All Title Author
Keywords Abstract


A Three Decades of Marvellous Significant Review of Power Quality Events Regarding Detection & Classification

DOI: 10.4236/jpee.2018.68001, PP. 1-37

Keywords: Power Quality, Feature Extraction, Power Quality Disturbances, Power Quality Events Classifier

Full-Text   Cite this paper   Add to My Lib

Abstract:

Around the globe, the necessity of green supply with a dedicated standard quality thrust of consumers is increasing day by day. The advancement in technology urges the electrical power system to deliver a high-quality rated undistorted sinusoidal current, the voltage at a constant desired standard frequency to its consumers. The present paper reveals a complete and inclusive study of power quality events, such as automatic classification and signal processing via creative techniques and the noises effect on the detection and classification of power quality disturbances. It’s planned to make a possible list for quick reference to obtain an extensive variety on the condition & status of available research for detection and classification for young engineers, designers and researchers who enter in the power quality field. The current extensive study is supported by a critical review of more than 200 publications on detection and classification techniques of power quality disturbances.

References

[1]  Thapar, A., Saha, T.K. and Dong, Z.Y. (2004) Investigation of Power Quality Categorization and Simulating Its Impact on Sensitive Electronic Equipment. IEEE Power Engineering Society General Meeting, Denver, 6-10 June 2004, 528-533.
[2]  Smith, J.C., Hensley, G. and Ray, L. (1995) 1159-1995-IEEE Recommended Practice for Monitoring Electric Power Quality. IEEE, New York.
[3]  Montoya, F.G., García-Cruz, A., Montoya, M.G. and Manzano-Agugliaro, F. (2016) Power Quality Techniques Research Worldwide: A Review. Renewable and Sustainable Energy Reviews, 54, 846-856.
https://doi.org/10.1016/j.rser.2015.10.091
[4]  Liang, X. (2017) Emerging Power Quality Challenges Due to the Integration of Renewable Energy Sources. IEEE Transactions on Industry Applications, 53, 855-866.
https://doi.org/10.1109/TIA.2016.2626253
[5]  Dugan, R.C., McGranaghan, M.F. and Beaty, H.W. (1996) Electrical Power Systems Quality. McGraw-Hill, New York.
[6]  Flores, R.A. (2002) State of the Art in the Classification of Power Quality Events, an Overview. 10th International Conference on Harmonics and Quality of Power, Rio de Janeiro, 6-9 October 2002, 17-20.
https://doi.org/10.1109/ICHQP.2002.1221398
[7]  Brenna, M., Faranda, R. and Tironi, E. (2009) A New Proposal for Power Quality and Custom Power Improvement: OPEN UPQC. IEEE Transactions on Power Delivery, 24, 2107-2116.
https://doi.org/10.1109/TPWRD.2009.2028791
[8]  Morales-Velazquez, L., de Jesus Romero-Troncoso, R., Herrera-Ruiz, G., Morinigo-Sotelo, D. and Osornio-Rios, R.A. (2017) Smart Sensor Network for Power Quality Monitoring in Electrical Installations. Measurement, 103, 133-142.
https://doi.org/10.1016/j.measurement.2017.02.032
[9]  Teke, A., Saribulut, L. and Tumay, M. (2011) A Novel Reference Signal Generation Method for Power-Quality Improvement of Unified Power-Quality Conditioner. IEEE Transactions on Power Delivery, 26, 2205-2214.
https://doi.org/10.1109/TPWRD.2011.2141154
[10]  Sharon, D., Montano, J.C., Lopez, A., Castilla, M., Borras, D. and Gutiérrez, J. (2008) Power Quality Factor for Networks Supplying Unbalanced Nonlinear Loads. IEEE Transactions on Instrumentation and Measurement, 57, 1268-1274.
https://doi.org/10.1109/TIM.2007.915146
[11]  Biswal, B., Dash, P.K. and Mishra, S. (2011) A Hybrid Ant Colony Optimization Technique for Power Signal Pattern Classification. Expert Systems with Applications, 38, 6368-6375.
https://doi.org/10.1016/j.eswa.2010.11.102
[12]  Kwan, K.H., So, P.L. and Chu, Y.C. (2012) An Output Regulation-Based Unified Power Quality Conditioner with Kalman Filters. IEEE Transactions on Industrial Electronics, 59, 4248-4262.
https://doi.org/10.1109/TIE.2012.2193852
[13]  Rajasekaran, J. and Sasiraja, R.M. (2012) An Output Regulation-Based Unified Power Quality Conditioner with Kalman Filter. International Journal of Engineering Trends and Technology, 1, 3012-3019.
[14]  Wang, X., Yong, J., Xu, W. and Freitas, W. (2011) Practical Power Quality Charts for Motor Starting Assessment. IEEE Transactions on Power Delivery, 26, 799-808.
https://doi.org/10.1109/TPWRD.2010.2096237
[15]  Zhang, M., Li, K. and Hu, Y. (2011) A Real-Time Classification Method of Power Quality Disturbances. Electric Power Systems Research, 81, 660-666.
https://doi.org/10.1016/j.epsr.2010.10.032
[16]  Ken, D. and Hedman, H. (2005) The Role of Distributed Generation in Power Quality and Reliability: Final Report. Energy and Environmental Analysis.
[17]  Kezunovic, M. and Liao, Y. (2002) A Novel Software Implementation Concept for Power Quality Study. IEEE Transactions on Power Delivery, 17, 544-549.
https://doi.org/10.1109/61.997935
[18]  Dugan, R.C. and McGranaghan, M.F. (2002) Electrical Power Systems Quality. 2nd Edition, McGraw-Hill, New York.
[19]  Monedero, I., Leon, C., Ropero, J., Garcia, A., Elena, J.M. and Montano, J.C. (2007) Classification of Electrical Disturbances in Real Time Using Neural Networks. IEEE Transactions on Power Delivery, 22, 1288-1296.
https://doi.org/10.1109/TPWRD.2007.899522
[20]  Willems, J.L., Ghijselen, J.A. and Emanuel, A.E. (2005) The Apparent Power Concept and the IEEE Standard 1459-2000. IEEE Transactions on Power Delivery, 20, 876-884.
https://doi.org/10.1109/TPWRD.2005.844267
[21]  Filipski, P.S., Baghzouz, Y. and Cox, M.D. (1994) Discussion of Power Definitions Contained in the IEEE Dictionary. IEEE Transactions on Power Delivery, 9, 1237-1244.
https://doi.org/10.1109/61.311149
[22]  Alfonso-Gil, J.C., Orts-Grau, S., Munoz-Galeano, N., Gimeno-Sales, F.J. and Segui-Chilet, S. (2013) A Measurement System for a Power Quality Improvement Structure Based on IEEE Std. 1459. IEEE Transactions on Instrumentation and Measurement, 62, 3177-3188.
https://doi.org/10.1109/TIM.2013.2270901
[23]  IEEE (2010) 1459-2010-IEEE Standard Definitions for the Measurement of Electric Power Quantities under Sinusoidal, Non-Sinusoidal, Balanced, or Unbalanced Conditions. IEEE, New York.
[24]  Ferreira, S.C., Gonzatti, R.B., da Silva, C.H., Pereira, R.R., da Silva, L.E.B., Lambert-Torres, G. and Ahn, S.U. (2011) An Adaptive Algorithm for Real-Time Power Quality Measurement According to IEEE Std. 1459-2000. XI Brazilian Power Electronics Conference, Praiamar, 11-15 September 2011, 249-255.
https://doi.org/10.1109/COBEP.2011.6085257
[25]  Saxena, D., Verma, K. and Singh, S. (2010) Power Quality Event Classification: An Overview and Key Issues. International Journal of Engineering, Science, and Technology, 2, 186-199.
https://doi.org/10.4314/ijest.v2i3.59190
[26]  Barros, J., Diego, R.I. and De Apráiz, M. (2012) Applications of Wavelets in Electric Power Quality: Voltage Events. Electric Power Systems Research, 88, 130-136.
https://doi.org/10.1016/j.epsr.2012.02.009
[27]  Bollen, M.H. and Gu, I. (2006) Signal Processing of Power Quality Disturbances (Vol. 30). John Wiley & Sons, New York.
https://doi.org/10.1002/0471931314
[28]  Gunal, S., Gerek, O.N., Ece, D.G. and Edizkan, R. (2009) The Search for the Optimal Feature Set in Power Quality Event Classification. Expert Systems with Applications, 36, 10266-10273.
https://doi.org/10.1016/j.eswa.2009.01.051
[29]  Ribeiro, M.V., Romano, J.M. and Duque, C.A. (2004) An Improved Method for Signal Processing and Compression in Power Quality Evaluation. IEEE transactions on Power Delivery, 19, 464-471.
https://doi.org/10.1109/TPWRD.2003.822497
[30]  Sethi, S. and Upadhyay, R. (2017) Classification of Mental Tasks Using S-Transform Based Fractal Features. 2017 International Conference on Computer, Communications, and Electronics, Jaipur, 1-2 July 2017, 38-43.
https://doi.org/10.1109/COMPTELIX.2017.8003934
[31]  Ji, T.Y., Wu, Q.H., Jiang, L. and Tang, W.H. (2011) Disturbance Detection, Location, and Classification in Phase Space. IET Generation, Transmission & Distribution, 5, 257-265.
https://doi.org/10.1049/iet-gtd.2010.0254
[32]  Yang, L., Yu, J. and Lai, Y. (2010) Disturbance Source Identification of Voltage Sags Based on Hilbert-Huang Transform. 2010 Asia-Pacific Power and Energy Engineering Conference, Chengdu, 28-31 March 2010, 1-4.
https://doi.org/10.1109/APPEEC.2010.5448916
[33]  Rilling, G., Flandrin, P. and Goncalves, P. (2003) On Empirical Mode Decomposition and Its Algorithms. IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing, 3, 8-11.
