Competitive learning for Self Organizing Maps used in classification of partial discharge

This paper presents some competitive learning algorithms for Self Organizing Map (SOM). The competitive learning algorithms showed to self organizing map algorithm are winner-takes-all, Frequency Sensitive Competitive Learning and Rival Penalized Competitive Learning. The result shows the performance in classification of partial discharge on power cables using SOM.

[1]  M. Salama,et al.  PD pattern recognition with neural networks using the multilayer perceptron technique , 1993 .

[2]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[3]  F. Klawonn,et al.  Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition , 1999 .

[4]  Esa Alhoniemi,et al.  Clustering of the self-organizing map , 2000, IEEE Trans. Neural Networks Learn. Syst..

[5]  Trevor Blackburn,et al.  Data Mining Techniques to Assess the Condition of High Voltage Electrical Plant , 2002 .

[6]  Juha Vesanto,et al.  Data exploration process based on the self-organizing map , 2002 .

[7]  Erkki Oja,et al.  Engineering applications of the self-organizing map , 1996, Proc. IEEE.

[8]  Hong-Tzer Yang,et al.  Application of self organizing map approach to partial discharge pattern recognition of cast-resin current transformers , 2008 .

[9]  David Pollard,et al.  Quantization and the method of k -means , 1982, IEEE Trans. Inf. Theory.

[11]  Cecilia Forssén.  Modelling of cavity partial discharges at variable applied frequency , 2008 .

[12]  Erkki Oja,et al.  Rival penalized competitive learning for clustering analysis, RBF net, and curve detection , 1993, IEEE Trans. Neural Networks.

[13]  Robert B. Ash,et al.  Information Theory , 2020, The SAGE International Encyclopedia of Mass Media and Society.

[14]  Hans Edin,et al.  Partial Discharges Studied with Variable Frequency of the Applied Voltage , 2001 .

[15]  T.R. Blackburn,et al.  Descriptive data mining of partial discharge using decision tree with genetic algorithm , 2008, 2008 Australasian Universities Power Engineering Conference.

[16]  Georg Pölzlbauer.  Survey and Comparison of Quality Measures for Self-Organizing Maps , 2004 .

[17]  N.F. Ab Aziz,et al.  Analysis of Partial Discharge Measurement Data Using a Support Vector Machine , 2007, 2007 5th Student Conference on Research and Development.

[18]  S. Tenbohlen,et al.  Detection and location of partial discharges in power transformers using acoustic and electromagnetic signals , 2008, IEEE Transactions on Dielectrics and Electrical Insulation.

[19]  H. Hirose,et al.  Diagnosis of electric power apparatus using the decision tree method , 2008, IEEE Transactions on Dielectrics and Electrical Insulation.

[20]  Sung-Kwun Oh,et al.  Partial Discharge Pattern Recognition Using Fuzzy-Neural Networks (FNNs) Algorithm , 2008, 2008 IEEE International Power Modulators and High-Voltage Conference.

[21]  Stanley C. Ahalt,et al.  Competitive learning algorithms for vector quantization , 1990, Neural Networks.

[22]  S.D.J. McArthur,et al.  Practical Applications of Data Mining in Plant Monitoring and Diagnostics , 2007, 2007 IEEE Power Engineering Society General Meeting.

[23]  Miin-Shen Yang.  A survey of fuzzy clustering , 1993 .

[24]  Jianming Lu,et al.  Combining Recurrent Neural Networks with Self-Organizing Map for Channel Equalization , 2002 .

[25]  Xu Guo-zheng,et al.  Study on Partial Discharge Localization by Ultrasonic Measuring in Power Transformer Based on Particle Swarm Optimization , 2008, 2008 International Conference on High Voltage Engineering and Application.

[26]  A. Krivda,et al.  Automated recognition of partial discharges , 1995 .