Journal articles: 'Power quality disturbances'

1

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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Huang, Cong-Hui, and Chia-Hung Lin. "Multiple Chaos Synchronization System for Power Quality Classification in a Power System." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/902167.

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This document proposes multiple chaos synchronization (CS) systems for power quality (PQ) disturbances classification in a power system. Chen-Lee based CS systems use multiple detectors to track the dynamic errors between the normal signal and the disturbance signal, including power harmonics, voltage fluctuation phenomena, and voltage interruptions. Multiple detectors are used to monitor the dynamic errors between the master system and the slave system and are used to construct the feature patterns from time-domain signals. The maximum likelihood method (MLM), as a classifier, performs a comparison of the patterns of the features in the database. The proposed method can adapt itself without the need for adjustment of parameters or iterative computation. For a sample power system, the test results showed accurate discrimination, good robustness, and faster processing time for the detection of PQ disturbances.

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Bajandooh, Abdulrahman A., and Muhyaddin J. Rawa. "Power Quality Disturbances of Electrified Railway." International Journal of Engineering Research and Technology 13, no. 10 (October 31, 2020): 3020. http://dx.doi.org/10.37624/ijert/13.10.2020.3020-3028.

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Shin, Y. J., E. J. Powers, M. Grady, and A. Arapostathis. "Power Quality Indices for Transient Disturbances." IEEE Transactions on Power Delivery 21, no. 1 (January 2006): 253–61. http://dx.doi.org/10.1109/tpwrd.2005.855444.

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Duran-Tovar, Ivan Camilo, Fabio Andrés Pavas-Martínez, and Oscar German Duarte-Velasco. "Effects on lifetime of low voltage conductors due to stationary power quality disturbances." DYNA 82, no. 192 (August 25, 2015): 44–51. http://dx.doi.org/10.15446/dyna.v82n192.48568.

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<p>This paper presents a methodology to estimate the effects of heating and lifetime in Low Voltage conductors (LV) due to the presence of stationary power quality disturbances. Conductor overheating and cable insulation accelerated aging can be caused by temporary increases in the rms values of the voltages and currents due to stationary disturbances. Waveform distortion, unbalance and phase displacements can be considered among the stationary disturbances. For disturbances with short duration, there is no significant reductions in the insulation lifetime, but disturbances acting for long time periods will cause cumulative and detrimental effects. Currently valid models for insulation aging are employed; the expected power quality disturbance levels are extracted from power quality data bases. A discussion about the effects on insulation lifetime is presented.</p>

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Qian, Ping, Yu Juan Wang, Yin Zhong Ye, and Jin Sheng Liu. "Lifting Wavelet Detection Method of Transient Power Quality Disturbance for Power System Connected with Micro Grid." Applied Mechanics and Materials 568-570 (June 2014): 274–77. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.274.

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Based on the power quality disturbance problems of the power system connected with micro grid, The detecting method of transient power quality disturbance is mainly studied, which based on lifting wavelet transform, after the analysis of lifting wavelet construction principle, the transient power quality disturbance detecting method based lifting db4 wavelet is put forward, the results of simulation and comparison analysis prove that the method can detect and locate the transient power quality disturbances quickly and accurately, so that ,an effective and feasible method is provided to the research of transient power quality disturbance problems brought by the connection between micro grid and power system.

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Song, Yan Li, Ran Tao, and An Na Wang. "Detection and Localization Method of Power Quality Disturbance Based on Improved TT-Transform." Applied Mechanics and Materials 433-435 (October 2013): 1276–81. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.1276.

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Towards the problem of power quality disturbance detecting and localization in the power system, this paper proposed a new method based on improved TT-transform. Amplitude’s mutation in maximum element sequence of the TT- module matrix’s row is detected to locate beginning and ending time of power quality disturbances. This method can not only detect single power quality disturbance, but also detect composite disturbance accurately. The simulation results show that the method proposed can accurately detect the common power quality disturbance signal.

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AKKAYA, Sıtkı. "Empirical Investigations: Power Quality Disturbance Classification." International Conference on Applied Engineering and Natural Sciences 1, no. 1 (July 20, 2023): 320–24. http://dx.doi.org/10.59287/icaens.1014.

