Feature Extraction and Classification Techniques for Power Quality Disturbances in Distributed Generation: A Review

References

• H. J. Bollen, and I. Gu. Signal processing of power quality disturbances. Hoboken, NJ: Wiley-IEEE Press, 2006.
   Google Scholar
• K. H. LaCommare, and J. H. Eto. Understanding the cost of power interruptions to U.S. electricity consumers. Berkeley: Consortium for Electric Reliability Technology Solutions, August 2004.
   Google Scholar
• F. C. Pereira, O. C. N. Souto, J. C. De Oliveira, A. L. A. Vilaca, and P. F. Ribeiro, “An analysis of costs related to the loss of power quality,” Proceedings of the International Conference Harmonics and Quality of Power, Vol. 2, no. 1, pp. 777–82, Oct. 1998.
   Google Scholar
• Y. Liao, “Automated analysis of power quality data and Transmission Line Fault location,” Dissertation of Texas A&M University, College Station, TX, 2000.
   Google Scholar
• R.-C. Leou, W.-R. Tsai, and Y.-N. Chang, “A power quality monitoring system based on J2EE architecture,” in TENCON 2004, IEEE Region 10 Conference, Melbourne, Australia, Nov. 2004, pp. 13–7.
   Google Scholar
• D. Piombo, and R. Zunino, “FPGA realization of power quality disturbance detection: an approach with wavelet, ANN and fuzzy logic,” in IJCNN ‘05 – IEEE International Joint Conference on Neural Networks, 30 July–4 Aug, 2005, pp. 132–8.
   Google Scholar
• F. Choon, M. B. I. Reaz, and F. Mohd-Yasin, “Power quality disturbance detection using artificial intelligence: A hardware approach,” in 19th IEEE International Parallel and Distributed Processing Symposium, Apr 04–08, 2005, pp. 146a–9a.
   Google Scholar
• S. Pittner, and S. V. Kamarthi, “Feature extraction from wavelet coefficients for pattern recognition tasks,” IEEE Trans. Pattern Anal. Mach., Vol. 21, no. 1, pp. 83–8, Jan. 1999.
   Web of Science ®Google Scholar
• E. W. Gunther, and H. Mehta, “A survey of distribution system power quality – preliminary results,” IEEE Trans. Power Deliv., Vol. 10, no. 1, pp. 322–9, Jan. 1995.
   Web of Science ®Google Scholar
• M. B. Hughes, J. S. Chan, and D. O. Koval, “Distribution customer power quality experience,” IEEE Trans. Ind. Appl., Vol. 29, no. 6, pp. 1204–11. Nov. 1993.
   Web of Science ®Google Scholar
• 1159–1995, IEEE recommended practice for monitoring electric power quality. New York: IEEE Inc., 1995.
   Google Scholar
• J. J. Burke, D. C. Grifith, and J. Ward, “Power quality—Two different perspectives,” IEEE Trans. Power Deliv., Vol. 5, no. 3, pp. 1501–13, Jun. 1990.
   Web of Science ®Google Scholar
• G. Beylkin, R. Coifman, I. Daubechies, S. G. Mallat, Y. Meyer, L. Raphael, and M. B. Ruskai. Introduction to wavelets. Boston, MA: Jones and Bartlett, 1991.
   Google Scholar
• S. G. Mallat, “Multiresolution approximations and wavelet orthonormal bases,” Trans. Am. Math. Soc., Vol. 315, no. 1, pp. 69–87, 1989.
   Web of Science ®Google Scholar
• F. Xie, M. Huang, W. Zhang, and J. Li, “Research on electric vehicle charging station load forecasting,” in International Conference on Advanced Power System Automation and Protection (APAP), 2011, pp. 2055–60.
   Google Scholar
• B. Deng, and Z. Wang, “Research on electric-vehicle charging station technologies based on smart grid,” in Power Energy Engineering Conference (APPEEC), 2011, pp. 1–4.
   Google Scholar
• M. Farhoodnea, A. Mohamed, H. Shareef, H. Zayandehroodi, “Power quality impacts of high-penetration electric vehicle stations and renewable energy-based generators on power distribution systems,” Measurement, Vol. 46, no. 8, pp. 2423–34, Oct. 2013.