[34]  Afroni, M.J., Sutanto, D. and Stirling, D. (2013) Analysis of Nonstationary Power-Quality Waveforms Using Iterative Hilbert Huang Transform and SAX Algorithm. IEEE Transactions on Power Delivery, 28, 2134-2144.
https://doi.org/10.1109/TPWRD.2013.2264948
[35]  Huang, N.E., et al. (1998) The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis. Proceedings of the Royal Society, 454, 903-995.
https://doi.org/10.1098/rspa.1998.0193
[36]  Shukla, S., Mishra, S. and Singh, B. (2009) Empirical-Mode Decomposition with Hilbert Transforms for Power-Quality Assessment. IEEE Transactions on Power Delivery, 24, 2159-2165.
https://doi.org/10.1109/TPWRD.2009.2028792
[37]  Hafiz, F., Chowdhury, A.H. and Shahnaz, C. (2012) An Approach for Classification of Power Quality Disturbances Based on Hilbert Huang Transform and Relevance Vector Machine. 2012 7th International Conference on Electrical & Computer Engineering, Dhaka, 20-22 December 2012, 201-204.
https://doi.org/10.1109/ICECE.2012.6471520
[38]  Wang, Z., Zeng, X.J., Hu, X.X. and Hu, J.Y. (2012) The Multi-Disturbance Complex Power Quality Signal HHT Detection Technique. IEEE PES Innovative Smart Grid Technologies, Tianjin, 21-24 May 2012, 1-5.
https://doi.org/10.1109/ISGT-Asia.2012.6303259
[39]  Önal, Y. and Turhal, ü.Ç. (2013) The Orthogonal Hilbert-Huang Transform Application in Voltage Flicker Analysis. 4th International Conference on Power Engineering, Energy and Electrical Drives, Istanbul, 13-17 May 2013, 700-704.
https://doi.org/10.1109/PowerEng.2013.6635695
[40]  Huang, Y., Liu, Y. and Hong, Z. (2009) Detection and Location of Power Quality Disturbances Based on Mathematical Morphology and Hilbert-Huang Transform. 2009 9th International Conference on Electronic Measurement & Instruments, Beijing, 16-19 August 2009, 2-319.
https://doi.org/10.1109/ICEMI.2009.5274596
[41]  Senroy, N., Suryanarayanan, S. and Ribeiro, P.F. (2007) An Improved Hilbert-Huang Method for Analysis of Time-Varying Waveforms in Power Quality. IEEE Transactions on Power Systems, 22, 1843-1850.
https://doi.org/10.1109/TPWRS.2007.907542
[42]  Jayasree, T., Devaraj, D. and Sukanesh, R. (2010) Power Quality Disturbance Classification Using Hilbert Transform and RBF Networks. Neurocomputing, 73, 1451-1456.
https://doi.org/10.1016/j.neucom.2009.11.008
[43]  Rodriguez, A., Ruiz, I.E., Aguado, J., Lopez, J.J., Martin, F.I. and Muñoz, F. (2011) Classification of Power Quality Disturbances Using S-Transform and Artificial Neural Networks. 2011 International Conference on Power Engineering, Energy and Electrical Drives, Malaga, 11-13 May 2011, 1-6.
https://doi.org/10.1109/PowerEng.2011.6036517
[44]  Dash, P.K., Panigrahi, B.K. and Panda, G. (2003) Power Quality Analysis Using S-Transform. IEEE Transactions on Power Delivery, 18, 406-411.
https://doi.org/10.1109/TPWRD.2003.809616
[45]  Bhuiyan, M.J.U., Begum, M.T.A. and Alam, M.R. (2017) S-Transform and Mahalanobis Distance-Based Approach for Classifying Power Quality Disturbances. International Conference on Electrical, Computer and Communication Engineering, Cox’s Bazar, 16-18 February 2017, 681-685.
[46]  Suja, S. and Jerome, J. (2010) Pattern Recognition of Power Signal Disturbances Using S Transform and TT Transform. International Journal of Electrical Power & Energy Systems, 32, 37-53.
https://doi.org/10.1016/j.ijepes.2009.06.012
[47]  Li, J., Teng, Z., Tang, Q. and Song, J. (2016) Detection and Classification of Power Quality Disturbances Using Double Resolution S-Transform and DAG-SVMs. IEEE Transactions on Instrumentation and Measurement, 65, 2302-2312.
https://doi.org/10.1109/TIM.2016.2578518
[48]  Zhao, F. and Yang, R. (2007) Power-Quality Disturbance Recognition Using S-Transform. IEEE Transactions on Power Delivery, 22, 944-950.
https://doi.org/10.1109/TPWRD.2006.881575
[49]  He, S., Li, K. and Zhang, M. (2013) A Real-Time Power Quality Disturbances Classification Using a Hybrid Method Based on S-Transform and Dynamics. IEEE Transactions on Instrumentation and Measurement, 62, 2465-2475.
https://doi.org/10.1109/TIM.2013.2258761
[50]  Uyar, M., Yildirim, S. and Gencoglu, M.T. (2009) An Expert System Based on S-Transform and Neural Network for Automatic Classification of Power Quality Disturbances. Expert Systems with Applications, 36, 5962-5975.
https://doi.org/10.1016/j.eswa.2008.07.030
[51]  Reddy, M.J.B., Raghupathy, R.K., Venkatesh, K.P. and Mohanta, D.K. (2013) Power Quality Analysis Using Discrete Orthogonal S-Transform (DOST). Digital Signal Processing, 23, 616-626.
https://doi.org/10.1016/j.dsp.2012.09.013
[52]  Chilukuri, M.V. and Dash, P.K. (2004) Multiresolution S-Transform-Based Fuzzy Recognition System for Power Quality Events. IEEE Transactions on Power Delivery, 19, 323-330.
https://doi.org/10.1109/TPWRD.2003.820180
[53]  Biswal, M. and Dash, P.K. (2013) Detection and Characterization of Multiple Power Quality Disturbances with a Fast S-Transform and Decision Tree Based Classifier. Digital Signal Processing, 23, 1071-1083.
https://doi.org/10.1016/j.dsp.2013.02.012
[54]  Mahela, O.P. and Shaik, A.G. (2016) Recognition of Power Quality Disturbances Using S-Transform and Fuzzy C-Means Clustering. 2016 International Conference on Cogeneration, Small Power Plants and District Energy, Bangkok, 14-16 September 2016, 1-6.
https://doi.org/10.1109/COGEN.2016.7728955
[55]  Kumar, R., Singh, B., Shahani, D.T., Chandra, A. and Al-Haddad, K. (2015) Recognition of Power-Quality Disturbances Using S-Transform-Based ANN Classifier and rule-Based Decision Tree. IEEE Transactions on Industry Applications, 51, 1249-1258.
https://doi.org/10.1109/TIA.2014.2356639
[56]  Mahela, O.P. and Shaik, A.G. (2016) Recognition of Power Quality Disturbances Using S-Transform and Rule-Based Decision Tree. 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems, Delhi, 4-6 Julyr 2016, 1-6.
https://doi.org/10.1109/ICPEICES.2016.7853093
[57]  Santoso, S., Powers, E.J., Grady, W.M. and Hofmann, P. (1996) Power Quality Assessment via Wavelet Transform Analysis. IEEE Transactions on Power Delivery, 11, 924-930.
https://doi.org/10.1109/61.489353
[58]  Santoso, S., Powers, E.J. and Grady, W.M. (1997) Power Quality Disturbance Data Compression Using Wavelet Transform Methods. IEEE Transactions on Power Delivery, 12, 1250-1257.
https://doi.org/10.1109/61.637001
[59]  Angrisani, L., Daponte, P., D’apuzzo, M. and Testa, A. (1998). A Measurement Method Based on the Wavelet Transform for Power Quality Analysis. IEEE Transactions on Power Delivery, 13, 990-998.
https://doi.org/10.1109/61.714415
[60]  Sharma, A.K., Mahela, O.P. and Ola, S.R. (2016) Detection of Power Quality Disturbances Using Discrete Wavelet Transform. 18th International Conference on Electrical Power and Energy Systems, Los Angeles, 5-6 April 2016, 450-455.
