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Research and Implementation of a USB Interfaced Real-Time Power Quality Disturbance Classification SystemGOK, M. , SEFA, I.
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discrete transforms, graphical user interfaces, neural networks, power quality, real-time system
power(46), quality(29), transform(19), system(17), classification(17), disturbances(16), systems(10), detection(10), time(9), real(8)
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About this article
Date of Publication: 2017-08-31
Volume 17, Issue 3, Year 2017, On page(s): 61 - 70
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.03008
Web of Science Accession Number: 000410369500008
SCOPUS ID: 85028532369
In this study, the research and implementation of an automatic power quality (PQ) recognition system are presented. This system contains a USB interfaced multichannel data acquisition (DAQ) device and a graphical user interfaced (GUI) application. The DAQ device consists of an analog-to-digital (ADC) converter, field programmable gate array (FPGA) and a USB first in first out (FIFO) buffer interface chip. The application employs Stockwell Transform (ST) technique combined with neural network model to build the classifier. Eight basic and two combined PQ disturbances are determined for the classification. Different from the previous studies, the synthetic signals used for neural network training are modified by adding the harmonics detected in the real signal. This approach is used to increase the classifier accuracy against the real line power signal. Also, ST is simplified by using only the frequencies which are required in the feature extraction step to reduce the processing time. Developed application handles the signal processing, the classification, and the database recording tasks by using multi-threaded programming approach under the mean time of 41 ms. The experimental results show that the proposed power quality disturbance detection system is capable of recognizing and reporting power quality faults effectively within the real-time requirements.
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 Dual Tree Complex Wavelet Transform with Multiobjective Optimization Algorithm for Real‐Time Power Quality Events Classification, Rahul,, Advanced Theory and Simulations, ISSN 2513-0390, Issue 10, Volume 3, 2020.
Digital Object Identifier: 10.1002/adts.202000141 [CrossRef]
 Real-Time Implementation of Optimized Power Quality Events Classifier, Markovska, Marija, Taskovski, Dimitar, Kokolanski, Zivko, Dimchev, Vladimir, Velkovski, Bodan, IEEE Transactions on Industry Applications, ISSN 0093-9994, 2020.
Digital Object Identifier: 10.1109/TIA.2020.2991950 [CrossRef]
 Review of Signal Processing Techniques and Machine Learning Algorithms for Power Quality Analysis, Rahul,, Advanced Theory and Simulations, ISSN 2513-0390, Issue 10, Volume 3, 2020.
Digital Object Identifier: 10.1002/adts.202000118 [CrossRef]
 An Advanced Genetic Algorithm with Improved Support Vector Machine for Multi-Class Classification of Real Power Quality Events, Rahul, , Choudhary, Bharat, Electric Power Systems Research, ISSN 0378-7796, Issue , 2021.
Digital Object Identifier: 10.1016/j.epsr.2020.106879 [CrossRef]
 A Novel Approach to Power Quality Analysis: Adaptive FEM Algorithm with Sparse Signal Decomposition, Rahul,, Advanced Theory and Simulations, ISSN 2513-0390, Issue 9, Volume 3, 2020.
Digital Object Identifier: 10.1002/adts.202000095 [CrossRef]
 An Efficient and High-Speed Disturbance Detection Algorithm Design with Emphasis on Operation of Static Transfer Switch, USMAN, A., CHOUDHRY, M. A., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 2, Volume 21, 2021.
Digital Object Identifier: 10.4316/AECE.2021.02010 [CrossRef] [Full text]
 Three-Layer Bayesian Network for Classification of Complex Power Quality Disturbances, Luo, Yi, Li, Kaicheng, Li, Yuanzheng, Cai, Delong, Zhao, Chen, Meng, Qingxu, IEEE Transactions on Industrial Informatics, ISSN 1551-3203, Issue 9, Volume 14, 2018.
Digital Object Identifier: 10.1109/TII.2017.2785321 [CrossRef]
 Controllable AC/DC Integration for Power Quality Improvement in Microgrids, KARABIBER, A., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 2, Volume 19, 2019.
Digital Object Identifier: 10.4316/AECE.2019.02013 [CrossRef] [Full text]
 A precision detection technique for power disturbance in electrical system, Usman, Adil, Choudhry, Mohammad Ahmad, Electrical Engineering, ISSN 0948-7921, 2021.
Digital Object Identifier: 10.1007/s00202-021-01343-0 [CrossRef]
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Stefan cel Mare University of Suceava, Romania
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