3/2017 - 8 |
Research and Implementation of a USB Interfaced Real-Time Power Quality Disturbance Classification SystemGOK, M. , SEFA, I. |
Extra paper information in |
Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science |
Download PDF (1,660 KB) | Citation | Downloads: 1,053 | Views: 2,642 |
Author keywords
discrete transforms, graphical user interfaces, neural networks, power quality, real-time system
References keywords
power(46), quality(29), transform(19), system(17), classification(17), disturbances(16), systems(10), detection(10), time(9), real(8)
Blue keywords are present in both the references section and the paper title.
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
Abstract
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. |
References | | | Cited By «-- Click to see who has cited this paper |
[1] S. Roy and S. Nath, "Classification of power quality disturbances using features of signals", International Journal of Scientific and Research Publications, vol. 2, no. 11, pp. 1-9, Nov. 2012.
[2] A. Rodriguez, J. Aguado, J. J. Lopez, F. I. Martin, F. J. Munoz and J. E. Ruiz, Time-frequency transforms for classification of power quality disturbances, Power Quality, Intech, 2011, ch. 15. [CrossRef] [3] Y. Kong, J. Yuan, J. An and L. Che, "Online power quality disturbances detection and classification using one-pass Wavelet decomposition and Decision tree (Published Conference Proceedings style)" in Proceeding of Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, 2007, pp. 2990-2995. [CrossRef] [SCOPUS Times Cited 6] [4] S. Wei, H. Wen-fang, Y. Gui and D. Li-Fang, "Classification for power quality disturbances based on Cubic B-spline Wavelet and Decision tree", in International Conference on Computer Science and Software Engineering, Washington, 2008, pp. 823-826. [CrossRef] [SCOPUS Times Cited 2] [5] N. C. F. Tse, "Practical application of wavelet to power quality analysis (Published Conference Proceedings style)" in Proc. IEEE PES Gen. Meeting, Montreal, 2006, pp. 1-5. [CrossRef] [SCOPUS Times Cited 31] [6] J. Xu, S. Song and S. Shao, "Application in harmonics signal detection based on STFT and S Transform", Journal of Information and Computational Science, vol. 12, no. 9, pp. 3655-3664, Jun. 2015. [CrossRef] [SCOPUS Times Cited 1] [7] M. Uyar, S. Yildirim and M. T. Gencoglu, "An expert system based on S-transform and neural network for automatic classification of power quality disturbances", Expert Systems with Applications, vol. 36, no. 3, pp. 5962-5975, Apr. 2009. [CrossRef] [Web of Science Times Cited 145] [SCOPUS Times Cited 184] [8] C. N. Bhende, S. Mishra and B. K. Panigrahi, "Detection and classification of power quality disturbances using S-Transform and Modular neural network", Electric Power Systems Research, vol. 78, no. 1, pp. 122-128, Jan. 2008. [CrossRef] [Web of Science Times Cited 121] [SCOPUS Times Cited 157] [9] S. Kaewarsa, "Classification of power quality disturbance using S-Transform based artificial neural networks", in IEEE International Conference on Intelligent Computing and Intelligent Systems, Shanghai, 2009, pp. 566-570. [CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 12] [10] M. Biswal and P. K. Dash, "Measurement and classification of simultaneous power signal patterns with an S-transform variant and fuzzy decision tree", IEEE Transactions on Industrial Informatics, vol. 9, no. 4, pp. 1819-1827, Nov. 2013. [CrossRef] [Web of Science Times Cited 124] [SCOPUS Times Cited 145] [11] A. Rodriguez, E. Merino, J. Aguado, J. J. Lopez, F. Munoz, F. I. Martin and J. Munoz, "A Decision tree and S-Transform based approach for power quality disturbances classification", In Fourth International Conference on Power Engineering, Energy and Electrical Drives (POWERENG), Istanbul, 2013, pp. 1093-1097. [CrossRef] [SCOPUS Times Cited 6] [12] M. E. Salem, A. Mohamed and S. A. Samad, "Rule based system for power quality disturbance classification incorporating S-transform features", Expert Systems with Applications, vol. 37, no. 4, pp. 3229-3235, Apr. 2010. [CrossRef] [Web of Science Times Cited 25] [SCOPUS Times Cited 38] [13] X. Xiao, F. Xu and H. Yang, "Short duration disturbance classifying based on S-transform maximum similarity", Electrical Power and Energy Systems, vol. 31, no. 7-8, pp. 374-378, Sept. 2009. [CrossRef] [Web of Science Times Cited 26] [SCOPUS Times Cited 46] [14] M. J. B. Reddy, K. Sagar and D. K. Mohanta, "A multifunctional real-time power quality monitoring