[61]  Gaouda, A.M., Salama, M.M.A., Sultan, M.R. and Chikhani, A.Y. (1999) Power Quality Detection and Classification Using Wavelet-Multiresolution Signal Decomposition. IEEE Transactions on Power Delivery, 14, 1469-1476.
https://doi.org/10.1109/61.796242
[62]  Chen, X.X. (2002) Wavelet-Based Detection, Localization, Quantification and Classification of Short Duration Power Quality Disturbances. 2002 IEEE Power Engineering Society Winter Meeting. Conference Proceedings, New York, 27-31 January 2002, 931-936.
[63]  Hamid, E.Y. and Kawasaki, Z.I. (2002) Wavelet-Based Data Compression of Power System Disturbances Using the Minimum Description Length Criterion. IEEE Transactions on Power Delivery, 17, 460-466.
https://doi.org/10.1109/61.997918
[64]  Lin, C.H. and Tsao, M.C. (2005) Power Quality Detection with Classification Enhancive Wavelet-Probabilistic Network in a Power System. IEE Proceedings-Generation, Transmission, and Distribution, 152, 969-976.
https://doi.org/10.1049/ip-gtd:20045177
[65]  He, H. and Starzyk, J.A. (2006) A Self-Organizing Learning Array System for Power Quality Classification Based on Wavelet Transform. IEEE Transactions on Power Delivery, 21, 286-295.
https://doi.org/10.1109/TPWRD.2005.852392
[66]  Morsi, W.G. and & El-Hawary, M.E. (2008) A New Perspective for the IEEE Standard 1459-2000 via Stationary Wavelet Transforms in the Presence of Nonstationary Power Quality Disturbance. IEEE Transactions on Power Delivery, 23, 2356-2365.
https://doi.org/10.1109/TPWRD.2008.2002660
[67]  Morsi, W.G. and El-Hawary, M.E. (2009) Wavelet Packet Transform-Based Power Quality Indices for Balanced and Unbalanced Three-Phase Systems under Stationary or Nonstationary Operating Conditions. IEEE Transactions on Power Delivery, 24, 2300-2310.
https://doi.org/10.1109/TPWRD.2009.2027496
[68]  Langella, R., Testa, A. and Et, A. (2014) 591-2014-IEEE Recommended Practice and Requirements for Harmonic Control in Electric Power Systems. IEEE, New York.
[69]  IEEE (2000) 1459-2000-IEEE Standard Definitions for the Measurement of Electric Quantities under Sinusoidal, Nonsinusoidal, Balanced, or Unbalanced Conditions. IEEE, New York.
[70]  Uyar, M., Yildirim, S. and Gencoglu, M.T. (2008) An Effective Wavelet-Based Feature Extraction Method for Classification of Power Quality Disturbance Signals. Electric Power Systems Research, 78, 1747-1755.
https://doi.org/10.1016/j.epsr.2008.03.002
[71]  Ray, P.K., Mohanty, S.R. and Kishor, N. (2011) Disturbance Detection in Grid-Connected Distributed Generation System Using Wavelet and S-Transform. Electric Power Systems Research, 81, 805-819.
https://doi.org/10.1016/j.epsr.2010.11.011
[72]  Zafar, T. and Morsi, W.G. (2013) Power Quality and the Un-Decimated Wavelet Transform: An Analytic Approach for Time-Varying Disturbances. Electric Power Systems Research, 96, 201-210.
https://doi.org/10.1016/j.epsr.2012.11.016
[73]  Khokhar, S., Zin, A.A.M., Memon, A.P. and Mokhtar, A.S. (2017) A New Optimal Feature Selection Algorithm for Classification of Power Quality Disturbances Using Discrete Wavelet Transform and Probabilistic Neural Network. Measurement, 95, 246-259.
https://doi.org/10.1016/j.measurement.2016.10.013
[74]  Thirumala, K., Maganuru, S.P., Jain, T. and Umarikar, A. (2016) Tunable-Q Wavelet Transform and Dual Multiclass SVM for Online Automatic Detection of Power Quality Disturbances. IEEE Transactions on Smart Grid, 9, 3018-3028.
https://doi.org/10.1109/TSG.2016.2624313
[75]  Malbasa, V., Zheng, C., Chen, P.C., Popovic, T. and Kezunovic, M. (2017) Voltage Stability Prediction Using Active Machine Learning. IEEE Transactions on Smart Grid, 8, 3117-3124.
https://doi.org/10.1109/TSG.2017.2693394
[76]  De Yong, D., Bhowmik, S. and Magnago, F. (2015) An Effective Power Quality Classifier Using Wavelet Transform and Support Vector Machines. Expert Systems with Applications, 42, 6075-6081.
https://doi.org/10.1016/j.eswa.2015.04.002
[77]  Uçar, F., Alçin, Ö.F., Dandil, B. and Ata, F. (2016) Machine Learning Based Power Quality Event Classification Using Wavelet—Entropy and Basic Statistical Features. 2016 21st International Conference on Methods and Models in Automation and Robotics, Miedzyzdroje, 29 August-1 September 2016, 414-419.
https://doi.org/10.1109/MMAR.2016.7575171
[78]  Sahani, M., Mishra, S., Ipsita, A. and Upadhyay, B. (2016) Detection and Classification of Power Quality Event Using Wavelet Transform and Weighted Extreme Learning Machine. 2016 International Conference on Circuit, Power and Computing Technologies, Nagercoil, 18-19 March 2016, 1-6.
[79]  Markovska, M. and Taskovski, D. (2017) On the Choice of Wavelet-Based Features in Power Quality Disturbances Classification. 2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe, Milan, 6-9 June, 2017, 1-6.
https://doi.org/10.1109/EEEIC.2017.7977586
[80]  Naik, C., Hafiz, F., Swain, A. and Kar, A.K. (2016) Classification of Power Quality Events Using Wavelet Packet Transform and extreme Learning Machine. IEEE Annual Southern Power Electronics Conference, Auckland, 5-8 December 2016, 1-6.
https://doi.org/10.1109/SPEC.2016.7846169
[81]  Upadhyaya, S. and Mohanty, S. (2016) Localization and Classification of Power Quality Disturbances using Maximal Overlap Discrete Wavelet Transform and Data Mining Based Classifiers**Sponsor, and Financial Support Acknowledgment Goes here. Paper Titles Should Be Written in Uppercase and Lowercase Letters, Not All Uppercase. IFAC-PapersOnLine, 49, 437-442.
https://doi.org/10.1016/j.ifacol.2016.03.093
[82]  Deokar, S.A. and Waghmare, L.M. (2014) Integrated DWT-FFT Approach for Detection and Classification of Power Quality Disturbances. International Journal of Electrical Power & Energy Systems, 61, 594-605.
https://doi.org/10.1016/j.ijepes.2014.04.015
[83]  Kamthekar, P.R., Munje, R.K. and Kushare, B.E. (2017) Detection and Classification of Power Quality Events Using DWT and MSD. 2017 International Conference on Innovative Mechanisms for Industry Applications, Bengaluru, 21-23 February 2017, 150-157.
https://doi.org/10.1109/ICIMIA.2017.7975591
[84]  Karimi, M., Mokhtari, H. and Iravani, M.R. (2000) Wavelet-Based On-Line Disturbance Detection for Power Quality Applications. IEEE Transactions on Power Delivery, 15, 1212-1220.
https://doi.org/10.1109/61.891505
[85]  Axelberg, P.G., Gu, I.Y.H. and Bollen, M.H. (2007) Support Vector Machine for Classification of Voltage Disturbances. IEEE Transactions on Power Delivery, 22, 1297-1303.
https://doi.org/10.1109/TPWRD.2007.900065
[86]  Granados-Lieberman, D., Romero-Troncoso, R.J., Osornio-Rios, R.A., Garcia-Perez, A. and Cabal-Yepez, E. (2011) Techniques and Methodologies for Power Quality Analysis and Disturbances Classification in Power Systems: A Review. IET Generation, Transmission & Distribution, 5, 519-529.
https://doi.org/10.1049/iet-gtd.2010.0466
[87]  Dash, P.K., Panigrahi, B.K., Sahoo, D.K. and Panda, G. (2003) Power Quality Disturbance Data Compression, Detection, and Classification Using Integrated Spline Wavelet and S-Transform. IEEE Transactions on Power Delivery, 18, 595-600.
