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Journal articles on the topic 'Power quality disturbances'

Author: Grafiati

Published: 6 September 2023

Last updated: 25 July 2025

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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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M.S. Priyadarshini. "Generation of Power Spectrum of Power Quality Disturbances using MATLAB." South Asian Journal of Engineering and Technology 12, no. 5 (2022): 1–8. http://dx.doi.org/10.26524/sajet.2022.12.58.

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The aim of this paper is to present power spectrum of electrical power quality disturbances obtained by mathematical modeling. Signals considered for analysis are transients, harmonics, interruption, voltage sag, voltage swell, voltage fluctuations and voltage flicker. These power quality disturbances affect the continuity of supply and results in reduction of stability of the power system. So there is a necessity for an analysis of power quality disturbances in different perspectives. Power spectrum representation of the different disturbance signals is obtained. The values of power in Decibe

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M.S. Priyadarshini. "Generation of Power Quality Disturbances using MATLAB Simulink." South Asian Journal of Engineering and Technology 12, no. 4 (2022): 1–8. http://dx.doi.org/10.26524/sajet.2022.12.57.

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Proper operation of a power system depends upon the quality of power supplied to consumers. To have preventive measures and to design power quality monitoring equipment, it is necessary to have proper information about the signals. To design relevant equipment and compensation devices, the signals are to be generated. The objective of this paper is to generate power quality disturbances in MATLAB Simulink environment. The disturbances considered for analysis are voltage sag, swell, interruption, harmonics and transient. The aim of this paper is to present different signals which can be used fo

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4

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

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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

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Prof., A. P. Padol. "IDENTIFICATION AND CLASSIFICATION OF POWER QUALITY DISTURBANCES BY AN EFFECTIVE WAVELET BASED NEURAL CLASSIFIER." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES [NC-Rase 18] (November 16, 2018): 52–60. https://doi.org/10.5281/zenodo.1489825.

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This paper presents a wavelet based wavelet based neural for identification and classification of Power Quality disturbances. The disturbances to be classified from the power system under study and it is simulated in Power System Computer Aided Design (PSCAD).In this paper, the Power Quality disturbances to be identified and classified with the help of Discrete Wavelet Transform and Artificial Neural Network. Power Quality disturbances will be detected by using Discrete Wavelet Transform (DWT) and classified by using Artificial Neural Network (ANN). Discrete Wavelet Transform is used to extrac

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Mozaffari, Mahsa, Keval Doshi, and Yasin Yilmaz. "Real-Time Detection and Classification of Power Quality Disturbances." Sensors 22, no. 20 (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

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Bai, Hao, Ruotian Yao, Wenhan Zhang, Zhenxin Zhong, and Hongbo Zou. "Power Quality Disturbance Classification Strategy Based on Fast S-Transform and an Improved CNN-LSTM Hybrid Model." Processes 13, no. 3 (2025): 743. https://doi.org/10.3390/pr13030743.

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With the increasing complexity of power systems and the widespread application of power electronic equipment, power quality issues have become increasingly prominent, among which power quality disturbances are one of the key factors affecting the stable operation of power systems and the normal functioning of electrical equipment. Current research methods are still limited by feature extraction, insufficient model generalization ability, and strong data dependence. This paper proposes a power quality disturbance classification strategy based on the fast S-transform (FST) and an improved convol

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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 a

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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 (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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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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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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Mithilesh, Kumar Thakur, and Kumar Kumawat Neeraj. "Assessment of Power Quality Disturbance Signals Using Fractional Fourier Transform." International Journal of Research and Review 6, no. 1 (2019): 57–66. https://doi.org/10.5281/zenodo.3984754.

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In this paper disturbances like voltage sag, voltage swell and harmonics in power signal using fractional Fourier transform methods are analyzed for different values of transform 0.1 to 1. We have analyzed this algorithm in MATLAB simulation tool. This analysis is performed to find out the performance of this method on different types of disturbances. We have analyzed this to find out maximum deviation in each disturbance.

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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 increas

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N.K., Bhagat, Singh Shubhechchhu, Gupta Akash, and Tanwar Ankush. "Detection and Classification of Power Quality Disturbances Using Discrete Wavelet Transform and Neural Network Technique." Advancement of Signal Processing and its Applications 4, no. 1 (2021): 1–18. https://doi.org/10.5281/zenodo.4836272.