   Web of Science ®Google Scholar
• C. Kong, R. Jovanovic, I. S. Bayram, and M. Devetsikiotis, “A hierarchical optimization model for a network of electric vehicle charging stations,” Energies, Vol. 10, no. 5, pp. 673–5, 2017.
   Web of Science ®Google Scholar
• S. Habib, and M. Kamran, “A novel vehicle-to-grid technology with constraint analysis – a review,” in Proceedings of the International Conference on Emerging Technologies (ICET), Islamabad, Pakistan, Dec. 2014, pp. 69–74.
   Google Scholar
• M. P. Collins, W. G. Hurley, and E. Jones, “The application of wavelet theory in an expert system for power quality diagnostics,” in 30th Universal Power Engineering Conference, 1995.
   Google Scholar
• O. Poisson, P. Rioual, and M. Meunier, “New signal processing tools applied to power quality analysis,” IEEE Trans. Power Deliv., Vol. 14, no. 2, pp. 324–7, Jul. 1999.
   Web of Science ®Google Scholar
• O. Poisson, P. Rioual, and M. Meunier, “Detection and measurement of power quality disturbances using wavelet transform,” IEEE Trans. Power Deliv., Vol. 15, no. 3, pp. 214–9, Jul. 2000.
   Web of Science ®Google Scholar
• K. Srinivasan, and A. St.-Jacques, “A new fault location technique for two-And three-terminal lines,” IEEE Trans. Power Deliver., Vol. 7, no. 1, pp. 98–107, Jan. 1992.
   Web of Science ®Google Scholar
• M. S. Sachdev, and R. Agarwal, “A technique for estimating transmission line fault locations from digital impedance relay measurements,” IEEE Trans. Power Deliver., Vol. 3, no. 1, pp. 121–9, Jan. 1988.
   Web of Science ®Google Scholar
• C. Christopoulos, D. W. P. Thomas, and A. Wright, “Scheme based on traveling waves for the protection of major transmission lines,” Proc. Inst. Elect. Eng., Gen. Transm. Distrib., Vol. 135, no. 1, pp. 63–73, Jan. 1988.
   Google Scholar
• M. Caciotta, S. Giannetti, F. Leccese, and Z. Leonowicz. Comparison between DFT, adaptive window DFT And EDFT for power quality frequency spectrum analysis. Wroclaw, Poland: Modern Electric Power Systems, 2010.
   Google Scholar
• K. Won-Ki, L. Jae-Won, and K. Chul-Hwan, “Fault type classification in transmission line using STFT,” Dev. Power Syst. Prot., Vol. 15, no. 1, pp. 1–5, 2012.
   Google Scholar
• M. Srividya T, A. Muni Sankar, and Dr. T. Devaraju, “Identifying, classifying power quality disturbances using short time fourier transform & S-transform,” Wkly Sci., Vol. 1, no. 1, pp. 18–9, Jul. 2013. ISSN: 2321-7871.
   Google Scholar
• D. Luo, and H. Sun, “Research and application of fundamental wave extraction in electric power system based on wavelet analysis,” Adv. Inf. Sci. Serv. Sci., Vol. 4, no. 9, pp. 69–76, May 2012.
   PubMedGoogle Scholar
• R. M. de Castro Fernandez, and H. N. D. Rojas. “An overview of wavelet transform applications in power systems”, 14th PSCC, Sevilla, Session 01, Paper 6, Page 1, 2012.
   Google Scholar
• M. FunshoAkorede, and H. Hizam, “Wavelet transforms: practical applications in power systems,” J. Electr. Eng. Technol., Vol. 4, no. 2, pp. 168–74, 2009.
   Web of Science ®Google Scholar
• P. Das, “Power system transient using wavelet transform: a case study,” J. Electr. Eng., Vol. 4, no. 2, pp. 202–6, 2013.