system using Stockwell transform", IET Science, Measurement and Technology, vol. 8, no. 4, pp. 155-169, Jul. 2014. [CrossRef] [Web of Science Times Cited 20] [SCOPUS Times Cited 26] [15] M. Biswal and P. K. Dash, "Detection and characterization of multiple power quality disturbances with a fast S-transform and decision tree based classifier", Digital Signal Processing, vol. 23, no. 4, pp. 1071-1083, Jul. 2013. [CrossRef] [Web of Science Times Cited 117] [SCOPUS Times Cited 161] [16] T. K. A. Galil, M. Kamel, A. M. Youssef, E. F. E. Saadany and M. M. A. Salama, "Power quality disturbance classification using the inductive inference approach", IEEE Transactions on Power Delivery, vol. 19 no. 10, pp. 1812-1818, Oct. 2004. [CrossRef] [Web of Science Times Cited 121] [SCOPUS Times Cited 156] [17] F. Zhao and R. Yang, "Power-Quality disturbance recognition using S-Transform", IEEE Transactions on Power Delivery, vol. 22, no. 2, pp. 944-950, Apr. 2007. [CrossRef] [Web of Science Times Cited 148] [SCOPUS Times Cited 218] [18] J. Li and M. V. Chilukuri, "Power supply quality analysis using S-Transform and SVM classifier", Journal of Power and Energy Engineering, vol. 2, no. 4, pp. 438-447, Apr. 2014. [CrossRef] [19] A. Wang, F. Pan, Y. Li and R. Tao, "The design of power quality detecting system based of OMAP-L138", in IEEE 13th Workshop on Control and Modeling for Power Electronics, 2012, Kyoto, pp. 1-4. [CrossRef] [SCOPUS Times Cited 4] [20] A. Rodriguez, J. A. Aguado, F. Martin, J. J. Lopez, F. Munoz and J. E. Ruiz, "Rule-based classification of power quality disturbances using S-transform", Electric Power Systems Research, vol. 86, pp. 113-121, May 2012. [CrossRef] [Web of Science Times Cited 79] [SCOPUS Times Cited 99] [21] Y. Chen, "Research and design of intelligent electric power quality detection system based on VI", Journal of Computers, vol. 5, no. 1, pp. 158-165, Jan. 2010. [CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 3] [22] T. Radil, P. M. Ramos, F. M. Janeiro and A. C. Serra, "PQ monitoring system for real-time detection and classification of disturbances in a single-phase power system", IEEE Transactions on Instrumentation and Measurement, vol. 57, no. 8, pp. 1725-1733, Aug. 2008. [CrossRef] [Web of Science Times Cited 87] [SCOPUS Times Cited 112] [23] R. Kumar, B. Singh, D. T. Shahani, A. Chandra and K. Al-Haddad, "Recognition of power quality disturbances using S-Transform-Based ANN classifier and rule-based Decision tree", IEEE Transactions on Industry Applications, vol. 51 no. 2, pp. 1249-1258, Mar. 2015. [CrossRef] [Web of Science Times Cited 191] [SCOPUS Times Cited 246] [24] A. Miron, M. Dorin and A. C. Cziker, "Software tool for real-time power quality analysis", Advances in Electrical and Computer Engineering", vol. 13, no. 4, pp. 125-132, Nov. 2013. [CrossRef] [Full Text] [Web of Science Times Cited 5] [SCOPUS Times Cited 5] [25] M. Zhang and K. Li, "DSP-FPGA based real-time power quality disturbances classifier", in IEEE PES Transmission and Distribution Conference and Exposition, 2010, New Orleans, pp. 1-6. [CrossRef] [SCOPUS Times Cited 4] [26] X. She and J. Xiong, "Multi-channel electrical power data acquisition system Based on AD7606 and NIOSII", in International Conference on Electrical and Control Engineering, Yichang, 2011, pp. 1625-1627. [CrossRef] [SCOPUS Times Cited 7] [27] M. Wang, G. I. Rowe and A. V. Mamishev, "Real-Time power quality waveform recognition with a programmable digital processor", in IEEE Power Engineering Society General Meeting, 2003, Toronto, pp. 1268-1273. [CrossRef] [Web of Science Times Cited 1] [28] Z. He and Y. Liao, "The design of analog acquisition system in distribution automation", in 2012 China International Conference on Electricity Distribution (CICED), 2012, Shanghai, pp. 1-4. [CrossRef] [SCOPUS Times Cited 4] [29] M. Gök, S. Görgünoglu and I. Sefa, "Design of a real-time USB interfaced multi-channel power system harmonics detection system", in 9th International Conference on Electrical and Electronics Engineering (ELECO), Bursa, 2015, pp. 521-524. [CrossRef] [SCOPUS Times Cited 3] [30] H. Guo, H. Yu, C. Sun, Z. Zhang and E. Zheng, "Continuous and real-time vibration data acquisition and analysis system based on S3C6410 and Linux", in Fifth Conference on Measuring Technology and Mechanism Automation, 2013, Hong Kong, pp. 389-392. [CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 8] [31] S. Shujing and L. Jiansheng, "A method of multi-channel