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<em>The nature of electric power and unsettling influences happened in power signal has become a significant issue among the electric power providers and clients. For improving the power quality ceaseless checking of power is required which is being conveyed at customer’s locales. Accordingly, recognition of PQ disturbances, and appropriate characterization of PQD is exceptionally attractive. The detection and classification of the PQD in distribution systems are important tasks for protection of power distributed network. The majority of the unsettling influences are non- stationary and

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16

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 (2000): 1081–86. http://dx.doi.org/10.1109/61.871378.

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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 (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 lo

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18

Guan, Zhaoxiong, Hancong Huangfu, and Weilin Pang. "Wavelet transform-based composite disturbance detection method for power quality in distribution networks." Journal of Physics: Conference Series 2835, no. 1 (2024): 012032. http://dx.doi.org/10.1088/1742-6596/2835/1/012032.

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Abstract Changes in modal extremum points can result in a low signal-to-noise ratio. Therefore, it is necessary to design a new composite disturbance detection method for power quality in distribution networks based on wavelet transform. That is to say, the abnormal points of composite disturbances in the power quality of the distribution network have been identified, and wavelet transform has been used for detection and classification, thus achieving comprehensive disturbance detection of power quality in the distribution network. The experimental results show that under different types of di

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19

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 comp

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20

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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21

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 res

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22

Bajandooh, Abdulrahman A., and Muhyaddin J. Rawa. "Power Quality Disturbances of Electrified Railway." International Journal of Engineering Research and Technology 13, no. 10 (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 (2006): 253–61. http://dx.doi.org/10.1109/tpwrd.2005.855444.

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Tang, Minan, Hongjie Wang, Jiandong Qiu, Zhanglong Tao, and Tong Yang. "Classification of offshore wind grid-connected power quality disturbances based on fast S-transform and CPO-optimized convolutional neural network." PLOS ONE 19, no. 12 (2024): e0314720. https://doi.org/10.1371/journal.pone.0314720.

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The large-scale integration of offshore wind power into the power grid has brought serious challenges to the power system power quality. Aiming at the problem of power quality disturbance detection and classification, this paper proposes a novel algorithm based on fast S-transform and crested porcupine optimizer (CPO) optimized CNN. Firstly, the intrinsic mechanism and waveform characteristics of offshore wind power grid-connected disturbances are analyzed, and the simulated disturbance signals are feature extracted and time-frequency diagrams are obtained by fast S-transform. Secondly, the CP

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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 (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

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Suyan, Nitin Kumar, Mahendra Kumar, Fateh L Lohar, and Deepak Agrawal. "DETECTION AND CLASSIFICATION OF COMPLEX POWER QUALITY DISTURBANCES USING DISCRETE WAVELET TRANSFORM AND RULE BASED DECISION TREE." ICTACT Journal on Microelectronics 7, no. 4 (2022): 1227–33. https://doi.org/10.21917/ijme.2022.0211.

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This paper presents a method for the detection and classification of complex power quality (PQ) disturbances using discrete wavelet transform (DWT) based ruled decision tree. The power quality disturbances are generated with the help of MATLAB using the mathematical relations as per IEEE Standard-1159. The investigated PQ disturbances include various combinations of voltage sag, voltage swell, momentary interruption, harmonics, oscillatory transient and impulsive transient. These power quality signals are decomposed using discrete wavelet transform with db4 as mother wavelet up to level 4 of d

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AKKAYA, Sıtkı. "Empirical Investigations: Power Quality Disturbance Classification." International Conference on Applied Engineering and Natural Sciences 1, no. 1 (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

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Chen, Lei, Chao Zhou, and Jianjun Chen. "Power Quality Disturbance Recognition based on Deep Neural Network and Adaptive Feature Fusion." Frontiers in Computing and Intelligent Systems 6, no. 1 (2023): 4–10. http://dx.doi.org/10.54097/fcis.v6i1.02.