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In electrical power systems, one of the most essential parameters is a system signal with stable fundamental amplitude and frequency. Some disturbances have a negative impact on this stability, yet.These disturbances, known as power quality disturbances (PQDs), encompass phenomena such as sag, interruption, swell, harmonics, flicker, interharmonics, spike, notch, and transients. PQDs can ariseindividually or in some combinations. They pose unpredictable and variable effects on system componentsgiving rise to destructive outcomes. Acquisition of real-world datasets related to these disturbances is challenging due to the randomness and complex nature of power systems. Therefore, the importance of conducting experimental studies to investigate and analyze PQDs has significantly increased. This study aims to serve as a valuable resource for researchers investigating PQDs. It provides a guidance, offering insights into some types of PQDs and their characteristics. This paper supports researchers in understanding and addressing the challenges based on PQDs by presenting knowledge about these disturbances and their impacts. Recognizing the significance of experimental studies, the compilation includes methodologies, experimental setups, and tools employed for studying PQDs. It emphasizes the necessity for experimental datasets to enhance research in this field and highlights the importance of PQD investigation.

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Barros, Julio, Matilde de Apráiz, and Ramón Diego. "Power Quality in DC Distribution Networks." Energies 12, no. 5 (March 5, 2019): 848. http://dx.doi.org/10.3390/en12050848.

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This paper presents an overview of power quality in low-voltage DC distribution networks. We study which of the power quality disturbances in AC networks are also relevant in DC networks, as well as other disturbances specific to DC networks. The paper reviews the current status of international regulations in this topic and proposes different indices for the detection and characterization of the main types of power quality disturbances, presenting some results obtained in different laboratory tests in DC networks using different DC voltage shapes delivered by different DC power source types.

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Liu, Dongmei, Weiyuan Zhu, Yanhui Wang, Ziyi Chang, Kaikai Xie, and Shun Wang. "Power quality transient disturbances detection system based on db5 wavelet." Journal of Physics: Conference Series 2564, no. 1 (August 1, 2023): 012010. http://dx.doi.org/10.1088/1742-6596/2564/1/012010.

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Abstract To improve the real-time and accuracy of transient disturbance localization in the power grid, this paper designs an FPGA-based transient disturbance detection system based on the good time-frequency local analysis capability, multi-resolution analysis characteristics of wavelet decomposition, and the selection of db5 (Daubechies 5) wavelets. The system acquires the original disturbance signal through an external high-speed AD/DA board, implements the wavelet transform algorithm by the EP4CE10F17C8 FPGA, and then feeds back to the high-speed AD/DA board to output the location of the singularities in the high-frequency detail signal that corresponds to the start and end moments of the disturbance. Simulation and actual test results together show that the system can accurately and quickly locate the start and end moments of typical single disturbances such as voltage sag and short interruption, with a measurement deviation of within 3ms, meeting the real-time requirements.

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De Lima, Robson Rosserrani, Danton Diego Ferreira, José Manoel de Seixas, and Leonardo Silveira Paiva. "A SIMPLE PATTERN RECOGNITION-BASED METHOD FOR POWER QUALITY DISTURBANCE DETECTION." Theoretical and Applied Engineering 5, no. 3 (April 20, 2021): 1–10. http://dx.doi.org/10.31422/taae.v5i3.36.

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Voltage disturbances are the most frequent cause of a large range of disruption in industrial, commercial, and residential power supply systems. These disturbances are often referred to as power quality problems and affect the Power Systems causing substantial losses. To avoid the storage of a large amount of data, the first task in monitoring the power quality is the realtime detection of disturbances, which must be performed by an accurate and low-complexity system. This paper proposes a low-complexity system for power quality disturbance detection. The method makes innovative use of simple features extracted from reduced segments of the monitored voltage waveform. The extract features (the mean value, variance, energy, and the maximum and minimum values of the filtered voltage signals) require low computational effort and allow a considerable dimensional reduction of the signals, leading to simple detection algorithms. The proposed method achieves high detection rates on both simulated and real signals.

