The Transactions of The Korean Institute of Electrical Engineers (전기학회논문지)

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

DOI QR Code

A Study on Three Phase Partial Discharge Pattern Classification with the Aid of Optimized Polynomial Radial Basis Function Neural Networks

최적화된 pRBF 뉴럴 네트워크에 이용한 삼상 부분방전 패턴분류에 관한 연구

  • Oh, Sung-Kwun (Dept. of Electrical Engineering, The University of Suwon) ;
  • Kim, Hyun-Ki (Dept. of Electrical Engineering, The University of Suwon) ;
  • Kim, Jung-Tae (Dept. of Electrical Information Systems, Daejin University)
  • 오성권 (수원대학교 전기공학과) ;
  • 김현기 (수원대학교 전기공학과) ;
  • 김정태 (대진대 공대 전기정보시스템공학과)
  • Received : 2012.09.17
  • Accepted : 2013.01.21
  • Published : 2013.04.01
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we propose the pattern classifier of Radial Basis Function Neural Networks(RBFNNs) for diagnosis of 3-phase partial discharge. Conventional methods map the partial discharge/noise data on 3-PARD map, and decide whether the partial discharge occurs or not from 3-phase or neutral point. However, it is decided based on his own subjective knowledge of skilled experter. In order to solve these problems, the mapping of data as well as the classification of phases are considered by using the general 3-PARD map and PA method, and the identification of phases occurring partial discharge/noise discharge is done. In the sequel, the type of partial discharge occurring on arbitrary random phase is classified and identified by fuzzy clustering-based polynomial Radial Basis Function Neural Networks(RBFNN) classifier. And by identifying the learning rate, momentum coefficient, and fuzzification coefficient of FCM fuzzy clustering with the aid of PSO algorithm, the RBFNN classifier is optimized. The virtual simulated data and the experimental data acquired from practical field are used for performance estimation of 3-phase partial discharge pattern classifier.

Keywords

References

  1. A. Kandal, L. Li, and Z. Cao, "Fuzzy Inference and Its Application to Control Systems," Fuzzy Sets and Systems, Vol. 48, No. 1, pp. 99-111, 1992. https://doi.org/10.1016/0165-0114(92)90254-2
  2. Z-B. Xu, H. Q, J. Peng, and B. Zhang, "A Comparative Study of Two Modeling Approaches in Neural Network," Neural Networks, Vol. 17, No. 1, pp. 73-85, 2004. https://doi.org/10.1016/S0893-6080(03)00192-8
  3. L. Sanchez, I. Couso, and J. Casillas, "Genetic Learning of Fuzzy Rules based on Low Quality Data," Fuzzy Sets and Systems, Vol. 160, No. 17, pp. 2524-2552, 2009. https://doi.org/10.1016/j.fss.2009.03.004
  4. S. Kiranyaz, T. Ince, A. Yildirim, and M. Gabbouj, "Evolutionary Artificial Neural Networks by Multi-dimensional Particle Swarm Optimization," Neural Networks, Vol. 22, No. 10, pp. 1448-1462, 2009. https://doi.org/10.1016/j.neunet.2009.05.013
  5. A. A. Frolov, D. Husek, I. P. Muraviev, P. Y. Polyakov, "A Boolean Factor Analysis by Attractor Neural Network," IEEE Trans. Neural Networks, Vol. 3, pp. 698-707, 2007.
  6. R. A. Aliev, B .G. Guirimov, B. Fazlollahi, and R. R. Aliev "Evolutionary Algorithm-based Learning of Fuzzy Neural Networks. part 2: Recurrent Fuzzy Neural Networks," Fuzzy Sets and Systems, Vol. 160, No. 17, pp. 2553-2566, 2009. https://doi.org/10.1016/j.fss.2008.12.018
  7. S. B. Roh, S. K. Oh, and W. Pedrycz, "A Fuzzy Ensemble of Parallel Polynomial Neural Networks with Information Granules formed by Fuzzy Clustering," Knowledge-Based Systems, Vol. 23, No. 3, pp. 202-219, 2010. https://doi.org/10.1016/j.knosys.2009.12.002
  8. James C. Bezdek, Robert Ehrlich, William Full "FCM: The Fuzzy C-Means Clustering Algorithm" Computers & Geosciences, Vol. 10, No. 2-3, pp. 191-203, 1984. https://doi.org/10.1016/0098-3004(84)90020-7
  9. S. P. Lloyd, "Least Squares Quantization in PCM," IEEE Trans. on Inf. Theory, Vol. 28, No. 2, pp. 129-137, 1982. https://doi.org/10.1109/TIT.1982.1056489
  10. R. R. Yager, D. P. Filev, "Approximate Clulstering via the Mountain Method," IEEE Trans. on S.M.C, Vol. 24, pp. 1279-1284, 1994. https://doi.org/10.1109/21.299710
  11. S. L. Chiu, "Fuzzy Model Identification Based on Cluster Estimation," Journal of Intelligent and Fuzzy Systems, Vol. 2, No. 3, 1994.
  12. J. Holland, "Adaptation In Natural and Artificial Systems," University of Michigan Press, 1975.
  13. J. Kennedy and R. Eberhart, "Particle Swarm Optimization," Proc. of IEEE International Conference on Neural Networks, Vol. 4, pp. 1942-1948, 1995.
  14. R. Heinrich, S. Schaper, W. Kalkner, R. Plath, A. Bethge, "Synchronous Three Phase Partial Discharge Detection on Rotating Machines," ISH, pp. 542, 2003.
  15. J. T. Kim, J. H. Kim, J. Y. Koo, "Noise Discriminations in Measuring Particle Discharges using Pulse Wave Shape Analysis," CMD, pp. 698. 2006.
  16. J. T. Kim, J. H. Kim, Y. M. Chang, J. Y. Koo, "Pulse Analysis Algorithms for Distinguishing Discharge Sources using Different Types of Sensors," IEEJ Trans. FM, Vol. 128, No. 5, pp. 350-356. 2008. https://doi.org/10.1541/ieejfms.128.350