances like Energy and Total Harmonic Distortion (THD) are separated utilizing discrete wavelet transform and is nourished as inputs to the fuzzy expert system for precise recognition and order of different PQ disturbances. The fuzzy expert system classifies the PQ disturbances as well as demonstrates whether the disturbance is unadulterated or contains harmonics. A neural network based Power Quality Disturbance (PQD) recognition framework is additionally displayed executing Multilayer Feedforward Neural Network (MFNN).

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45

Wang, Jen-Shuan, i 王仁舜. "Design the Chip for Classifying Power Quality Disturbance". Thesis, 2004. http://ndltd.ncl.edu.tw/handle/13696311961748313383.

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碩士
淡江大學
電機工程學系
92
Power quality (PQ) is recognized as an essential feature of a successful electric power system mainly due to the rapid increase of loads which generate noise and, at the same time, are sensitive to the noise present in the supply system. Existing techniques for recognizing and identifying power quality disturbance waveforms are primarily based on visual inspection of the waveform. It is the purpose of this study to bring to bear a recent advance method based on the artificial neural network to the problem of automatic power quality disturbance waveform classification. This study proposes a new approach to classify various types of power quality disturbance events based on a hierarchical artificial neural network and presents its implements on a field programmable gate array (FPGA). This proposed hierarchical neural network utilizes self-organizing feature map (SOM) networks and learning vector quantization (LVQ) networks. In the proposed network, the SOM networks provide an approximate method for computing the input vectors in an unsupervised manner with the approximation being specified by the synaptic weight vector of the neurons in the SOM. The computation of the SOM may therefore be viewed as the first stage of the proposed hierarchical network for solving the PQ classification problem. The second stage is provided by the LVQ networks. The LVQ network is based on a supervised learning techniques that uses class information to improve the quality of the classifies from the first stage. The multistages hierarchical classifier attempts to factorize the overall input vector into a number of small groups, each of which requires very little computation. Therefore, by use of the hierarchical classifier, the loss in accuracy can be small. The solution algorithm based on the proposed hierarchical network is then implemented by FPGA. This work presents the actual design of a system on a programmable chip (SOPC), and describes the related synthesis, layout, and verification phases. Finally, the effectiveness of the proposed algorithm and its hardware implementation is verified through various test experiments.

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46

Yao-Hui, Nien, i 粘遙輝. "Power Quality Disturbance Waveform Recognition Using Neural Network". Thesis, 2002. http://ndltd.ncl.edu.tw/handle/74303057801787422778.

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碩士
淡江大學
電機工程學系
90
In recent years, because of rapid development of the high-tech industries, the limits on the specification higher power quality is strict. However, to improve the power quality, the import thing is to collect the information regarding to power quality events for analysis their properties. Therefore, enhancing the power quality analysis technique is one of the most important tasks in the power industry. This thesis utilized the import neural network technique to implement a PQ disturbance classifier. The PQ disturbance classifier is based on the Learning Vector Quantization algorithm implemented by Matlab. The classifier can classify the transient signals into voltage interruption, swell, sag, flicker, and harmonic. Users can characterize those power quality problems by this tool. To verify the performance of the proposed approach, this approach has been tested on many disturbance signals generated by MATLAB/SIMULINK. The accuracy and efficiency of the proposed approach are verified by many cases. The results show the propose approaches can identify the PQ disturbance correctly.

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47

Sarkar, S. "Power quality disturbance detection and classification using signal processing and soft computing techniques". Thesis, 2014. http://ethesis.nitrkl.ac.in/6149/1/E-66.pdf.