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Neupane, Bishnu Prasad. "Power quality monitoring and analysis of aerodrome supply system: A study in Gautam Buddha International Airport, Nepal." Journal of Engineering Issues and Solutions 2, no. 1 (June 6, 2023): 64–74. http://dx.doi.org/10.3126/joeis.v2i1.49481.

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The power supply system may contain disturbances leading to variations in voltage and frequency from their nominal value, non-sinusoidal waveform injection, presence of negative and zero sequence components, etc. Power quality is a measure of characteristic disturbances present in the power supply which ensures the importance of quality of service and consumer requirements. Electrical equipment installed in an aerodrome is more sensitive and prone to disturbance present at the power supply end. This study provides results of a theoretical power flow study, power quality monitoring, quality analysis, and probable remedies for power quality issues in an aerodrome electrical supply system in Nepal. Power quality measurement for supply from the utility here, Nepal Electricity Authority, and internal power generations in Gautam Buddha International Airport (GBIA) Siddharthanagar, Rupandehi, Nepal is performed by using theoretical load flow approach and continuous monitoring using a power data analyzer. The scope and origin of various disturbances which may induce power quality issues are analyzed. It is observed that the power supply at GBIA has voltage fluctuations of about ±12% and total voltage harmonic distortion of 6.5% with the presence of negative and zero sequence components. A remedial solution for the power quality problem is proposed for future enhancement of the electrical supply system at GBIA.

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Bharatiraja, C., and Harish Chowdary V. "Real Time Power Quality Phenomenon for Various Distribution Feeders." Indonesian Journal of Electrical Engineering and Computer Science 3, no. 1 (July 1, 2016): 10. http://dx.doi.org/10.11591/ijeecs.v3.i1.pp10-16.

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Power Quality (PQ) brings more challenges to the large- scale and medium scale industries because in the recent years most of them use high efficiency and low energy devices which cause vulnerable PQ disturbances at Point of Common Coupling (PCC). In this paper, the measurement at different times during load condition and analysis of all types of disturbances occurred has been done. When large rated equipments run, the disturbance (harmonics, RMS variations, and switching transients) levels are very high and poor power factor (PF) has also appeared. Due to this poor PF, reactive power consumption in load increases and accordingly total power increases. An electronic device such as LED lights, fluorescent lamps, computers, copy machines, and laser printers also disturb the supply voltage. We are very well known that every PQ problem directly or indirectly must affect economically. Many researchers have investigated PQ audit for over three decades. However these studies and analysis have been done only at simulation level. Hence, the PQ analyzer based study is required to find out the PQ issues at distribution feeders. It will be a valuable guide for researchers, who are interested in the domain of PQ and wish to explore the opportunities offered by these techniques for further improvement in the field of PQ. This paper gives a brief Real Time PQ measurement using PQ analyzer HIOKI PW3198 at Distribution Feeders and it gives an idea to the researcher to optimize problems-related to PQ with respect to the high rated and low rated electric machinery of different feeders at PCC level. This study further extends to analyze the grid disturbances and looks forward to the optimization methods for each individual PQ disturbance.

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Chan, Kok Wai, Rodney H. G. Tan, and V. H. Mok. "Simulation of Power Quality Disturbances Using PSCAD." Applied Mechanics and Materials 785 (August 2015): 373–77. http://dx.doi.org/10.4028/www.scientific.net/amm.785.373.

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Power quality is becoming a concern in modern electrical network due to complexity of the system. To improve power quality, there needs to be analysis and research on complex power quality events systematically. Disturbances in distribution system, including sag/swell, transient, harmonic, voltage notch, and flicker that affect power quality are simulated in this research work. The comprehensive set of models are developed in PSCAD with minimum blocks and settings without compromising the essence of power quality events. This research work aims to introduce the power quality events to electrical engineering students, as well as reinforce understanding of power quality disturbances in distribution system through interactive simulation approach.

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Barros, Julio, Daniel Cando, and Iker Durana. "A Laboratory for Power Quality Analysis." International Journal of Electrical Engineering & Education 38, no. 3 (July 2001): 210–22. http://dx.doi.org/10.7227/ijeee.38.3.3.

