Journal of Electrical Engineering and Technology

The Korean Institute of Electrical Engineers (대한전기학회)
권호 표지
DOI QR코드

DOI QR Code

Application of SA-SVM Incremental Algorithm in GIS PD Pattern Recognition

  • Tang, Ju (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University) ;
  • Zhuo, Ran (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University) ;
  • Wang, DiBo (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University) ;
  • Wu, JianRong (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University) ;
  • Zhang, XiaoXing (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University)
  • Received : 2013.11.06
  • Accepted : 2015.05.26
  • Published : 2016.01.01
Download PDF
⟨ Previous Next ⟩

Abstract

With changes in insulated defects, the environment, and so on, new partial discharge (PD) data are highly different from the original samples. It leads to a decrease in on-line recognition rate. The UHF signal and pulse current signal of four kinds of typical artificial defect models in gas insulated switchgear (GIS) are obtained simultaneously by experiment. The relationship map of ultra-high frequency (UHF) cumulative energy and its corresponding apparent discharge of four kinds of typical artificial defect models are plotted. UHF cumulative energy and its corresponding apparent discharge are used as inputs. The support vector machine (SVM) incremental method is constructed. Examples show that the PD SVM incremental method based on simulated annealing (SA) effectively speeds up the data update rate and improves the adaptability of the classifier compared with the original method, in that the total sample is constituted by the old and new data. The PD SVM incremental method is a better pattern recognition technology for PD on-line monitoring.