data acquisition with adjustable sampling rate", Telkomnika, vol. 11, no. 9, pp. 5299-537, Sept. 2013. [32] M. Caciotta, S. Giarnetti, F. Leccese, and D. Trinca, "Development of an USB data acquisition system for power quality and smart metering applications", in Proc. 11th International Conference on Environment and Electrical Engineering, Rome, 2012, pp. 835-839. [CrossRef] [SCOPUS Times Cited 8] [33] R. G. Stockwell, L. Mansinha and R. P. Lowe, "Localization of the complex spectrum: The S Transform", IEEE Transactions on Signal Processing, vol. 44, no. 4, pp. 998-1001, Apr. 1996. [CrossRef] [Web of Science Times Cited 2435] [SCOPUS Times Cited 57] [34] S. He, K. Li and M. Zhang, "A real-time power quality disturbances classification using hybrid method based on S-Transform and dynamics", IEEE Transactions on Instrumentation and Measurement, vol. 62, no. 9, pp. 2465-2475, Sept. 2013. [CrossRef] [Web of Science Times Cited 118] [SCOPUS Times Cited 148] [35] R. Hooshmand and A. Enshaee, "Detection and classification of single and combined power quality disturbances using fuzzy systems oriented by particle swarm optimization algorithm", Electric Power Systems Research, vol. 80, no. 12, pp. 1552-1561, Jul. 2010. [CrossRef] [Web of Science Times Cited 90] [SCOPUS Times Cited 119] [36] B. Widrow, D. E. Rumelhard and M. A. Lehr, "Neural networks: Applications in industry, business and science", Communication of ACM, vol. 37, no. 3, pp. 93-105, Mar. 1994. [CrossRef] [Web of Science Times Cited 268] [SCOPUS Times Cited 387] [37] D. Borras, M. Castilla, N. Moreno and J. C. Montano, "Wavelet and neural structure: a new tool for diagnostic of power system disturbances", IEEE Transactions on Industry Applications, vol. 37, no. 1, pp. 184-190, Jan. 2001. [CrossRef] [Web of Science Times Cited 80] [SCOPUS Times Cited 110] [38] S. Mishra, C. N. Bhende and B. K. Panigrahi, "Detection and classification of power quality disturbances using S-Transform and probabilistic neural network", IEEE Transactions on Power Delivery, vol. 23, no. 1, pp. 280-287, Jan. 2008. [CrossRef] [Web of Science Times Cited 299] [SCOPUS Times Cited 470] [39] I. W. C. Lee and P. K. Dash, "S-Transform based intelligent system for classification of power quality disturbance signals", IEEE Transactions on Industrial Electronics, vol. 50, no. 4, pp. 800-805, Aug. 2003. [CrossRef] [Web of Science Times Cited 140] [SCOPUS Times Cited 220] [40] M. V. Chilukuri and P. K. Dash, "Multiresolution S-Transform based fuzzy recognition system for power quality events", IEEE Transactions on Power Delivery, vol. 19, no. 1, pp. 323-330, Jan. 2004. [CrossRef] [Web of Science Times Cited 192] [SCOPUS Times Cited 271] [41] I. krjanc, S. Blaic and D. Matko, "Direct fuzzy model-reference adaptive control", International Journal of Intelligent Systems, vol. 17, no. 10, pp. 943-963, Oct. 2002. [CrossRef] [Web of Science Times Cited 52] [SCOPUS Times Cited 63] [42] R. Precup and S. Preitl, "PI-fuzzy controllers for integral plants to ensure robust stability", Information Sciences, vol. 177, pp. 4410-4429, May 2007. [CrossRef] [Web of Science Times Cited 67] [SCOPUS Times Cited 87] [43] P. Moallem, B. S. Mousavi and S. Sh. Naghibzadeh, "Fuzzy inference system optimized by genetic algorithm for robust face and pose detection", International Journal of Artificial Intelligence, vol. 13, no. 2, pp. 73-88, Oct. 2015. [44] J. Nowaková, M. Prilepok and V. Snásel, "Medical image retrieval using vector quantization and fuzzy S-tree", Journal of Medical Systems, vol. 41, no. 18, pp. 1-16, Feb. 2017. [CrossRef] [Web of Science Times Cited 66] [SCOPUS Times Cited 76] [45] M. Frigo and S. G. Johnson, "FFTW: An adaptive software architecture for the FFT", in IEEE International Conference on Acoustics, Speech and Signal Processing, Seattle, 1998, pp. 1381-1384. Web of Science® Citations for all references: 5,033 TCR SCOPUS® Citations for all references: 3,700 TCR Web of Science® Average Citations per reference: 109 ACR SCOPUS® Average Citations per reference: 80 ACR TCR = Total Citations for References / ACR = Average Citations per Reference We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more Citations for references updated on 2024-12-18 07:36 in 276 seconds. Note1: Web of Science® is a registered trademark of Clarivate Analytics. Note2: SCOPUS® is a registered trademark of Elsevier B.V. Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site. |
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.