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This paper describes a laboratory designed for electrical power quality analysis. Among the different types of disturbances in voltage supply that the laboratory allows us to generate are harmonics, voltage dips and short interruptions in voltage supply, voltage imbalance and frequency deviations. Using this laboratory we can test software for analysis, detection and classification of power quality disturbances and also study their effects on equipment.

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Yang, Han Sheng. "Denoising Power Quality Signal Using Savitzky-Golay Based on Virtual Instrument." Advanced Materials Research 655-657 (January 2013): 974–77. http://dx.doi.org/10.4028/www.scientific.net/amr.655-657.974.

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In power quality monitoring system, there are unavoidably existing various kinds of noises in collected data，the presence of noise may result in increased false classification rate, denoising is an extremely important work for detection and classification of power quality disturbances. In order to improve the denoising result of power quality signal, an denoising method for power quality signal using Savitzky-Golay is proposed. Numerical results show that the proposed method can eliminate the influence of noise components and implement transient power quality disturbance detection and localization, thus providing good foundations for transient power quality disturbance monitoring under noise environment.

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Kurnia, Rani, and Riki Mukhaiyar. "Implementasi Metode Fast Fourier Transform pada Sistem Monitoring Voltage Flicker." Ranah Research : Journal of Multidisciplinary Research and Development 3, no. 3 (May 3, 2021): 136–46. http://dx.doi.org/10.38035/rrj.v3i3.385.

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West Sumatra is recorded to have an electrification ratio of 92.96%. Every year this figure will continue to increase, with the increase in the electrification ratio in West Sumatra, the quality of the required electric power will increase. Meanwhile, an increase in the electrification ratio will cause a decrease in the quality of electric power. The power quality of the electric power system is affected by disturbances. In other words, some disturbances occur in the electric power system which causes a decrease in power quality. The disturbance is in the form of external factors and internal factors. External factors include lightning, fallen trees, and others, while internal factors include short interruptions, voltage swells, voltage and current transients, and voltage flicker. Of these disturbances, the discussion will be focused on voltage flicker disturbances. Voltage flickers have several pathways, including causing malfunctions in the protective relay, and voltage surges that can cause bright and dim flickering of lighting lamps. The purpose of this final project is to create a tool to detect the presence of Voltage Flicker using the fast Fourier transform method so that it can observe the flicker that occurs with fast Fourier transform waves in Matlab applications.

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Gonzalez-Abreu, Artvin-Darien, Miguel Delgado-Prieto, Roque-Alfredo Osornio-Rios, Juan-Jose Saucedo-Dorantes, and Rene-de-Jesus Romero-Troncoso. "A Novel Deep Learning-Based Diagnosis Method Applied to Power Quality Disturbances." Energies 14, no. 10 (May 14, 2021): 2839. http://dx.doi.org/10.3390/en14102839.

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Monitoring electrical power quality has become a priority in the industrial sector background: avoiding unwanted effects that affect the whole performance at industrial facilities is an aim. The lack of commercial equipment capable of detecting them is a proven fact. Studies and research related to these types of grid behaviors are still a subject for which contributions are required. Although research has been conducted for disturbance detection, most methodologies consider only a few standardized disturbance combinations. This paper proposes an innovative deep learning-based diagnosis method to be applied on power quality disturbances, and it is based on three stages. Firstly, a domain fusion approach is considered in a feature extraction stage to characterize the electrical power grid. Secondly, an adaptive pattern characterization is carried out by considering a stacked autoencoder. Finally, a neural network structure is applied to identify disturbances. The proposed approach relies on the training and validation of the diagnosis system with synthetic data: single, double and triple disturbances combinations and different noise levels, also validated with available experimental measurements provided by IEEE 1159.2 Working Group. The proposed method achieves nearly a 100% hit rate allowing a far more practical application due to its capability of pattern characterization.

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Sekar, Kavaskar, Karthick Kanagarathinam, Sendilkumar Subramanian, Ellappan Venugopal, and C. Udayakumar. "An Improved Power Quality Disturbance Detection Using Deep Learning Approach." Mathematical Problems in Engineering 2022 (May 21, 2022): 1–12. http://dx.doi.org/10.1155/2022/7020979.

