Fast Support Vector Machine for Power Quality Disturbance Classification
Source abstract
Abstract
The power quality disturbance (PQD) problem involves problems of voltage swell, voltage sag, power interruption, harmonics and complex events involving multiple PQD problems. The PQD problem attracted considerable attention from utilities, especially when renewable energy is getting a higher penetration. The PQD problem could downgrade the service quality, causing problems of malfunctions and instabilities. This paper proposed a simplified SVM technique to identify the PQD problem including the multiple PQD classification. With the simple structure proposed, the methodology could reduce a great deal of training data; requires much less memory space and saves computing time. An IEEE 14-bus power system was used to show the performance. Many tests were conducted, and the method was compared with an artificial neural network (ANN). Simulation results showed the shortened processing time and the effectiveness of the proposed approach.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Power Quality and Harmonics · Power Transformer Diagnostics and Insulation · Energy Load and Power Forecasting
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanical and Electrical Engineering, Tan Kah Kee College, Xiamen University, Zhangzhou 361005, China
Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan
Funding
Tan Kah Kee College, Xiamen University
References (36)
Show source-provided references
1. Disturbance Ratio for Optimal Multi-Event Classification in Power Distribution Networks10.1109/TIE.2016.2521615 ↗
2. Cause, classification of voltage sag, and voltage sag emulators and applications: A comprehensive overview10.1109/ACCESS.2019.2958965 ↗
3. Multiple Harmonic Source Detection and Equipment Identification With Cascade Correlation Network10.1109/TPWRD.2004.843462 ↗
4. Grid Integration Challenges of Wind Energy: A Review10.1109/ACCESS.2020.2964896 ↗
5. An Improved Hilbert–Huang Method for Analysis of Time-Varying Waveforms in Power Quality10.1109/TPWRS.2007.907542 ↗
6. Empirical mode decomposition with Hilbert transform for classification of voltage sag causes using probabilistic neural network10.1016/j.ijepes.2012.07.040 ↗
7. A hybrid intelligence approach for power quality disturbances detection and classification10.1002/etep.1628 ↗
8. Method for classification of PQ events based on discrete Gabor transform with FIR window and T2FK-based SVM and its experimental verification10.1049/iet-gtd.2016.0703 ↗
9. Adaptive detection and enhancement of multiple sinusoids using a cascade IIR filter10.1109/31.31329 ↗
10. Characterization of distribution power quality events with Fourier and wavelet transforms10.1109/61.847259 ↗
11. Pecharanin, N., Sone, M., and Mitsui, H. (July, January 28). An application of neural network for harmonic detection in active filter. Proceedings of the 1994 IEEE International Conference on Neural Networks (ICNN’94), Orlando, FL, USA.10.1109/ICNN.1994.374807 ↗
12. A Proposal for On-Line Detection and Classification of Voltage Events in Power Systems10.1109/TPWRD.2008.921121 ↗
13. Application of Advanced Digital Signal Processing Tools for Analysis of Voltage Events in Power Systems10.7227/IJEEE.46.3.1 ↗
14. Hybrid Approach Based on GA and PSO for Parameter Estimation of a Full Power Quality Disturbance Parameterized Model10.1109/TII.2017.2743762 ↗
15. Placement of power quality monitors using enhanced genetic algorithm and wavelet transform10.1049/iet-gtd.2010.0397 ↗
16. Sammut, C., and Webb, G.I. (2011). Particle Swarm Optimization. Encyclopedia of Machine Learning, Springer.10.1007/978-0-387-30164-8 ↗
17. A hybrid ant colony optimization technique for power signal pattern classification10.1016/j.eswa.2010.11.102 ↗
18. An artificial bee colony-least square algorithm for solving harmonic estimation problems10.1016/j.asoc.2012.12.006 ↗
19. A patent quality analysis and classification system using self-organizing maps with support vector machine10.1016/j.asoc.2016.01.020 ↗
20. Adaptive Multiple Fault Detection and Alarm Processing for Loop System With Probabilistic Network10.1109/TPWRD.2003.820203 ↗
21. Classification of Electrical Disturbances in Real Time Using Neural Networks10.1109/TPWRD.2007.899522 ↗
22. Conjugate gradient back-propagation based artificial neural network for real time power quality assessment10.1016/j.ijepes.2016.03.020 ↗
23. Vapnik, V. (1998). Statistical Learning Theory, Wiley.
24. Kecman, V. (2001). Learning and Soft Computing, MIT Press.
25. Nonconvex Online Support Vector Machines10.1109/TPAMI.2010.109 ↗
26. Gunn, S.R. (1998). Support Vector Machines for Classification and Regression, IRIS Research Group, University of Southampton. Technical Report.
27. Scholkopf, B., and Smola, A.J. (2001). Learning with Kernel, MIT Press.
28. Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration10.1109/ACCESS.2020.3014732 ↗
29. A Tutorial on Support Vector Machines for Pattern Recognition10.1023/A:1009715923555 ↗
30. Performance Comparison of Variational Mode Decomposition over Empirical Wavelet Transform for the Classification of Power Quality Disturbances Using Support Vector Machine10.1016/j.procs.2015.02.033 ↗
31. Novelty detection and multi-class classification in power distribution voltage waveforms10.1016/j.eswa.2015.09.048 ↗
32. (1995). IEEE Recommended Practices for Monitoring Electric Power Quality (Standard No. ANSI/IEEE Std. 1159-1995).
33. Harmonic analysis for industrial customers10.1109/28.287508 ↗
34. Multiple-Frequency Three-Phase Load Flow for Harmonic Analysis10.1109/TPWRS.2004.825906 ↗
35. Zin, A.A.M., Rukonuzzaman, M., Shaibon, H., and Lo, K.L. (1998, January 5). Neural Network Approach of Harmonics Detection. Proceedings of the Proceedings of EMPD ’98. 1998 International Conference on Energy Management and Power Delivery, Singapore.
36. Application of algorithms and artificial-intelligence approach for locating multiple harmonics in distribution systems10.1049/ip-gtd:19990164 ↗