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The quality of electric power and disturbances occurred in power signal has become a major issue among the electric power suppliers and customers. For improving the power quality continuous monitoring of power is needed which is being delivered at customer’s sites. Therefore, detection of PQ disturbances, and proper classification of PQD is highly desirable. The detection and classification of the PQD in distribution systems are important tasks for protection of power distributed network. Most of the disturbances are non-stationary and transitory in nature hence it requires advanced tools and techniques for the analysis of PQ disturbances. In this work a hybrid technique is used for characterizing PQ disturbances using wavelet transform and fuzzy logic. A no of PQ events are generated and decomposed using wavelet decomposition algorithm of wavelet transform for accurate detection of disturbances. It is also observed that when the PQ disturbances are contaminated with noise the detection becomes difficult and the feature vectors to be extracted will contain a high percentage of noise which may degrade the classification accuracy. Hence a Wavelet based de-noising technique is proposed in this work before feature extraction process. Two very distinct features common to all PQ disturbances like Energy and Total Harmonic Distortion (THD) are extracted using discrete wavelet transform and are fed as inputs to the fuzzy expert system for accurate detection and classification of various PQ disturbances. The fuzzy expert system not only classifies the PQ disturbances but also indicates whether the disturbance is pure or contains harmonics. A neural network based Power Quality Disturbance (PQD) detection system is also modeled implementing Multilayer Feed forward Neural Network (MFNN).

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48

Chen, Shih-Wei, i 陳世偉. "Power Quality Disturbance Recognition using Hidden Markov Models and SOFM Network withＫ-Means Algorithm". Thesis, 2007. http://ndltd.ncl.edu.tw/handle/75364705610335374653.

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碩士
國立雲林科技大學
電機工程系碩士班
95
Because of widespread use of the sensitive electronic products, the requirements of power quality will be further emphasized. In order to know the reasons of occurrence for power quality disturbances, we need to get the signals of voltage/current disturbances that can be used to recognize what kind of disturbance event happen. Therefore, identification and recognition of voltage and current disturbances in power system is an important task in power system monitoring. This paper presents two approaches based on hidden Markov models and SOFM network withＫ-Means for recognition of power quality disturbances. The first presents an approach based on hidden Markov models for recognition of power quality disturbances. The feature extraction and vector quantization of disturbance signals are first made. Then, the hidden Markov models for each disturbance event are constructed. Finally, the test signals can be recognized by a forward algorithm and backward algorithm to obtain the results. Some discussions which include the characteristics of hidden Markov models and vector quantization are made to obtain better results. The second presents an approach based on SOFM network withＫ-Means algorithm for recognition of power quality disturbances. The feature extraction of disturbance signals are first made. Then, the SOFM network withＫ-Means algorithm for all disturbance data are trained to obtain map. Finally, the test signals can be recognized with map by SOFM network to obtain the results. Some discussions which include the characteristics of SOFM network withＫ-Means algorithm and enter noise disturbances are made to obtain better results.

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49

Paracha, Zahir Javed. "Design and development of intelligent computational techniques for power quality data monitoring and management". Thesis, 2011. https://vuir.vu.edu.au/19381/.

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The most important requirement of power system operations is sustained availability and quality supply of electric power. In Electrical Power Distribution System (EPDS), non-linear loads are the main cause of power quality (PQ) degradation. The PQ problems generated by these non-linear loads are complex and diversified in nature. The power system which is not capable to handle non-linear loads faces the problem of voltage unbalance, sag, swell, momentary or temporary interruption and ultimately complete outage of EPDS. The PQ problems have motivated power system engineers to design and develop new methodologies and techniques to enhance EPDS performance. To do so, they are required to analyse the PQ data of the system under consideration. Since, the density of the monitoring nodes in EPDS is quite high, the aggregate analysis is computationally involved. In addition, the cost involved with the PQ shortcomings is significantly high (for domestic consumers and rises exponentially for industrial consumers), hence it also becomes mandatory to project /predict the undesired PQ disturbance in EPDS. This will provides power system engineers to formulate intelligent strategy for efficient power system operations. This objective of the research is to identify and exploit the hidden correlation in PQ data with minimal computational cost and further use this knowledge to classify any PQ disturbance that may occur. ... Further this research also investigates the power distribution system behaviour considering the relationship of main PQ disturbance harmonics in conjunction with the other major PQ parameters i.e. voltage unbalance, sag, swell and frequency.

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