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Recently, the distribution network has been integrated with an increasing number of renewable energy sources (RESs) to create hybrid power systems. Due to the interconnection of RESs, there is an increase in power quality disturbances (PQDs). The aim of this article was to present an innovative method for detecting and classifying PQDs that combines convolutional neural networks (CNNs) and long short-term memory (LSTM). The disturbance signals are fed into a combined CNN and LSTM model, which automatically recognizes and classifies the features associated with power quality disturbances. In comparison with other methods, the proposed method overcomes the limitations associated with conventional signal analysis and feature selection. Additionally, to validate the proposed method's robustness, data samples from a modified IEEE 13-node hybrid system are collected and tested using MATLAB/Simulink. The results are good and encouraging.

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Zhang, Hong, Zhi Guo Lei, Yan Chun Guo, and Zhao Yu Pian. "Application of Power Quality Disturbance Location Based on MUDW." Advanced Materials Research 1070-1072 (December 2014): 745–48. http://dx.doi.org/10.4028/www.scientific.net/amr.1070-1072.745.

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It is the random and irregular and variable properties of disturbance signal in the detection of power quality, there is lack of mature methods for detection and location of PQ disturbances. An improved morphological undecimated wavelet scheme was presented in this paper and applied to the detection of power quality disturbance. The improved MUDW scheme, which meets signal reconstruction conditions, contains broad open-close or close-open filter and morphological gradient which detect mutations on the top and bottom edges of signal. It used MATLB to detect transient or steady state with single or composite disturbance signals and made comparison with the existing form of sampling wavelet. The result shows that the new MUDW scheme can recognize and detect the signal and has a good anti-noise performance.

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Rahim Abdullah, Abdul, Nur Hafizah Tul Huda Ahmad, N. A. Abidullah, N. H. Shamsudin, and M. H. Jopri. "Performance Evaluation of Real Power Quality Disturbances Analysis Using S-Transform." Applied Mechanics and Materials 752-753 (April 2015): 1343–48. http://dx.doi.org/10.4028/www.scientific.net/amm.752-753.1343.

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Power quality is main issue because of the impact to electricity suppliers, equipments, manufacturers and user.To solve the power quality problem, an analysis of power quality disturbances is required to identify and rectify any failures on power system. Most of researchers apply fourier transform in power quality analysis, however the ability of fourier transform is limited to spectral information extraction that can be applied on stationary disturbances. Thus, time-frequency analysis is introduced for analyzing the power quality distubances because of the limitation of fourier transform. This paper presents the analysis of real power quality disturbances using S-transform. This time-frequency distribution (TFD) is presented to analyze power quality disturbances in time-frequency representation (TFR). From the TFR, parameters of the disturbances such as instantaneous of root mean square (RMS), fundamental RMS, total harmonic distortion (THD), total nonharmonic distortion (TnHD) and total waveform distortion (TWD) of the disturbances are estimated. The experimental of three phase voltage inverter and starting motor are conducted in laboratory to record the real power quality disturbances. The disturbances are recorded via data logger system which is mplemented using LabVIEW while the analysis is done using Matlab in offline condition. The results show that S-transform gives good performance in identifying, detecting and analyzing the real power quality disturbances, effectively.

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Kanirajan, P., M. Joly, and T. Eswaran. "Recognition of Power Quality Disturbances using Fuzzy Expert Systems." WSEAS TRANSACTIONS ON SIGNAL PROCESSING 16 (January 19, 2021): 166–77. http://dx.doi.org/10.37394/232014.2020.16.18.

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This paper presents a new approach to detect and classify power quality disturbances in the power system using Fuzzy C-means clustering, Fuzzy logic (FL) and Radial basis Function Neural Networks (RBFNN). Feature extracted through wavelet is used for training, after training, the obtained weight is used to classify the power quality problems in RBFNN, but it suffers from extensive computation and low convergence speed. Then to detect and classify the events, FL is proposed, the extracted characters are used to find out membership functions and fuzzy rules being determined from the power quality inherence. For the classification,5 types of disturbance are taken in to account. The classification performance of FL is compared with RBFNN.The clustering analysis is used to group the data in to clusters to identifying the class of the data with Fuzzy C-means algorithm. The classification accuracy of FL and Fuzzy C-means clustering is improved with the help of cognitive as well as the social behavior of particles along with fitness value using Particle swarm optimization (PSO),just by determining the ranges of the feature of the membership funtion for each rules to identify each disturbance specifically.The simulation result using Fuzzy C-means clustering possess significant improvements and gives classification results in less than a cycle when compared over other considered approach.

