Relevant bibliographies by topics / Power quality disturbances

Academic literature on the topic 'Power quality disturbances'

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Published: 6 September 2023
Last updated: 11 September 2023

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Journal articles on the topic "Power quality disturbances"

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Liu, Bin, and Xi Wang. "Quality Disturbance Recognition Based on the Generalized-S Transform." Applied Mechanics and Materials 246-247 (December 2012): 251–56. http://dx.doi.org/10.4028/www.scientific.net/amm.246-247.251.

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In order to achieve the power quality disturbance signal feature extraction and automatic classification of power quality disturbances based on the generalized S transform to identify the improved algorithm, the generalized S transform results according to the power quality disturbance signal, extract the characteristics of power quality disturbance signal, to achieve power quality disturbances automatic identification of the signal. Through a standard sinusoidal signal simulation examples prove that the algorithm has high noise immunity, simple structure, and high recognition rate.
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Peng, Fei Jin, Xiao Yun Huang, Hong Yuan Huang, and Zhi Wen Xie. "A Novel Power Quality Disturbances Detection and Classification Method." Applied Mechanics and Materials 737 (March 2015): 193–98. http://dx.doi.org/10.4028/www.scientific.net/amm.737.193.

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Power quality disturbance detection and identification is the prerequisite and basis for the power quality management and control. This paper presents a new power quality disturbance detection and classification method. Firstly, the time-time transform is applied to power quality disturbance signal analysis. According to spectrum analysis results of the diagonal elements of time-time transform matrix, a preliminary judge about whether the disturbance signal contains harmonics and inter harmonic was given. For disturbances with non-harmonics, based on time-time transform modulus matrix diagonal sequence, the beginning and ending time of the disturbance is located, and the disturbance amplitude is calculated. For the disturbances which contain harmonics, time-time transform is perform twice to get the row mean value curve and the column mean value curve, which are required by disturbance time location and amplitude measurement. Finally, disturbance classification had realized by using rule tree. Simulation results reveal that this method is very robust and adaptable, which can identify transient power quality disturbance with minor magnitude under noisy environment, and the recognition rate is satisfactory.
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Saini, Manish Kumar, and Rajiv Kapoor. "Power Quality Events Classification Using MWT and MLP." Advanced Materials Research 403-408 (November 2011): 4266–71. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.4266.

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The work presented uses multiwavelet because of its inherent property to resolve the signal better than all single wavelets. Multiwavelets are based on more than one scaling function. The proposed methodology utilizes an enhanced resolving capability of multiwavelet to recognize power system disturbances. The disturbance classification schema is performed with multiwavelet neural network (MWNN). It performs a feature extraction and a classification algorithm composed of a multiwavelet feature extractor based on norm entropy and a classifier based on a multi-layer perceptron. The performance of this classifier is evaluated by using total 1000 PQ disturbance signals which are generated the based model. The classification performance of different PQ disturbance using proposed algorithm is tested. The rate of average correct classification is about 99.65% for the different PQ disturbance signals and noisy disturbances.
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Mozaffari, Mahsa, Keval Doshi, and Yasin Yilmaz. "Real-Time Detection and Classification of Power Quality Disturbances." Sensors 22, no. 20 (October 19, 2022): 7958. http://dx.doi.org/10.3390/s22207958.

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This paper considers the problem of real-time detection and classification of power quality disturbances in power delivery systems. We propose a sequential and multivariate disturbance detection method (aiming for quick and accurate detection). Our proposed detector follows a non-parametric and supervised approach, i.e., it learns nominal and anomalous patterns from training data involving clean and disturbance signals. The multivariate nature of the method enables joint processing of data from multiple meters, facilitating quicker detection as a result of the cooperative analysis. We further extend our supervised sequential detection method to a multi-hypothesis setting, which aims to classify the disturbance events as quickly and accurately as possible in a real-time manner. The multi-hypothesis method requires a training dataset per hypothesis, i.e., per each disturbance type as well as the ’no disturbance’ case. The proposed classification method is demonstrated to quickly and accurately detect and classify power disturbances.
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Gonzalez-Abreu, A. D., M. Delgado-Prieto, J. J. Saucedo-Dorantes, and R. A. Osornio-Rios. "Novelty Detection on Power Quality Disturbances Monitoring." Renewable Energy and Power Quality Journal 19 (September 2021): 211–16. http://dx.doi.org/10.24084/repqj19.259.