https://doi.org/10.1109/TPWRD.2002.803824
[88]  Lee, I.W. and Dash, P.K. (2003) S-Transform-Based Intelligent System for Classification of Power Quality Disturbance Signals. IEEE Transactions on Industrial Electronics, 50, 800-805.
https://doi.org/10.1109/TIE.2003.814991
[89]  Gu, Y.H. and Bollen, M.H. (2000) Time-Frequency and Time-Scale Domain Analysis of Voltage Disturbances. IEEE Transactions on Power Delivery, 15, 1279-1284.
https://doi.org/10.1109/61.891515
[90]  Wright, P.S. (1999) Short-Time Fourier Transforms and Wigner-Ville Distributions Applied to the Calibration of Power Frequency Harmonic Analyzers. IEEE Transactions on Instrumentation and Measurement, 48, 475-478.
https://doi.org/10.1109/19.769633
[91]  Jurado, F. and Saenz, J.R. (2002) Comparison between Discrete STFT and Wavelets for the Analysis of Power Quality Events. Electric Power Systems Research, 62, 183-190.
https://doi.org/10.1016/S0378-7796(02)00035-4
[92]  Huang, S.J., Hsieh, C.T. and Huang, C.L. (1999) Application of Morlet Wavelets to Supervise Power System Disturbances. IEEE Transactions on Power Delivery, 14, 235-243.
https://doi.org/10.1109/61.736728
[93]  Santoso, S., Grady, W.M., Powers, E.J., Lamoree, J. and Bhatt, S.C. (2000) Characterization of Distribution Power Quality Events with Fourier and Wavelet Transforms. IEEE Transactions on Power Delivery, 15, 247-254.
https://doi.org/10.1109/61.847259
[94]  Heydt, G.T., Fjeld, P.S., Liu, C.C., Pierce, D., Tu, L. and Hensley, G. (1999) Applications of the Windowed FFT to Electric Power Quality Assessment. IEEE Transactions on Power Delivery, 14, 1411-1416.
https://doi.org/10.1109/61.796235
[95]  Islam, M.M., Hossain, M.R., Dougal, R.A. and Brice, C.W. (2016) Analysis of Real-World Power Quality Disturbances Employing Time-Frequency Distribution. 2016 Clemson University Power Systems Conference, Clemson, 8-11 March 2016, 1-5.
https://doi.org/10.1109/PSC.2016.7462816
[96]  Borges, F.A., Fernandes, R.A., Silva, I.N. and Silva, C.B. (2016) Feature Extraction and Power Quality Disturbances Classification Using Smart Meters Signals. IEEE Transactions on Industrial Informatics, 12, 824-833.
https://doi.org/10.1109/TII.2015.2486379
[97]  Lin, L., Wu, X., Qi, J. and Ci, H. (2016) Power Quality Disturbance Classification Based on a Novel Fourier Neural Network and Hyperbolic S-Transform. International Journal of Signal Processing, Image Processing, and Pattern Recognition, 9, 111-124.
https://doi.org/10.14257/ijsip.2016.9.1.11
[98]  Singh, U. and Singh, S.N. (2017) Application of Fractional Fourier Transforms for Classification of Power Quality Disturbances. IET Science, Measurement & Technology, 11, 67-76.
https://doi.org/10.1049/iet-smt.2016.0194
[99]  Dwivedi, U.D. and Singh, S.N. (2009) Denoising Techniques with a Change-Point Approach for Wavelet-Based Power-Quality Monitoring. IEEE Transactions on Power Delivery, 24, 1719-1727.
https://doi.org/10.1109/TPWRD.2009.2022665
[100]  Krishna, B.V. and Baskaran, K. (2013) Parallel Computing for Efficient Time-Frequency Feature Extraction of Power Quality Disturbances. IET Signal Processing, 7, 312-326.
https://doi.org/10.1049/iet-spr.2012.0262
[101]  Norman, C.F., Chan, J.Y., Lau, W.H. and Lai, L.L. (2012) Hybrid Wavelet and Hilbert Transform with Frequency-Shifting Decomposition for Power Quality Analysis. IEEE Transactions on Instrumentation and Measurement, 61, 3225-3233.
https://doi.org/10.1109/TIM.2012.2211474
[102]  Zhang, H., Liu, P. and Malik, O.P. (2003) Detection and Classification of Power Quality Disturbances in Noisy Conditions. IEE Proceedings-Generation, Transmission, and Distribution, 150, 567-572.
https://doi.org/10.1049/ip-gtd:20030459
[103]  Lin, W.M. Wu, C.H. Lin, C.H. and Cheng, F.S. (2008) Detection and Classification of Multiple Power-Quality Disturbances with Wavelet Multiclass SVM. IEEE Transactions on Power Delivery, 23, 2575-2582.
https://doi.org/10.1109/TPWRD.2008.923463
[104]  Dash, P.K., Liew, A.C., Salama, M.M.A., Mishra, B.R. and Jena, R.K. (1999) A New Approach to Identification of Transient Power Quality Problems Using Linear Combiners. Electric Power Systems Research, 51, 1-11.
https://doi.org/10.1016/S0378-7796(98)00087-X
[105]  Abdel-Galil, T.K., El-Saadany, E.F. and Salama, M.M.A. (2003) Power Quality Event Detection Using Adaline. Electric Power Systems Research, 64, 137-144.
https://doi.org/10.1016/S0378-7796(02)00173-6
[106]  Chen, Z. and Urwin, P. (2001) Power Quality Detection and Classification Using Digital Filters. 2001 IEEE Porto Power Tech Proceedings, Porto, 10-13 September 2001, 1-6.
https://doi.org/10.1109/PTC.2001.964610
[107]  Yilmaz, A.S., Alkan, A. and Asyali, M.H. (2008) Applications of Parametric Spectral Estimation Methods on Detection of Power System Harmonics. Electric Power Systems Research, 78, 683-693.
https://doi.org/10.1016/j.epsr.2007.05.011
[108]  Cho, S.H., Jang, G. and Kwon, S.H. (2010) Time-Frequency Analysis of Power-Quality Disturbances via the Gabor-Wigner Transform. IEEE Transactions on Power Delivery, 25, 494-499.
https://doi.org/10.1109/TPWRD.2009.2034832
[109]  Decanini, J.G., Tonelli-Neto, M.S., Malange, F.C. and Minussi, C.R. (2011) Detection and Classification of Voltage Disturbances Using a Fuzzy-ARTMAP-Wavelet Network. Electric Power Systems Research, 81, 2057-2065.
https://doi.org/10.1016/j.epsr.2011.07.018
[110]  Styvaktakis, E., Bollen, M.H. and Gu, I.Y. (2002) Expert System for Classification and Analysis of Power System Events. IEEE Transactions on Power Delivery, 17, 423-428.
https://doi.org/10.1109/61.997911
[111]  Macias, J.R. and Exposito, A.G. (2006) Self-Tuning of Kalman Filters for Harmonic Computation. IEEE Transactions on Power Delivery, 21, 501-503.
https://doi.org/10.1109/TPWRD.2005.860411
[112]  Perez, E. and Barros, J. (2008) An Extended Kalman Filtering Approach for Detection and Analysis of Voltage Dips in Power Systems. Electric Power Systems Research, 78, 618-625.
https://doi.org/10.1016/j.epsr.2007.05.006
[113]  Masoum, M.A.S., Jamali, S. and Ghaffarzadeh, N. (2010) Detection and Classification of Power Quality Disturbances Using Discrete Wavelet Transform and Wavelet Networks. IET Science, Measurement & Technology, 4, 193-205.
https://doi.org/10.1049/iet-smt.2009.0006
[114]  Manimala, K., Selvi, K. and Ahila, R. (2011) Hybrid Soft Computing Techniques for Feature Selection and Parameter Optimization in Power Quality Data Mining. Applied Soft Computing, 11, 5485-5497.
https://doi.org/10.1016/j.asoc.2011.05.010
[115]  De la Rosa, J.J.G., Agüera-Pérez, A., Palomares-Salas, J.C., Sierra-Fernández, J.M. and Moreno-Muñoz, A. (2012) A Novel Virtual Instrument for Power Quality Surveillance Based on Higher-Order Statistics and Case-Based Reasoning. Measurement, 45, 1824-1835.
https://doi.org/10.1016/j.measurement.2012.03.036
[116]  Balouji, E. and Salor, O. (2017) Classification of Power Quality Events Using Deep Learning on Event Images. 2017 3rd International Conference on Pattern Recognition and Image Analysis, Shahrekord, 19-20 April 2017, 216-221.