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Kanirajan, P., and M. Joly. "Fuzzy Expert System for Recognition of Power Quality Disturbances." WSEAS TRANSACTIONS ON ELECTRONICS 11 (May 20, 2020): 60–71. http://dx.doi.org/10.37394/232017.2020.11.8.

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This paper presents a new approach to detect and classify power quality disturbances in the power system using Fuzzy C-means clustering, Fuzzy logic (FL) and Radial basis Function Neural Networks (RBFNN). Feature extracted through wavelet is used for training, after training, the obtained weight is used to classify the power quality problems in RBFNN, but it suffers from extensive computation and low convergence speed. Then to detect and classify the events, FL is proposed, the extracted characters are used to find out membership functions and fuzzy rules being determined from the power quality inherence. For the classification,5 types of disturbance are taken in to account. The classification performance of FL is compared with RBFNN.The clustering analysis is used to group the data in to clusters to identifying the class of the data with Fuzzy C-means algorithm. The classification accuracy of FL and Fuzzy C-means clustering is improved with the help of cognitive as well as the social behavior of particles along with fitness value using Particle swarm optimization (PSO),just by determining the ranges of the feature of the membership funtion for each rules to identify each disturbance specifically.The simulation result using Fuzzy C-means clustering possess significant improvements and gives classification results in less than a cycle when compared over other considered approach.

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Wang, Song Jun, Qing Fen Liao, Di Chen Liu, Yu Tian Zhou, Bin Kun Xu, Yi Fei Wang, and Lie Lu. "Identification of Power Quality Disturbances Based on EEMD and TEO." Applied Mechanics and Materials 433-435 (October 2013): 469–76. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.469.

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The empirical mode decomposition (EMD) is a good time-frequency analysis method, which can deal with nonlinear and non-stationary signals. Aiming at improving modal aliasing problem brought by the traditional EMD, white noise is introduced into the improved aided analysis algorithm namely ensemble empirical mode decomposition (EEMD), instantaneous amplitude and frequency can be obtained by using teager energy operator (TEO), which is adopted to identify the type of power quality disturbance. The anti-aliasing of EEMD and real-time detection of TEO are verified by the signal simulation in Matlab. Simulation and experimental results show that the proposed algorithm can detect and locate power quality disturbances accurately and quickly, with excellent detection effects.

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Sun, Wen Sheng, Xiang Ning Xiao, Shun Tao, and Jian Wang. "Transient Power Quality Disturbances Identification and Classification Using Wavelet and Support Vector Machines." Advanced Materials Research 433-440 (January 2012): 1071–77. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.1071.

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Based on wavelet transform and support vector machines, a method of recognition and classification of transient power quality disturbance is presented. Using wavelet transform time-frequency localization characteristics, according to the principle of modulus maxima, realize the automatic detection positioning. After multi-resolution signal decomposition of PQ disturbances, multi-scale information in frequency domain and time domain of the signal can be extracted as the characteristic vectors. After choose and optimization of the eigenvectors based on the method of F-score, support vector machines are used to classify these eigenvectors of power quality disturbances. Effectiveness of the proposed method is verified through Matlab simulation.

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Djoudi, Djellouli, Benoudjafer Cherif, Toumi Toufik, and Othmane Abdelkhalek. "Hybrid unified power quality conditioner for power quality enhancement." International Journal of Power Electronics and Drive Systems (IJPEDS) 11, no. 4 (December 1, 2020): 2126. http://dx.doi.org/10.11591/ijpeds.v11.i4.pp2126-2134.