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Complex disturbance patterns take place over the corresponding power supply networks due to the increased complexity of electrical loads at industrial plants. Such complex patterns are the result of a combination of simpler standardized disturbances. However, their detection and identification represent a challenge to current power quality monitoring systems. The detection of disturbances and their identification would allow early and effective decision-making processes towards optimal power grid controls or maintenance and security operations of the grid. In this regard, this paper presents an evaluation of the four main techniques for novelty detection: k-Nearest Neighbor, Gaussian Mixture Models, One-Class Support Vector Machine, and Stacked Autoencoder. A set of synthetic signals have been considered to evaluate the performance and suitability of each technique as an anomaly detector applied to power quality disturbances. A set of statistical features have been considered to characterize the power line. The evaluation of the techniques is carried out throughout different scenarios considering combined and single disturbances. The obtained results show the complementary performance of the considered techniques in front of different scenarios due to their differences in the knowledge modelization.
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Podestà, L., G. Sforza, A. Churikov, A. Divin, and A. Filatova. "Wireless Sensors-Based Network to Measure Different Power Quality Disturbances." Advanced Materials & Technologies, no. 2 (2017): 026–37. http://dx.doi.org/10.17277/amt.2017.02.pp.026-037.

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Gu, Jin Hong, Qi Liu, and Chao Hui Cheng. "Simulation of Power Quality Using S-Transform." Advanced Materials Research 429 (January 2012): 172–78. http://dx.doi.org/10.4028/www.scientific.net/amr.429.172.

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According to the signal characteristics of power quality disturbances, a detection and classification method based on S-transform is proposed. The S-transform module matrix is used to detect and classify power quality disturbance signal. Eight disturbance signals (voltage sag, voltage swell, momentary interruption, voltage spike, voltage notch, harmonic, inter-harmonic and oscillatory transients) which influence power quality have been simulated. The results show that the method can be used to localize the disturbance time and duration precisely and classify them simply.
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Suja, S., and Jovitha Jerome. "POWER SIGNAL DISTURBANCE CLASSIFICATION USING WAVELET BASED NEURAL NETWORK." ASEAN Journal on Science and Technology for Development 25, no. 2 (November 22, 2017): 205–17. http://dx.doi.org/10.29037/ajstd.243.

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In this paper, the power signal disturbances are detected using discrete wavelet transform (DWT) and categorized using neural networks. This paper presents a prototype of power quality disturbance recognition system. The prototype contains three main components. First a simulator is used to generate power signal disturbances. The second component is a detector which uses the technique of DWT to detect the power signal disturbances. DWT is used to extract disturbance features in the power signal. The third component is neural network architecture to classify the power signal disturbances.
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Parsons, A. C., W. M. Grady, E. J. Powers, and J. C. Soward. "A direction finder for power quality disturbances based upon disturbance power and energy." IEEE Transactions on Power Delivery 15, no. 3 (July 2000): 1081–86. http://dx.doi.org/10.1109/61.871378.

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Suja, S., and Jovitha Jerome. "Power signal disturbance classification using wavelet based neural network." Serbian Journal of Electrical Engineering 4, no. 1 (2007): 71–83. http://dx.doi.org/10.2298/sjee0701071s.

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In this paper, the power signal disturbances are detected using discrete wavelet transform (DWT) and categorized using neural networks. This paper presents a prototype of power quality disturbance recognition system. The prototype contains three main components. First a simulator is used to generate power signal disturbances. The second component is a detector which uses the technique of DWT to detect the power signal disturbances. DWT is used to extract disturbance features in the power signal. These coefficients obtained from DWT are further subjected to statistical manipulations for increasing the detection accuracy. The third component is neural network architecture to classify the power signal disturbances with increased accuracy of detection.
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Dissertations / Theses on the topic "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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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.
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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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Books on the topic "Power quality disturbances"

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Yu-Hua, Gu Irene, and Knovel (Firm), eds. Signal processing of power quality disturbances. Piscataway, NJ: IEEE Press, 2006.

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Signal Processing of Power Quality Disturbances. New York: John Wiley & Sons, Inc., 2006.

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Gu, Irene Y. H., and Math H. J. Bollen. Signal Processing of Power Quality Disturbances. Wiley & Sons, Incorporated, John, 2006.

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Bollen, Math H., and Irene Gu. Signal Processing of Power Quality Disturbances. Wiley & Sons, Incorporated, John, 2006.

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Olsen, Isak. Electrical Generation and Distribution Systems and Power Quality Disturbances. Scitus Academics LLC, 2017.

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Romero, Gregorio, ed. Electrical Generation and Distribution Systems and Power Quality Disturbances. InTech, 2011. http://dx.doi.org/10.5772/1426.

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Signal Processing of Power Quality Disturbances (IEEE Press Series on Power Engineering). Wiley-IEEE Press, 2006.

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Book chapters on the topic "Power quality disturbances"

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Ping, Ji. "Power Quality Disturbances Detection Based on EMD." In Advances in Intelligent Systems and Computing, 88–99. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14680-1_11.

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Panigrahi, B. K., and Nilanjan Senroy. "A Meta-heuristic Approach for Optimal Classification of Power Quality Disturbances." In Power Electronics and Power Systems, 53–64. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17190-6_2.

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Borges, Fábbio, Ivan Silva, Ricardo Fernandes, and Lucas Moraes. "Classification of Power Quality Disturbances Using Forest Algorithm." In Data Mining and Big Data, 247–52. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40973-3_24.