Keywords

References

  1. Kranz, H. -G., “Fundamentals in computer aided PD processing, PD pattern recognition and automated diagnosis in GIS”, IEEE Transactions on Dielectrics and Electrical Insulation, Vol. 7, pp. 12-20, 2002.
  2. Jian Li, Caixin Sun, “Partial Discharge Image Recognition Using a New Group of Features”, IEEE Transactions on Dielectrics and Electrical Insulation, Vol. 13, No. 6, pp. 1245-1253, 2006. https://doi.org/10.1109/TDEI.2006.258196
  3. Xiaoxing Zhang, Jiangbo Ren, Ju Tan, Caixin Sun, “Kernel Statistical Uncorrelated Optimum Discriminant Vectors Algorithm for GIS PD Recognition”, IEEE Transactions on Dielectrics and Electrical Insulation, Vol. 16, No. 1, pp. 206-213, 2009 https://doi.org/10.1109/TDEI.2009.4784569
  4. M. M. A. Salama, R. Bartnikas, “Determination of Neural-Network Topology for Partial Discharge Pulse Pattern Recognition”, IEEE Transactions on Networks, Vol. 13, No. 2, pp. 446-456, 2002. https://doi.org/10.1109/72.991430
  5. N. C. Sahoo, M. M. A. Salama, “Trends in Partial Discharge Pattern Classification: A Survey”, IEEE Transactions on Dielectrics and Electrical Insulation, Vol. 12, No. 2, pp. 248-264, 2005. https://doi.org/10.1109/TDEI.2005.1430395
  6. Yanbin Xie, Ju Tang, Qian Zhou, “Feature extraction and recognition of UHF partial discharge signals in GIS based on dual-tree complex wavelet transform”, European Transactions on Electrical Power, Vol. 20, pp. 639-649, 2009.
  7. Jin Lijun, Zhang Mingrui, Liu Weidong, “Examination and Research on the Partial Discharge Fault Diagnostics in Gas Insulated Switchgear”, Transactions of China Electrotechnical Society, Vol. 20, No. 11, pp. 88-92, 2005.
  8. TANG Ju, WU Jianrong, ZHUO Ran, XIE Yanbin, “Relationship Between VHF Signals and Discharge Magnitude of Partial Discharge from Needle Plate Electrode”, High Volt age Engineering, Vol. 36, No. 5, pp. 1083-1089, 2010.
  9. Kranz, H. -G., “Diagnosis of partial discharge signals using neural networks and minimum distance classification”, IEEE Transactions on Electrical Insulation, Vol. 28, pp. 1016-1024, 1993. https://doi.org/10.1109/14.249375
  10. Contin A., Cavallini A., Montanari G. C., Pasini G., Puletti F., “Digital detection and fuzzy classification of partial discharge signals”, IEEE Trans. On Dielectrics and Electrical Insulation, Vol. 9, No. 2, pp. 335-348, 2002. https://doi.org/10.1109/TDEI.2002.1007695
  11. Sharkawy, R. M., Mangoubi, R.S., Abdel-Galil, T. K., Salama, M. M. A., Bartnikas, R., “SVM Classification of Contaminating Particles in Liquid Dielectrics Using Higher Order Statistics of Electrical and Acoustic PD Measurements”, IEEE Transactions on Dielectrics and Electrical Insulation Vol. 14, No. 3, pp. 669-678, 2007 . https://doi.org/10.1109/TDEI.2007.369530
  12. L. Hao, P. L. Lewin, “Partial Discharge Source Discrimination using a Support Vector Machine”, IEEE Transactions on Dielectrics and Electrical Insulation, Vol. 17, No. 1, pp. 189-197, 2010. https://doi.org/10.1109/TDEI.2010.5412017
  13. Tang Ju, Zhou Qian, Tang Ming, Xie Yanbin, “Study on Mathematical Model for VHF Partial Discharge of Typical Insulated Defects in GIS”, IEEE Transactions on Dielectrics and Electrical Insulation, Vol. 14, No. 1, pp. 30-38, 2007. https://doi.org/10.1109/TDEI.2007.302869
  14. Zhang Xiaoxing Tang Ju Peng Wenxiong Meng Yanhui Sun Caixin, “Study on the Outer UHF Microstrip Patch Antenna for Partial Discharge Detection in GIS”, Chinese Journal of Scientific Instrument, Vol. 27, No. 12, pp. 1595-1599, 2006.
  15. V. N. Vapnik, The Nature of Statistical Learning Theory, Springer-Verlag Inc., 1995.
  16. Nello Cristianini, John Shawe-Taylor, Royal Holloway, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, Cambridge University Press, Inc., Cambridge, 2000.
  17. Scott Kirkpatrick, "Optimization by Simulated Annealing: Quantitative Studies", Journal of Statistical Physics, Vol. 34, pp. 975-986, 1984. https://doi.org/10.1007/BF01009452
  18. Longhan Cao, Shanquan Zhou, Rui Li, Fan Wu, Tao Liu, “Application of optimizing the parameters of SVM using genetic simulated annealing algorithm”, WCICA 2008. 7th World Congress on Intelligent Control and Automation, pp. 5381-5385, 2008.
  19. BAI Dong-ying, WANG Xiao-dan, MA Fei, " SVM-based Incremental Learning Method and its Application", Aeronautical Computing Technique, Vol. 37 No. 4, pp. 23-26, 2007.
  20. Joseph Vardi and Benjamin Avi-Itzhak, Electric Energy Generation; Economics, Reliability and Rates: MIT, 1981, p.75-94.

Cited by

  1. Using SF6 Decomposed Component Analysis for the Diagnosis of Partial Discharge Severity Initiated by Free Metal Particle Defect vol.10, pp.8, 2017, https://doi.org/10.3390/en10081119
  2. Failure Prognosis of High Voltage Circuit Breakers with Temporal Latent Dirichlet Allocation vol.10, pp.12, 2017, https://doi.org/10.3390/en10111913
  3. Feature Selection for Partial Discharge Severity Assessment in Gas-Insulated Switchgear Based on Minimum Redundancy and Maximum Relevance vol.10, pp.10, 2017, https://doi.org/10.3390/en10101516
  4. Feature extraction of GIS partial discharge signal based on S-transform and singular value decomposition vol.11, pp.2, 2017, https://doi.org/10.1049/iet-smt.2016.0255