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In a low-voltage electrical network, harmonics, reactive power, the current and voltage imbalance, and voltage dips have harmful effects on electrical equipments. To overcome these problems, the hybrid UPQC is proposed. This paper discusses the structure of passive filters, parallel active filters, serial and combines (UPQC) to study the compensation of all types of disturbances likely to appear in the grid. Furthermore, the aim of reducing the size, cost of UPQC is to improve the quality of electric power, making it in compliance with the new regulatory constraints, we proposed the hybrid UPQC which uses passive filters and a combination of active filters. To validate the proposed topology, several sags of source voltage have been applied, at the point of common coupling (PCC). The simulation results from MATLAB/Simulink are discussed to verify the proposed topology.

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Safwan Sadeq, Mark Ovinis, and Saravanan Karuppanan. "Modeling and Simulation of PMSG Wind Energy Conversion System Using Active Disturbance Rejection Control." Journal of Advanced Research in Fluid Mechanics and Thermal Sciences 92, no. 1 (March 5, 2022): 105–22. http://dx.doi.org/10.37934/arfmts.92.1.105122.

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Electrical power generated from wind turbines inherently fluctuates due to changing wind speeds. Without proper control, disturbances such as changing wind speeds can degrade the power quality factor and robustness of the electrical grid. To ensure good power quality factor, high performance and robustness of the grid against internal and external disturbances, the use of Active Disturbance Rejection Control with an extended state observer ESO for a PMSG Wind Energy Conversion System is investigated. The system has been simulated in MATLAB/Simulink at various wind speeds. The obtained simulation results indicate that the controller maintains constant DC voltage at the interface of the generator-side converter and grid-side converters and achieves maximum power. The results also show that the system performance has good stability, precision and rejection of internal disturbances, with an overall system efficiency of 98.65%.

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Zaro, Fouad R. "Power Quality Disturbances Detection and Classification Rule-Based Decision Tree." WSEAS TRANSACTIONS ON SIGNAL PROCESSING 17 (April 15, 2021): 22–27. http://dx.doi.org/10.37394/232014.2021.17.3.

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In this paper, the power quality (PQ) disturbances have been detected and classified using Stockwell’s transform (S-transform) and rule-based decision tree (DT) according to IEEE standards. The proposed technique based on the extracted features of the PQ events signals, which are extracted from the timefrequency analysis. Several PQ disturbances are considered with simple and complex disturbances to include spike, flicker, oscillatory transient, impulsive transient, and notch. The performance and robustness of the proposed technique for the recognition of PQ disturbances have been demonstrated through the results of the various disturbances. By comparing the performance of the proposed technique with other reported studies it was distinguished results under noiseless and noisy conditions

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Lee, Chun-Yao, and Yi-Xing Shen. "Optimal Feature Selection for Power-Quality Disturbances Classification." IEEE Transactions on Power Delivery 26, no. 4 (October 2011): 2342–51. http://dx.doi.org/10.1109/tpwrd.2011.2149547.

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Sushama, M., G. Tulasi Ram Das, and A. Jaya Laxmi. "Diagnosis of Power Quality Disturbances Using Wavelet Transforms." i-manager's Journal on Electrical Engineering 3, no. 4 (June 15, 2010): 29–34. http://dx.doi.org/10.26634/jee.3.4.1186.

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Shen, Yue, Fida Hussain, Hui Liu, and Destaw Addis. "Power quality disturbances classification based on curvelet transform." International Journal of Computers and Applications 40, no. 4 (November 27, 2017): 192–201. http://dx.doi.org/10.1080/1206212x.2017.1398213.

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Khaing, Wai Phyo, Theingi Zin, and Hla Myo Tun. "Detection and Localization of Electrical Power Quality Disturbances." International Journal of Engineering Trends and Technology 11, no. 4 (May 25, 2014): 163–68. http://dx.doi.org/10.14445/22315381/ijett-v11p232.

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Ma, Jian, Jun Zhang, Luxin Xiao, Kexu Chen, and Jianhua Wu. "Classification of Power Quality Disturbances via Deep Learning." IETE Technical Review 34, no. 4 (July 28, 2016): 408–15. http://dx.doi.org/10.1080/02564602.2016.1196620.

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KIM, S. D., and M. M. MORCOS. "An Improved Method for Classifying Power Quality Disturbances." Electric Power Components and Systems 32, no. 4 (April 2004): 407–20. http://dx.doi.org/10.1080/15325000490217443.