https://doi.org/10.1109/PRIA.2017.7983049
[117]  Ma, J., Zhang, J., Xiao, L., Chen, K. and Wu, J. (2017) Classification of Power Quality Disturbances via Deep Learning. IETE Technical Review, 34, 408-415.
https://doi.org/10.1080/02564602.2016.1196620
[118]  Ferreira, D.D., de Seixas, J.M. and Cerqueira, A.S. (2015) A Method Based on Independent Component Analysis for Single and Multiple Power Quality Disturbance Classifications. Electric Power Systems Research, 119, 425-431.
https://doi.org/10.1016/j.epsr.2014.10.028
[119]  Zyabkina, O., Domagk, M., Meyer, J. and Schegner, P. (2016) Classification and Identification of Anomalies in Time Series of Power Quality Measurements. 2016 IEEE PES Innovative Smart Grid Technologies Conference Europe, Ljubljana, 9-12 October 2016, 1-6.
https://doi.org/10.1109/ISGTEurope.2016.7856290
[120]  Saputra, I.D., Smith, J.S., Jiang, L. and Wu, Q.H. (2016) Detection and Classification of Power Disturbances Using Half Multi-Resolution Morphology Gradient. 2016 IEEE PES Innovative Smart Grid Technologies Conference Europe, Ljubljana, 9-12 October 2016, 1-5.
https://doi.org/10.1109/ISGTEurope.2016.7856263
[121]  Gharavi, H. and Hu, B. (2017) Space-Time Approach for Disturbance Detection and Classification. IEEE Transactions on Smart Grid, PP, 1.
[122]  Ahsan, M.K., Pan, T.H. and Li, Z.M. (2018) Intelligent Control System to Detect, Classify and Reserve Power Quality in Micro Grid through Multi-Agent System. American Journal of Engineering, Technology and Society, 5, 30-52.
[123]  Ibrahim, W.A. and Morcos, M.M. (2002) Artificial Intelligence and Advanced Mathematical Tools for Power Quality Applications: A Survey. IEEE Transactions on Power Delivery, 17, 668-673.
https://doi.org/10.1109/61.997958
[124]  Saini, M.K. and Kapoor, R. (2012) Classification of Power Quality Events—A Review. International Journal of Electrical Power & Energy Systems, 43, 11-19.
https://doi.org/10.1016/j.ijepes.2012.04.045
[125]  Ghosh, A.K. and Lubkeman, D.L. (1994) The Classification of Power System Disturbance Waveforms Using a Neural Network Approach. IEEE Transactions on Power Delivery, 10, 109-115.
https://doi.org/10.1109/61.368408
[126]  Dilokratanatrakool, C., Ayudhya, P.N., Chayavanich, T. and Prapanavarat, C. (2003) Automatic Detection-Localization of Fault Point on Waveform and Classification of Power Quality Disturbance Waveshape Fault Using Wavelet and Neural Network. Proceedings of the IEEE International Conference on Robotics, Intelligent Systems and Signal Processing, 1, 142-147.
[127]  Srinivasan, D., Ng, W.S. and Liew, A.C. (2006) Neural-Network-Based Signature Recognition for Harmonic Source Identification. IEEE Transactions on Power Delivery, 21, 398-405.
https://doi.org/10.1109/TPWRD.2005.852370
[128]  Silva, K.M., Souza, B.A. and Brito, N.S. (2006) Fault Detection and Classification in Transmission Lines Based on Wavelet Transform and ANN. IEEE Transactions on Power Delivery, 21, 2058-2063.
https://doi.org/10.1109/TPWRD.2006.876659
[129]  Cesar, D.G., Valdomiro, V.G. and Gabriel, O.P. (2006) Automatic Power Quality Disturbances Detection and Classification Based on Discrete Wavelet Transform and artificial Intelligence. 2006 IEEE/PES Transmission & Distribution Conference and Exposition: Latin America, Caracas, 15-18 August 2006, 1-6.
https://doi.org/10.1109/TDCLA.2006.311515
[130]  Chandel, A.K., Guleria, G. and Chandel, R. (2008) Classification of Power Quality Problems Using Wavelet-Based Artificial Neural Network. 2008 IEEE/PES Transmission and Distribution Conference and Exposition, Chicago, IL, 21-24 April 2008, 1-5.
https://doi.org/10.1109/TDC.2008.4517083
[131]  Talaat, N. and Ilic, M. (2008) ANNs Based on Subtractive Cluster Feature for Classifying Power Quality. 2008 40th North American Power Symposium, Calgary, AB, 28-30 September 2008, 1-7.
[132]  Huang, N., Xu, D., Liu, X. and Lin, L. (2012) Power Quality Disturbances Classification Based on S-Transform and Probabilistic Neural Network. Neurocomputing, 98, 12-23.
https://doi.org/10.1016/j.neucom.2011.06.041
[133]  Biswal, B., Biswal, M., Mishra, S. and Jalaja, R. (2014) Automatic Classification of Power Quality Events Using Balanced Neural Tree. IEEE Transactions on Industrial Electronics, 61, 521-530.
https://doi.org/10.1109/TIE.2013.2248335
[134]  Valtierra-Rodriguez, M., de Jesus Romero-Troncoso, R., Osornio-Rios, R.A. and Garcia-Perez, A. (2014) Detection and Classification of Single and Combined Power Quality Disturbances Using Neural Networks. IEEE Transactions on Industrial Electronics, 61, 2473-2482.
https://doi.org/10.1109/TIE.2013.2272276
[135]  Liao, C.C. (2010) Enhanced RBF Network for Recognizing Noise-Riding Power Quality Events. IEEE Transactions on Instrumentation and Measurement, 59, 1550-1561. https://doi.org/10.1109/TIM.2009.2027769
[136]  Mahela, O.P., Shaik, A.G. and Gupta, N. (2015) A Critical Review of Detection and Classification of Power Quality Events. Renewable and Sustainable Energy Reviews, 41, 495-505.
https://doi.org/10.1016/j.rser.2014.08.070
[137]  Jayasree, T., Devaraj, D. and Sukanesh, R. (2009) Power Quality Disturbance Classification Using S-Transform and Radial Basis Network. Applied Artificial Intelligence, 23, 680-693.
https://doi.org/10.1080/08839510903205563
[138]  Tan, R.H.G. and Ramachandramurthy, V.K. (2010) Numerical Model Framework of Power Quality Disturbances. European Journal of Scientific Research, 43, 30-47.
[139]  Mohod, S.B. and Ghate, V.N. (2015) MLP-Neural Network-Based Detection and Classification of Power Quality Disturbances. 2015 International Conference on Energy Systems and Applications, Pune, 30 October-1 November 2015, 124-129.
https://doi.org/10.1109/ICESA.2015.7503325
[140]  Chen, Z.M., Li, M.S., Ji, T.Y. and Wu, Q.H. (2016) Detection and Classification of Power Quality Disturbances in Time Domain Using Probabilistic Neural Network. 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, 24-29 July 2016, 1277-1282.
https://doi.org/10.1109/IJCNN.2016.7727344
[141]  Alshahrani, S., Abbod, M. and Taylort, G. (2016) Detection and Classification of Power Quality Disturbances Based on Hilbert-Huang Transform and Feed forward Neural Networks. 2016 51st International Universities Power Engineering Conference, 6-9 September 2016, Coimbra, 1-6.
https://doi.org/10.1109/UPEC.2016.8114075
[142]  Khadse, C.B., Chaudhari, M.A. and Borghate, V.B. (2016) Conjugate Gradient Back-Propagation Based Artificial Neural Network for Real-Time Power Quality Assessment. International Journal of Electrical Power & Energy Systems, 82, 197-206.
https://doi.org/10.1016/j.ijepes.2016.03.020
[143]  Martinez-Figueroa, G.D.J., Morinigo-Sotelo, D., Zorita-Lamadrid, A.L., Morales-Velazquez, L. and Romero-Troncoso, R.D.J. (2017) FPGA-Based Smart Sensor for Detection and Classification of Power Quality Disturbances Using Higher Order Statistics. IEEE Access, 5, 14259-14274.
https://doi.org/10.1109/ACCESS.2017.2732726
[144]  Ajil, K.S., Thapliyal, P.K., Shukla, M.V., Pal, P.K., Joshi, P.C. and Navalgund, R.R. (2010) A New Technique for Temperature and Humidity Profile Retrieval from Infrared-Sounder Observations Using the Adaptive Neuro-Fuzzy Inference System. IEEE Transactions on Geoscience and Remote Sensing, 48, 1650-1659.
https://doi.org/10.1109/TGRS.2009.2037314
[145]  Mizutani, E. and Nishio, K. (2002) Multi-Illuminant Color Reproduction for Electronic Cameras via CANFIS Neuro-Fuzzy Modular Network Device Characterization. IEEE Transactions on Neural Networks, 13, 1009-1022.