   Google Scholar
• A. M. Gargoom, N. Ertugrul, and W. L. Soong, “A comparative study on effective signal processing tools for optimum feature selection in automatic power quality events clustering,” in 40th IAS Annual Industrial Applied Conference, 1:52–8 2005.
   Google Scholar
• S. Shukla, S. Mishra, and B. Singh, “Empirical-mode decomposition with hilbert transform for power-quality assessment,” IEEE Trans. Power Deliv., Vol. 24, no. 4, pp. 2159–65, 2009.
   Web of Science ®Google Scholar
• S. Suja, and J. Jerome, “Pattern recognition of power signal disturbances using s- transform and tt-transform,” Int. J. Electr. Power Energy Syst., Vol. 32, no. 1, pp. 37–53, 2010.
   Web of Science ®Google Scholar
• J. A. R. Macias, and A. G. Exposito, “Self-tunning of kalman filters for harmonic computation,” IEEE Trans. Power Deliv., Vol. 21, no. 1, pp. 501–3, 2006.
   Web of Science ®Google Scholar
• E. Perez, and J. Barros, “An extended kalman filtering approach for detection and analysis of voltage dips in power systems,” Electr. Power Syst. Res., Vol. 78, no. 4, pp. 618–25, 2008.
   Web of Science ®Google Scholar
• E. Perez, and J. Barros, “Voltage event detection and characterization methods: a comparative study,” IEEE PES Transmission & Distribution Conference and Exposition: Latin America, 1–6, 2006.
   Google Scholar
• A. Khosravi, J. Melendez, and J. Colomer, “Classification of sags gathered in distribution substations based on multiway principal component analysis,” Electr. Power Syst. Res., Vol. 79, no. 1, pp. 144–51, 2009.
   Web of Science ®Google Scholar
• S. R. Samantaray, “Decision tree-initialised fuzzy rule-based approach for power quality events classification,” IET Gener. Transm. Distrib., Vol. 4, no. 4, pp. 538–51, 2010.
   Web of Science ®Google Scholar
• S. K. Meher, and A. K. Pradhan, “Fuzzy classifiers for power quality events analysis,” Electr. Power Syst. Res., Vol. 80, no. 1, pp. 71–6, 2010.
   Web of Science ®Google Scholar
• A. M. Al-Kandari, S. A. Soliman, and R. A. Alammari, “Power quality analysis based on fuzzy estimation algorithm: voltage flicker measurements,” Int. J. Electr. Power Energy Syst., Vol. 28, no. 10, pp. 723–8, 2006.
   Web of Science ®Google Scholar
• M. V. Chilukuri, and P. K. Dash, “Multiresolution s-transform-based fuzzy recognition system for power quality events,” IEEE Trans. Power Deliv., Vol. 19, no. 1, pp. 323–30, 2004.
   Web of Science ®Google Scholar
• T. X. Zhu, S. K. Tso, and K. L. Lo, “Wavelet-based fuzzy reasoning approach to power quality disturbance recognition,” IEEE Trans. Power Deliv., Vol. 19, no. 4, pp. 1928–35, 2004.
   Web of Science ®Google Scholar
• Y. Liao, and J.-B. Lee, “A fuzzy expert system for classifying power quality disturbances,” Int. J. Electr. Power Energy Syst., Vol. 26, no. 3, pp. 199–205, 2004.
   Web of Science ®Google Scholar
• M. Kezunovic, and Y. Liao, “A novel software implementation concept for power quality study,” IEEE Trans. Power Deliv., Vol. 17, no. 2, pp. 544–9, 2002.
   Web of Science ®Google Scholar
• H. S. Behera, P. K. Dash, and B. Biswal, “Power quality time series data mining using s- transform and fuzzy expert system,” Appl. Soft. Comput., Vol. 10, no. 3, pp. 945–55, 2010.
   Web of Science ®Google Scholar
• M. B. I. Reaz, F. Choong, M. S. Sulaiman, F. Mohd-Yasin, and M. Kamada, “Expert system for power quality disturbance classifier,” IEEE Trans. Power Deliv., Vol. 22, no. 3, pp. 1979–88, 2007.