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Zhan, Y., and H. Cheng. "Classification of power-quality disturbances in noisy conditions." IEE Proceedings - Generation, Transmission and Distribution 153, no. 6 (2006): 728. http://dx.doi.org/10.1049/ip-gtd:20050148.

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He, Haibo, Xiaoping Shen, and Janusz A. Starzyk. "Power quality disturbances analysis based on EDMRA method." International Journal of Electrical Power & Energy Systems 31, no. 6 (July 2009): 258–68. http://dx.doi.org/10.1016/j.ijepes.2009.03.017.

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Thakur, Mithilesh Kumar, and Dr Tanuj Manglani. "Detection and Analysis of Sag and Swell Power Quality Disturbances using Fractional Fourier Transform." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 1067–71. http://dx.doi.org/10.31142/ijtsrd14204.

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Yi, Ji Liang, and Ou Yang Qin. "Power Quality Disturbances Classification Based on Modified S Transform and Decision Tree." Advanced Materials Research 860-863 (December 2013): 1891–94. http://dx.doi.org/10.4028/www.scientific.net/amr.860-863.1891.

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A novel method for power quality disturbances classification is presented using modified S transform (MST) and decision tree. The time-frequency properties of power quality disturbances are analyzed and the effects of window-wide parameter g on the properties are discussed. Four statistical features are extracted from the MST module time-frequency matrix and a decision tree is utilized to recognize 9 power quality disturbances. The simulations are made to illustrate the validity of the method proposed for power quality disturbances recognition.

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Zhenhua, Shao, Chen Tianxiang, Chen Li-an, Sun Zelong, Yan Qichen, Guo Yanyan, Tao Xianliang, and Zheng Meirong. "Power Quality Disturbance Location Method based on Cross-Feedback - Recursive Least Squares." Open Electrical & Electronic Engineering Journal 9, no. 1 (July 31, 2015): 208–15. http://dx.doi.org/10.2174/1874129001409010208.

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Due to the randomness and complexity of power quality disturbances, there is lack of mature and reliable detection and analysing methods on power quality disturbance, especially in the construction site with the changeable operation condition .In order to deal with the problems of non-stationary power quality signals and spectrum leakage, a new CF Recursive Least Squares (CF-RLS) based on blind sources separation method is proposed in this paper. Furthermore the way of converging on the proposed method is based on the maximum negative entropy gradient value. In this way, the verges can be detected and the CF-RLS method can meet the requirement of signal reconstruction condition. With the help of Matlab 7.0, the simulation cases with the power system harmonics with single disturbance and mixed disturbance are discussed. Moreover the simulation results show that the harmonics parameters, including amplitudes, phase angles and disturbance time, can be detected precisely. At last, the proposed method can completely meet the requirements of the power quality disturbance location.

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Mittal, Devendra. "Classification of Power Quality Disturbances in Electric Power System: A Review." IOSR Journal of Electrical and Electronics Engineering 3, no. 5 (2012): 06–14. http://dx.doi.org/10.9790/1676-0350614.

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Pattanaik, Debasish, Sarat Chandra Swain, Indu Sekhar Samanta, Ritesh Dash, and Kunjabihari Swain. "Power Quality Disturbance Detection and Monitoring of Solar Integrated Micro-Grid." WSEAS TRANSACTIONS ON POWER SYSTEMS 17 (October 6, 2022): 306–15. http://dx.doi.org/10.37394/232016.2022.17.31.

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Due to the popularity of microgrids and power quality disturbances (PQD) induced by renewable energies, monitoring in microgrids has risen in popularity in recent years. For monitoring the PQD, many strategies based on artificial intelligence have been proposed. However, when the electrical parameters change, the need to retrain the Artificial neural network (ANN) becomes a significant issue. This paper presents a new approach to the power quality disturbance detection and monitoring of integrated solar microgrids. The power quality event detection is accomplished by analyzing the frequency signal with Wavelet transformation (WT). The classification of power quality disturbance is achieved based on the features. For the classification of PQDs, the retrieved features are fed into a Convolutional neural network (CNN) classifier.

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Journal articles on the topic 'Power quality disturbances'

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