Features Selection of SVM and ANN Using Particle Swarm Optimization for Power Transformers Incipient Fault Symptom Diagnosis

For the purpose of incipient power transformer fault symptom diagnosis, a successful adaptation of the particle swarm optimization (PSO) algorithm to improve the performances of Artificial Neural Network (ANN) and Support Vector Machine (SVM) is presented in this paper. A PSO-based encoding technique is applied to improve the accuracy of classification, which removed redundant input features that may be confusing the classifier. Experiments using actual data demonstrated the effectiveness and high efficiency of the proposed approach, which makes operation faster and also increases the accuracy of the classification.

[1]  Sayan Mukherjee,et al.  Choosing Multiple Parameters for Support Vector Machines , 2002, Machine Learning.

[2]  Ieee Standards Board.  IEEE guide for the interpretation of gases generated in oil-immersed transformers , 1992 .

[3]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[4]  L. L. Lai,et al.  A fuzzy dissolved gas analysis method for the diagnosis of multiple incipient faults in a transformer , 2000 .

[5]  Y. C. Huang,et al.  A New Data Mining Approach to Dissolved Gas Analysis of Oil-Insulated Power Apparatus , 2002, IEEE Power Engineering Review.

[6]  Asoke K. Nandi,et al.  FAULT DETECTION USING SUPPORT VECTOR MACHINES AND ARTIFICIAL NEURAL NETWORKS, AUGMENTED BY GENETIC ALGORITHMS , 2002 .

[7]  Michel Duval,et al.  Interpretation of gas-in-oil analysis using new IEC publication 60599 and IEC TC 10 databases , 2001 .

[8]  John F. Roddick,et al.  A tutorial on meta-heuristics for optimization , 2004 .

[9]  P. Purkait,et al.  An expert system for fault diagnosis in transformers during impulse tests , 2000, 2000 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.00CH37077).

[10]  Bernhard Schölkopf,et al.  Feature selection for support vector machines by means of genetic algorithm , 2003, Proceedings. 15th IEEE International Conference on Tools with Artificial Intelligence.

[11]  B. Samanta,et al.  Gear fault detection using artificial neural networks and support vector machines with genetic algorithms , 2004 .

[12]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[13]  R. Rogers.  IEEE and IEC Codes to Interpret Incipient Faults in Transformers, Using Gas in Oil Analysis , 1978, IEEE Transactions on Electrical Insulation.