   Web of Science ®Google Scholar
• I. Monedero, C. Leon, J. Ropero, A. Garcia, J. M. Elena, and J. C. Montano, “Classification of electrical disturbances in real-time using neural networks,” IEEE Trans. Power Deliv., Vol. 22, no. 3, pp. 1288–96, 2007.
   Web of Science ®Google Scholar
• L. Xu, and M.-Y. Chow, “A classification approach for power distribution systems fault cause identification,” IEEE Trans. Power Syst., Vol. 21, no. 1, pp. 53–60, 2006.
   Web of Science ®Google Scholar
• D. Borras, M. Castilla, N. Moreno, and J. C. Montano, “Wavelet and neural structure: a new tool for diagnostic of power system disturbances,” IEEE Trans. Ind. Appl., Vol. 37, no. 1, pp. 184–90, 2001.
   Web of Science ®Google Scholar
• A. M. Gaouda, S. H. Kanoun, M. M. A. Salama, and A. Y. Chikhani, “Pattern recognition applications for power system disturbance classification,” IEEE Trans. Power Deliv., Vol. 17, no. 3, pp. 677–82, 2002.
   Web of Science ®Google Scholar
• Z. He, S. Gao, X. Chen, J. Zhang, Z. Bo, and Q. Qian, “Study of a new method for power system transients classification based on wavelet entropy and neural network,” Int. J. Electr. Power Energy Syst., Vol. 33, no. 3, pp. 402–10, 2011.
   Web of Science ®Google Scholar
• S. Santoso, E. J. Powers, W. M. Grady, and A. C. Parsons, “Power quality disturbance waveform recognition using wavelet-based neural classifier -part 1: theoretical foundation,” IEEE Trans. Power Deliv., Vol. 15, no. 1, pp. 222–8, 2000.
   Web of Science ®Google Scholar
• N. Huang, G. Lu, G. Cai, D. Xu, J. Xu, F. Li, and L. Zhang, “Feature selection of power quality disturbance signals with an entropy-importance-based random forest,” Entropy, Vol. 18, no. 44, pp. 1900–6, 2016.
   Google Scholar
• S. Santoso, E. J. Powers, W. M. Grady, and A. C. Parsons, “Power quality disturbance waveform recognition using wavelet-based neural classifier-part 2: application,” IEEE Trans. Power Deliv., Vol. 15, no. 1, pp. 229–35, 2000.
   Web of Science ®Google Scholar
• L. Deng, D. Yu, “Deep learning: methods and applications,” Found. Trends Sig. Process., Vol. 7, no. 3–4, pp. 197–387, 2014.
   Google Scholar
• W. L. Rodrigues Jr, F. A. S. Borges, A. O. de Carvalho Filho, and R. de AL Rabelo, “A deep learning approach for the detection and classifcation of power quality disturbances with windowed signals,” SN Comput. Sci., Vol. 2, no. 2, pp. 1–4, Apr. 2021.
   Google Scholar
• S. K. Meher, A. K. Pradhan, and G. Panda, “An integrated data compression scheme for power quality events using spline wavelet and neural network,” Electr. Power Syst. Res., Vol. 69, no. 2–3, pp. 213–20, 2004.
   Web of Science ®Google Scholar
• H. He, and J. A. Starzyk, “A self-organizing learning array system for power quality classification based on wavelet transform,” IEEE Trans. Power Deliv., Vol. 21, no. 1, pp. 286–9, 2006.
   Web of Science ®Google Scholar
• S. Yan, S. C. Tan, C. K. Lee, B. Chaudhuri, and S. Y. R. Hui, “Use of smart loads for power quality improvement,” IEEE J. Emerg. Sel. Top. Power Electron., Vol. 5, pp. 504–12, 2017.
   Web of Science ®Google Scholar
• Y. Naderi, S. H. Hosseini, S. G. Zadeh, B. Mohammadi-Ivatloo, J. C. Vasquez, J. M. Guerrero, “An overview of power quality enhancement techniques applied to distributed generation in electrical distribution networks,” Renew. Sustain. Energy Rev., Vol. 93, pp. 201–14, 2018.
   Web of Science ®Google Scholar