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Fonseca, Tiago, and João F. Martins. "Power Quality Disturbances Recognition Based on Grammatical Inference." In Technological Innovation for Sustainability, 474–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19170-1_52.

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Samal, Laxmipriya, Hemanta Kumar Palo, Badri Narayan Sahu, and Debashisa Samal. "Characterization of Power Quality Disturbances and Their Efficient Classification." In Advances in Electrical Control and Signal Systems, 969–81. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5262-5_75.

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Mahela, Om Prakash, Shruti Rathore, Shoyab Ali, and Baseem Khan. "An Algorithm for Identification of Multiple Power Quality Disturbances." In Artificial Intelligence-Based Energy Management Systems for Smart Microgrids, 201–30. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/b22884-10.

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Umashankar, Subramaniam, Vishnu Kalaiselvan Arun Shankar, Shanmugam Paramasivam, Padmanaban Sanjeevikumar, and K. Anil Kumar. "Survey of Power Quality Discrete Disturbances in an Educational Institution." In Advances in Power Systems and Energy Management, 377–91. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4394-9_38.

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Alghazi, Omnia Sameer, and Saeed Mian Qaisar. "Power Quality Disturbances Classification Based on the Machine Learning Algorithms." In Research and Innovation Forum 2022, 165–77. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-19560-0_13.

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Fang, Fang, Zhensheng Wang, Tianhong Pan, Jun Tao, and Huaying Zhang. "Distinguishment of Power Quality Disturbances Using Segmented Adaptive S Transform." In Conference Proceedings of 2022 2nd International Joint Conference on Energy, Electrical and Power Engineering, 123–28. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4334-0_15.

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Krishnanand, K. R., Santanu Kumar Nayak, B. K. Panigrahi, V. Ravikumar Pandi, and Priyadarshini Dash. "Classification of Power Quality Disturbances Using GA Based Optimal Feature Selection." In Lecture Notes in Computer Science, 561–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-11164-8_91.

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Conference papers on the topic "Power quality disturbances"

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Nahoum, Pamela, Emile Yammine, Elie Karam, Maged B. Najjar, and Moustapha El Hassan. "Real generation of power quality disturbances." In 2015 Third International Conference on Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE). IEEE, 2015. http://dx.doi.org/10.1109/taeece.2015.7113631.

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Klaic, Z., D. Sipl, and S. Nikolovski. "Economic impact of power quality disturbances." In 22nd International Conference and Exhibition on Electricity Distribution (CIRED 2013). Institution of Engineering and Technology, 2013. http://dx.doi.org/10.1049/cp.2013.1083.

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Kezunovic, M. "Automated analysis of power quality disturbances." In 16th International Conference and Exhibition on Electricity Distribution (CIRED 2001). IEE, 2001. http://dx.doi.org/10.1049/cp:20010781.

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Shin, Y. J., E. Powers, M. Grady, and A. Arapostathis. "Power quality indices for transient disturbances." In 2006 IEEE Power Engineering Society General Meeting. IEEE, 2006. http://dx.doi.org/10.1109/pes.2006.1708866.

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Ribeiro, M. V., C. A. Marques, C. A. Duque, A. S. Cerqueira, and J. L. R. Pereira. "Power quality disturbances detection using HOS." In 2006 IEEE Power Engineering Society General Meeting. IEEE, 2006. http://dx.doi.org/10.1109/pes.2006.1709131.

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Islam, Md Moinul, Dhiman Chowdhury, Md Multan Biswas, Navila Rahman, and Charles Brice. "Redefined Power Quality Indices for Stationary and Nonstationary Power Quality Disturbances." In SoutheastCon 2019. IEEE, 2019. http://dx.doi.org/10.1109/southeastcon42311.2019.9020388.

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Islam, Moinul, P. Sanjeevikumar, and Charles W. Brice. "Redefined Power Quality Indices for Stationary and Nonstationary Power Quality Disturbances." In IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2019. http://dx.doi.org/10.1109/iecon.2019.8927242.

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Morrison, R. E. "Voltage disturbances, waveform distortion and unbalance." In IEE Colloquium on Issues in Power Quality. IEE, 1995. http://dx.doi.org/10.1049/ic:19951420.

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Rajagopalan, Satish, Brian Fortenbery, and Dennis Symanski. "Power quality disturbances within DC data centers." In INTELEC 2010 - 2010 International Telecommunications Energy Conference. IEEE, 2010. http://dx.doi.org/10.1109/intlec.2010.5525723.

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Cerqueira, Augusto S., Marcelo A. A. Lima, Marlon L. G. Salmento, Julio V. de Souza, and Denis V. Coury. "Nonlinear filtering for Power Quality disturbances analysis." In 2010 14th International Conference on Harmonics and Quality of Power (ICHQP). IEEE, 2010. http://dx.doi.org/10.1109/ichqp.2010.5625382.

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