https://doi.org/10.1109/TNN.2002.1021900
[146]  Chang, J., Han, G., Valverde, J.M., Griswold, N.C., Duque-Carrillo, J.F. and Sanchez-Sinencio, E. (1997) Cork Quality Classification System Using a Unified Image Processing and Fuzzy-Neural Network Methodology. IEEE Transactions on Neural Networks, 8, 964-974.
https://doi.org/10.1109/72.595897
[147]  Pires, V.F., Amaral, T.G. and Martins, J.F. (2011) Power Quality Disturbances Classification Using the 3-D Space Representation and PCA Based Neuro-Fuzzy Approach. Expert Systems with Applications, 38, 11911-11917.
https://doi.org/10.1016/j.eswa.2011.03.083
[148]  Reddy, M.J. and Mohanta, D.K. (2008) Adaptive-Neuro-Fuzzy Inference System Approach for Transmission Line Fault Classification and Location Incorporating Effects of Power Swings. IET Generation, Transmission & Distribution, 2, 235-244.
https://doi.org/10.1049/iet-gtd:20070079
[149]  Reddy, M.J.B. and Mohanta, D.K. (2008) Performance Evaluation of an Adaptive-Network-Based Fuzzy Inference System Approach for Location of Faults on Transmission Lines Using Monte Carlo Simulation. IEEE Transactions on Fuzzy Systems, 16, 909-919.
https://doi.org/10.1109/TFUZZ.2008.924210
[150]  Kumar, G.S., Kumar, B.K. and Mishra, M.K. (2011) Mitigation of Voltage Sags with Phase Jumps by UPQC with PSO-Based ANFIS. IEEE Transactions on Power Delivery, 26, 2761-2773.
https://doi.org/10.1109/TPWRD.2011.2165301
[151]  Dash, P., Chakravorti, T. and Patnaik, R. (2017) Advanced Signal Processing Techniques for Multiclass Disturbance Detection and Classification in Microgrids. IET Science, Measurement & Technology, 11, 504-515.
https://doi.org/10.1049/iet-smt.2016.0432
[152]  Dhimish, M., Holmes, V., Mehrdadi, B., Dales, M. and Mather, P. (2017) Photovoltaic Fault Detection Algorithm Based on Theoretical Curves Modeling and Fuzzy Classification System. Energy, 140, 276-290.
https://doi.org/10.1016/j.energy.2017.08.102
[153]  Chakraborty, S., Chatterjee, A. and Goswami, S.K. (2017) DTCWT Based Approach for Power Quality Disturbance Recognition. In: Computer, Communication and Electrical Technology: Proceedings of the International Conference on Advancement of Computer Communication and Electrical Technology (ACCET 2016), CRC Press, London, 209.
https://doi.org/10.1201/9781315400624-42
[154]  Vapnik, V. (2013) The Nature of Statistical Learning Theory. Springer Science & Business Media, Berlin, Heidelberg, Germany.
[155]  Khasnobish, A., Bhattacharyya, S., Konar, A., Tibarewala, D.N. and Nagar, A.K. (2011) A Two-Fold Classification for a Composite Decision about Localized Arm Movement from EEG by SVM and QDA Techniques. The 2011 International Joint Conference on Neural Networks, San Jose, CA, 31 July-5 August 2011, 1344-1351.
https://doi.org/10.1109/IJCNN.2011.6033380
[156]  Lv, G., Wang, X., Zhang, H. and Zhang, C. (2005) PQ Disturbances Identification Based on SVMs Classifier. 2005 International Conference on Neural Networks and Brain, Beijing, 13-15 October 2011, 222-226.
https://doi.org/10.1109/ICNNB.2005.1614602
[157]  Janik, P. and Lobos, T. (2006) Automated Classification of Power-Quality Disturbances Using SVM and RBF Networks. IEEE Transactions on Power Delivery, 21, 1663-1669.
https://doi.org/10.1109/TPWRD.2006.874114
[158]  Lin, W.M., Wu, C.H., Lin, C.H. and Cheng, F.S. (2006) Classification of Multiple Power Quality Disturbances Using Support Vector Machine and One-versus-One Approach. 2006 International Conference on Power System Technology, Chongqing, 22-26 October 2006, 1-8.
https://doi.org/10.1109/ICPST.2006.321956
[159]  Cerqueira, A.S., Ferreira, D.D., Ribeiro, M.V. and Duque, C.A. (2008) Power Quality Events Recognition Using a SVM-Based Method. Electric Power Systems Research, 78, 1546-1552.
https://doi.org/10.1016/j.epsr.2008.01.016
[160]  Janik, P. and Lobos, T. (2006) Automated Classification of Power Quality Disturbances Using SVM and RBF Networks. IEEE Transactions on Power Delivery, 21, 1663-1669.
https://doi.org/10.1109/TPWRD.2006.874114
[161]  Ekici, S. (2009) Classification of Power System Disturbances Using Support Vector Machines. Expert Systems with Applications, 36, 9859-9868.
https://doi.org/10.1016/j.eswa.2009.02.002
[162]  Panigrahi, B.K., Dash, P.K. and Reddy, J.B.V. (2009) Hybrid Signal Processing and Machine Intelligence Techniques for Detection, Quantification, and Classification of Power Quality Disturbances. Engineering Applications of Artificial Intelligence, 22, 442-454.
https://doi.org/10.1016/j.engappai.2008.10.003
[163]  Erişti, H. and Demir, Y. (2010) A New Algorithm for Automatic Classification of Power Quality Events Based on Wavelet Transform and SVM. Expert Systems with Applications, 37, 4094-4102.
https://doi.org/10.1016/j.eswa.2009.11.015
[164]  Erişti, H., Uçar, A. and Demir, Y. (2010) Wavelet-Based Feature Extraction and Selection for Classification of Power System Disturbances Using Support Vector Machines. Electric Power Systems Research, 80, 743-752.
https://doi.org/10.1016/j.epsr.2009.09.021
[165]  Moravej, Z., Abdoos, A.A. and Pazoki, M. (2009) Detection and Classification of Power Quality Disturbances Using Wavelet Transform and Support Vector Machines. Electric Power Components and Systems, 38, 182-196.
https://doi.org/10.1080/15325000903273387
[166]  Biswal, B., Biswal, M.K., Dash, P.K. and Mishra, S. (2013) Power Quality Event Characterization Using Support Vector Machine and Optimization Using Advanced Immune Algorithm. Neurocomputing, 103, 75-86.
https://doi.org/10.1016/j.neucom.2012.08.031
[167]  Palomares-Salas, J.C., Agüera-Pérez, A. and de la Rosa, J.J.G. (2011) Support Vector Machine for Power Quality Disturbances Classification Using Higher-Order Statistical Features. 2011 7th International Conference-Workshop Compatibility and Power Electronics, Tallinn, 1-3 June 2011, 6-10.
https://doi.org/10.1109/CPE.2011.5942198
[168]  Thirumala, K., Umarikar, A.C. and Jain, T. (2016) A New Classification Model Based on SVM for Single and Combined Power Quality Disturbances. 2016 National Power Systems Conference (NPSC), Bhubaneswar, 19-21 December 2016, 1-6.
https://doi.org/10.1109/NPSC.2016.7858889
[169]  Abdoos, A.A., Mianaei, P.K. and Ghadikolaei, M.R. (2016) Combined VMD-SVM Based Feature Selection Method for Classification of Power Quality Events. Applied Soft Computing, 38, 637-646.
https://doi.org/10.1016/j.asoc.2015.10.038
[170]  Cho, M.Y. and Hoang, T.T. (2017) Feature Selection and Parameters Optimization of SVM Using Particle Swarm Optimization for Fault Classification in Power Distribution Systems. Computational Intelligence and Neuroscience, 2017, Article ID: 4135465.
https://doi.org/10.1155/2017/4135465
[171]  Naderian, S. and Salemnia, A. (2017) An Implementation of Type-2 Fuzzy Kernel-Based Support Vector Machine Algorithm for Power Quality Events Classification. International Transactions on Electrical Energy Systems, 27, e2303.
https://doi.org/10.1002/etep.2303
[172]  Holland, J.H. and Goldberg, D. (1989) Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading, MA.