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Aiming at the problems of low recognition accuracy and low noise resistance of current power quality disturbance recognition algorithms, a power quality disturbance recognition method based on the fusion of neural network and adaptive features is proposed. Firstly, one-dimensional features are extracted by 1D_CNN+GRU network. The GADF algorithm and 2D_CNN are used to extract 2D features, and then the extracted 1D features and 2D features are adaptively fused into a new feature. Finally, the new features are input into the channel attention mechanism and classified through the full connection l

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Barros, Julio, Matilde de Apráiz, and Ramón Diego. "Power Quality in DC Distribution Networks." Energies 12, no. 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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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 (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 ana

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Suyan, Nitin Kumar, Mahendra Kumar, and Fateh L Lohar. "DETECTION AND CLASSIFICATION OF POWER QUALITY DISTURBANCES USING DISCRETE WAVELET TRANSFORM AND RULE BASED DECISION TREE." ICTACT Journal on Microelectronics 7, no. 2 (2021): 1141–47. https://doi.org/10.21917/ijme.2021.0198.

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This paper presents a method for the detection and classification of power quality (PQ) disturbances using discrete wavelet transform (DWT) based decision tree. The power quality disturbances are generated with the help of MATLAB using the mathematical relations as per IEEE Standard-1159. The investigated PQ disturbances include pure sine wave, voltage sag, voltage swell, momentary interruption, harmonics, oscillatory transient, impulsive transient and notch. These power quality signals are decomposed using discrete wavelet transform with db4 as mother wavelet up to third level of decompositio

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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 localiza

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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 (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 s

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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 (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

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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. Thi

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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 (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 consumpt

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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 (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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Gnaciński, Piotr, Marcin Pepliński, Adam Muc, and Damian Hallmann. "Induction Motors Under Voltage Unbalance Combined with Voltage Subharmonics." Energies 17, no. 24 (2024): 6324. https://doi.org/10.3390/en17246324.

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In power systems, various power quality disturbances are present, including voltage deviation, voltage unbalance, and voltage waveform distortions. Voltage waveform distortions are usually identified with harmonics, but in some systems, subharmonics (subsynchronous interharmonics) and interharmonics may also occur—that is, components of frequency less than the fundamental frequency, or not an integer multiple of it. This study examines torque pulsations of an induction motor under voltage subharmonics combined with voltage unbalance. The motor and the driven DC generator vibrations were analys

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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 electric

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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 (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

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Duan, Xiaomeng, Wei Cen, Peidong He, et al. "Classification Algorithm for DC Power Quality Disturbances Based on SABO-BP." Energies 17, no. 2 (2024): 361. http://dx.doi.org/10.3390/en17020361.

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To better address and improve the issues related to DC power quality, this paper proposes an identification method tailored for DC power quality disturbances. First, it explores the underlying mechanisms and waveform characteristics of common DC power disturbances. By integrating the results of time–frequency analysis obtained through the S-transform, five distinct features are designed and extracted to serve as classification indicators. The SABO algorithm is subsequently employed to optimize the BP neural network, assisting in determining the optimal input weights and hidden layer thresholds

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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 co

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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

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Nicholas, Iderifama Gabriel, C. Idoniboyeobu D., and Horsfall Dan. "Implementing Unified Power Quality Conditioner (UPQC) to Tackle Transient Disturbances in Nigeria's 132/33kV Transmission Network." Journal of Emerging Trends in Electrical Engineering 5, no. 2 (2023): 18–27. https://doi.org/10.5281/zenodo.8009607.

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<em>The study examines system's disturbances arising from the rapid increase in load demand due to urbanization on the 132kV Afam Port-Harcourt power transmission network. The prevalent disturbances encountered by this network are voltage sags, voltage swells and harmonic distortion which often occur during the switching operation of heavy-duty electrical devices in the network. However, this has been a major challenge to the management of the Transmission Company of Nigeria (TCN) as its 132/33kV Port-Harcourt Main network lacks the required equipment to tackle these disturbances in real-t

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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 (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 simulati

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Mithilesh, Kumar Thakur, and Tanuj Manglani Dr. "Detection and Analysis of Sag and Swell Power Quality Disturbances using Fractional Fourier Transform." International Journal of Trend in Scientific Research and Development 2, no. 4 (2018): 1067–71. https://doi.org/10.31142/ijtsrd14204.

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In this paper we have detected and analyzed sag and swell type of disturbance in power signal using fractional fourier transform method. Each signal is analyzed for different values of transform order in the range of 0 to 1. We have analyzed this algorithm in MATLAB simulation tool. This analysis is performed to find out the performance of this method on different types of disturbances. We have analyzed this to find out maximum deviation in each disturbance and compare it for each signal. Mithilesh Kumar Thakur | Dr. Tanuj Manglani "Detection and Analysis of Sag and Swell Power Quality Di

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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 qualit

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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 qualit

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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 machi

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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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