[173]  Levitin, G., Kalyuzhny, A., Shenkman, A. and Chertkov, M. (2000) Optimal Capacitor Allocation in Distribution Systems Using a Genetic Algorithm and a Fast Energy Loss Computation Technique. IEEE Transactions on Power Delivery, 15, 623-628.
https://doi.org/10.1109/61.852995
[174]  Kung, C.H., Devaney, M.J., Huang, C.M. and Kung, C.M. (1998) Fuzzy-Based Adaptive Digital Power Metering Using a Genetic Algorithm. IEEE Transactions on Instrumentation and Measurement, 47, 183-188.
https://doi.org/10.1109/19.728815
[175]  El-Zonkoly, A.M. (2005) Power System Model Validation for Power Quality Assessment Applications Using Genetic Algorithm. Expert Systems with Applications, 29, 941-944.
https://doi.org/10.1016/j.eswa.2005.06.013
[176]  El-Naggar, K.M. and Al-Hasawi, W.M. (2006) A Genetic-Based Algorithm for Measurement of Power System Disturbances. Electric Power Systems Research, 76, 808-814.
https://doi.org/10.1016/j.epsr.2005.06.012
[177]  Ma, H.M., Ng, K.T. and Man, K.F. (2008) Multiobjective Coordinated Power Voltage Control Using Jumping Genes Paradigm. IEEE Transactions on Industrial Electronics, 55, 4075-4084.
https://doi.org/10.1109/TIE.2008.928107
[178]  Hong, Y.Y. and Chen, Y.Y. (2011) Placement of Power Quality Monitors Using Enhanced Genetic Algorithm and Wavelet Transform. IET Generation, Transmission & Distribution, 5, 461-466.
https://doi.org/10.1049/iet-gtd.2010.0397
[179]  Wang, M.H. and Tseng, Y.F. (2011) A Novel Analytic Method of Power Quality Using Extension Genetic Algorithm and Wavelet Transform. Expert Systems with Applications, 38, 12491-12496.
https://doi.org/10.1016/j.eswa.2011.04.032
[180]  Sánchez, P., Montoya, F.G., Manzano-Agugliaro, F. and Gil, C. (2013) A Genetic Algorithm for S-Transform Optimisation in the Analysis and Classification of Electrical Signal Perturbations. Expert Systems with Applications, 40, 6766-6777.
https://doi.org/10.1016/j.eswa.2013.06.055
[181]  Jaen-Cuellar, A.Y., Morales-Velazquez, L., de Jesus Romero-Troncoso, R., Moriñigo-Sotelo, D. and Osornio-Rios, R.A. (2016) Micro-Genetic Algorithms for Detecting and Classifying Electric Power Disturbances. Neural Computing and Applications, 28, 379-392.
https://doi.org/10.1007/s00521-016-2355-z
[182]  Phan, A.V., Le Nguyen, M. and Bui, L.T. (2017) Feature Weighting and SVM Parameters Optimization Based on Genetic Algorithms for Classification Problems. Applied Intelligence, 46, 455-469.
https://doi.org/10.1007/s10489-016-0843-6
[183]  John, Y. and Langari, R. (1999) Fuzzy Logic: Intelligence, Control, and Information. Dorling Kindersley, India, 379-383.
[184]  Chacon, M.I., Duran, J.L. and Santiesteban, L.A. (2007) A Wavelet-Fuzzy logic Based System to Detect and Identify Electric Power Disturbances. 2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing, Honolulu, HI, 1-5 April 2007, 52-57.
https://doi.org/10.1109/CIISP.2007.369293
[185]  Bizjak, B. and Planinsic, P. (2006) Classification of Power Disturbances Using Fuzzy Logic. 2006 12th International Power Electronics and Motion Control Conference, Portoroz, 30 August-1 September 2006, 1356-1360.
[186]  Liao, Y. and Lee, J.B. (2004) A Fuzzy-Expert System for Classifying Power Quality Disturbances. International Journal of Electrical Power & Energy Systems, 26, 199-205.
https://doi.org/10.1016/j.ijepes.2003.10.012
[187]  Meher, S.K. and Pradhan, A.K. (2010) Fuzzy Classifiers for Power Quality Events Analysis. Electric Power Systems Research, 80, 71-76.
https://doi.org/10.1016/j.epsr.2009.08.014
[188]  Abdelsalam, A.A., Eldesouky, A.A. and Sallam, A.A. (2012) Characterization of Power Quality Disturbances Using Hybrid Technique of Linear Kalman Filter and Fuzzy-Expert System. Electric Power Systems Research, 83, 41-50.
https://doi.org/10.1016/j.epsr.2011.09.018
[189]  Hooshmand, R. and Enshaee, A. (2010) Detection and Classification of Single and Combined Power Quality Disturbances Using Fuzzy Systems Oriented by Particle Swarm Optimization Algorithm. Electric Power Systems Research, 80, 1552-1561.
https://doi.org/10.1016/j.epsr.2010.07.001
[190]  Biswal, B., Behera, H.S., Bisoi, R. and Dash, P.K. (2012) Classification of Power Quality Data Using Decision Tree and Chemotactic Differential Evolution Based Fuzzy Clustering. Swarm and Evolutionary Computation, 4, 12-24.
https://doi.org/10.1016/j.swevo.2011.12.003
[191]  Behera, H.S., Dash, P.K. and Biswal, B. (2010) Power Quality Time Series Data Mining Using S-Transform and Fuzzy Expert System. Applied Soft Computing, 10, 945-955.
https://doi.org/10.1016/j.asoc.2009.10.013
[192]  Biswal, B., Dash, P.K. and Panigrahi, B.K. (2009) Power Quality Disturbance Classification Using Fuzzy C-Means Algorithm and Adaptive Particle Swarm Optimization. IEEE Transactions on Industrial Electronics, 56, 212-220.
https://doi.org/10.1109/TIE.2008.928111
[193]  Dash, P.K., Mishra, S., Salama, M.A. and Liew, A.C. (2000) Classification of Power System Disturbances Using a Fuzzy Expert System and a Fourier Linear Combiner. IEEE Transactions on Power Delivery, 15, 472-477.
https://doi.org/10.1109/61.852971
[194]  Zhu, T.X., Tso, S.K. and Lo, K.L. (2004) Wavelet-Based Fuzzy Reasoning Approach to Power-Quality Disturbance Recognition. IEEE Transactions on Power Delivery, 19, 1928-1935.
https://doi.org/10.1109/TPWRD.2004.832382
[195]  Ibrahim, W.R.A. and Morcos, M.M. (2006) An Adaptive Fuzzy Self-Learning Technique for Prediction of Abnormal Operation of Electrical Systems. IEEE Transactions on Power Delivery, 21, 1770-1777.
https://doi.org/10.1109/TPWRD.2006.881795
[196]  Ibrahim, W.R.A. and Morcos, M.M. (2005) Novel Data Compression Technique for Power Waveforms Using Adaptive Fuzzy Logic. IEEE Transactions on Power Delivery, 20, 2136-2143.
https://doi.org/10.1109/TPWRD.2005.848458
[197]  Chakravorti, T. and Dash, P.K. (2016, December) Morphology-Based Fuzzy Approach for Detection & Classification of Simultaneous Power Quality Disturbances. 2016 IEEE Annual India Conference (INDICON), Bangalore, 16-18 December 2016, 1-6.
https://doi.org/10.1109/INDICON.2016.7838926
[198]  Sundaram, P.K. and Neela, R. (2016) Electric Power Quality Events Classification Using Kalman Filter and Fuzzy Expert System. International Journal of Applied Engineering Research, 11, 5956-5962.
[199]  Sundaram, P.K. and Neela, R. (2016) Automatic Recognition of Power Quality Disturbances Using Kalman Filter and Fuzzy Expert System. International Journal of Computer Applications, 149, 16-23.
https://doi.org/10.5120/ijca2016911353
[200]  Sundaram, P.K. and Neela, R. (2016) Hilbert Transform Based Fuzzy Expert System for Diagnosing and Classifying Power Quality Disturbances. International Journal of Computer Applications, 142, 48-55.
https://doi.org/10.5120/ijca2016909729
[201]  Sahu, G., Biswal, B. and Choubey, A. (2017) Non-Stationary Signal Classification via Modified Fuzzy C-Means Algorithm and Improved Bacterial Foraging Algorithm. International Journal of Numerical Modelling: Electronic Networks, Devices, and Fields, 30, e2181.
https://doi.org/10.1002/jnm.2181
[202]  Rodríguez, A., Aguado, J.A., Martín, F., López, J.J., Muñoz, F. and Ruiz, J.E. (2012) Rule-Based Classification of Power Quality Disturbances Using S-Transform. Electric Power Systems Research, 86, 113-121.
https://doi.org/10.1016/j.epsr.2011.12.009
[203]  Salem, M.E., Mohamed, A. and Samad, S.A. (2010) A Rule-Based System for Power Quality Disturbance Classification Incorporating S-Transform Features. Expert Systems with Applications, 37, 3229-3235.
https://doi.org/10.1016/j.eswa.2009.09.057
[204]  Zheng, G., Shi, M.X., Liu, D., Yao, J. and Miao, Z.M. (2002) Power Quality Disturbance Classification Based on Rule-Based and Wavelet-Multi-Resolution Decomposition. Proceedings of the International Conference on Machine Learning and Cybernetics, 4, 2137-2141.
https://doi.org/10.1109/ICMLC.2002.1175416
[205]  Reddy, M.V. and Sodhi, R. (2016) A Rule-Based S-Transform and AdaBoost Based Approach for Power Quality Assessment. Electric Power Systems Research, 134, 66-79.
https://doi.org/10.1016/j.epsr.2016.01.003
[206]  Liu, H., Hussain, F. and Shen, Y. (2017) Power Quality Disturbances Classification Using Compressive Sensing and Maximum Likelihood. IETE Technical Review, 1-10.
https://doi.org/10.1080/02564602.2017.1304290
[207]  Reaz, M.B.I., Choong, F., Sulaiman, M.S., Mohd-Yasin, F. and Kamada, M. (2007) Expert System for Power Quality Disturbance Classifier. IEEE Transactions on Power Delivery, 22, 1979-1988.
https://doi.org/10.1109/TPWRD.2007.899774
[208]  Yu, J., Wang, L., Zhou, B. and Tian, W. (2011, August) An Expert System Based on s-Transform for Classification of Voltage Dips. 2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC), Deng Leng, 8-10 August 2011, 3732-3735.
[209]  Ferrero, A. and Salicone, S. (2005) An Easy VI Program to Detect Transient Disturbances in the Supply Voltage. IEEE Transactions on Instrumentation and Measurement, 54, 1471-1474.
https://doi.org/10.1109/TIM.2005.851078
[210]  Ramos, P.M., Janeiro, F.M. and Serra, A.C. (2008) PQ Monitoring System for Real-Time Detection and Classification of Disturbances in a Single-Phase Power System. IEEE Transactions on Instrumentation and Measurement, 57, 1725-1733.
https://doi.org/10.1109/TIM.2008.925345
[211]  Youssef, A.M., Abdel-Galil, T.K., El-Saadany, E.F. and Salama, M.M.A. (2004) Disturbance Classification is Utilizing Dynamic Time Warping Classifier. IEEE Transactions on Power Delivery, 19, 272-278.
https://doi.org/10.1109/TPWRD.2003.820178
[212]  Khosravi, A., Meléndez, J. and Colomer, J. (2009) Classification of Sags Gathered in Distribution Substations Based on Multiway Principal Component Analysis. Electric Power Systems Research, 79, 144-151.
https://doi.org/10.1016/j.epsr.2008.05.014
[213]  Moreto, M. and Rolim, J.G. (2011) Using Phasor Data Records and Sequence of Events to Automate the Classification of Disturbances of Power Generating Units. Electric Power Systems Research, 81, 1266-1273.
https://doi.org/10.1016/j.epsr.2011.01.008
[214]  Abdel-Galil, T.K., Kamel, M., Youssef, A.M., El-Saadany, E.F. and Salama, M.M.A. (2004) Power Quality Disturbance Classification Using the Inductive Inference Approach. IEEE Transactions on Power Delivery, 19, 1812-1818.
https://doi.org/10.1109/TPWRD.2003.822533
[215]  De Yong, D., Reineri, C. and Magnago, F. (2013) Educational Software for Power Quality Analysis. IEEE Latin America Transactions, 11, 479-485.
https://doi.org/10.1109/TLA.2013.6502849
[216]  Lima, R., Quiroga, D., Reineri, C. and Magnago, F. (2008) Hardware and Software Architecture for Power Quality Analysis. Computers & Electrical Engineering, 34, 520-530.
https://doi.org/10.1016/j.compeleceng.2007.12.003
[217]  Salem, M.E., Mohamed, A., Samad, S.A. and Yahya, I. (2007) Software Tool for Real-Time Power Quality Disturbance Analysis and Classification. 2007 5th Student Conference on Research and Development, Selangor, Malaysia, 12-11 December 2007, 1-5.
https://doi.org/10.1109/SCORED.2007.4451390
[218]  Abdel-Galil, T.K., El-Saadany, E.F., Youssef, A.M. and Salama, M.M.A. (2005) Disturbance Classification Using Hidden Markov Models and Vector Quantization. IEEE Transactions on Power Delivery, 20, 2129-2135.
https://doi.org/10.1109/TPWRD.2004.843399
[219]  Gaouda, A.M., Kanoun, S.H. and Salama, M.M.A. (2001) On-Line Disturbance Classification Using Nearest Neighbor Rule. Electric Power Systems Research, 57, 1-8.
https://doi.org/10.1016/S0378-7796(00)00120-6
[220]  Daponte, P., Di Penta, M. and Mercurio, G. (2004) TransientMeter: A Distributed Measurement System for Power Quality Monitoring. IEEE Transactions on Power Delivery, 19, 456-463.
https://doi.org/10.1109/TPWRD.2004.825200
[221]  Wang, X., Bi, G.H., Chen, S.L. and Zu, Z. (2011) A Method to Analyze Power System Quality Disturbing Signal Based on Recurrence Quantification Analysis. Procedia Engineering, 15, 4115-4121.
https://doi.org/10.1016/j.proeng.2011.08.772
[222]  Kim, H.J., Shim, J.W., Sim, K. and Hur, K. (2013) Assessment of Improved Power Quality Due to Fault Current Limiting HTS Cable. IEEE Transactions on Applied Superconductivity, 23, 5602104-5602104.
https://doi.org/10.1109/TASC.2012.2235500
[223]  Dugan, R.C., McGranaghan, M.F., et al. (2012) Electrical Power Systems Quality. McGraw-Hill Education, New York, USA.
[224]  Rodriguez-Guerrero, M.A., Carranza-Lopez-Padilla, R., Osornio-Rios, R.A. and Romero-Troncoso, R.D.J. (2017) A Novel Methodology for Modeling Waveforms for Power Quality Disturbance Analysis. Electric Power Systems Research, 143, 14-24.
https://doi.org/10.1016/j.epsr.2016.09.003
[225]  Hua, L., Baoqun, Z. and Guangjian, W. (2007) Application of Wavelet Network for Automatic Power Quality Disturbances Recognition in Distribution Power System. 2007 Chinese Control Conference, Hunan, 26-31 July 2007, 254-258.
[226]  Panigrahi, B.K. and Sinha, S.K. (2006) Detection and Classification of Non-Stationary Power Disturbances in Noisy Conditions. 2006 International Conference on Power Electronic, Drives and Energy Systems, New Delhi, 12-15 December 2006, 1-5.
https://doi.org/10.1109/PEDES.2006.344258
[227]  Ozgonenel, O., Yalcin, T., Guney, I. and Kurt, U. (2013) A New Classification for Power Quality Events in Distribution System. Electric Power Systems Research, 95, 192-199.
https://doi.org/10.1016/j.epsr.2012.09.007
[228]  Hu, W.B., Li, K.C., Zhao, D.J. and Xie, B.R. (2007) Performance Improvement of Power Quality Disturbance Classification Based on a New De-Noising Technique. 2007 International Conference on Electrical Machines and Systems (ICEMS), Seoul, 8-11 October 2007, 1806-1810.
[229]  Liao, CC. and Yang, H.T. (2009) Recognizing Noise-Influenced Power Quality Events with Integrated Feature Extraction and Neuro-Fuzzy Network. IEEE Transactions on Power Delivery, 24, 2132-2141.
https://doi.org/10.1109/TPWRD.2009.2016789
[230]  Dwivedi, U.D. and Singh, S.N. (2010) Enhanced Detection of Power-Quality Events Using Intro and Interscale Dependencies of Wavelet Coefficients. IEEE Transactions on Power Delivery, 25, 358-366.
https://doi.org/10.1109/TPWRD.2009.2027482
[231]  Liao, C.C., Yang, H.T. and Chang, H.H. (2011) Denoising Techniques with a Spatial Noise-Suppression Method for Wavelet-Based Power Quality Monitoring. IEEE Transactions on Instrumentation and Measurement, 60, 1986-1996.
https://doi.org/10.1109/TIM.2011.2115610
[232]  Park, M., Kim, D. and Oh, H.S. (2015) Quantile-Based Empirical Mode Decomposition: An Efficient Way to Decompose Noisy Signals. IEEE Transactions on Instrumentation and Measurement, 64, 1802-1813.
https://doi.org/10.1109/TIM.2014.2381355
[233]  Hao, H., Wang, H.L. and Rehman, N.U. (2017) A Joint Framework for Multivariate Signal Denoising Using Multivariate Empirical Mode Decomposition. Signal Processing, 135, 263-273.
https://doi.org/10.1016/j.sigpro.2017.01.022
[234]  Guo, B., Peng, S., Hu, X. and Xu, P. (2017) Complex-Valued Differential Operator-Based Method for Multi-Component Signal Separation. Signal Processing, 132, 66-76.
https://doi.org/10.1016/j.sigpro.2016.09.015

Full-Text

comments powered by Disqus