Transformer fault prediction based on particle swarm optimization and SVM

Yan Zhang; Bide Zhang

doi:10.1117/12.896401

8 July 2011 Transformer fault prediction based on particle swarm optimization and SVM

Yan Zhang, Bide Zhang

Proceedings Volume 8009, Third International Conference on Digital Image Processing (ICDIP 2011); 80090U (2011) https://doi.org/10.1117/12.896401
Event: 3rd International Conference on Digital Image Processing, 2011, Chengdu, China

Abstract

Forecasting of dissolved gases content in power transformer oil is very significant to detect incipient failures of transformer early and ensure normal operation of entire power system. Forecasting of dissolved gases content in power transformer oil is a complicated problem due to its nonlinearity and the small quantity of training data. Support vector machine (SVM) has been successfully employed to solve regression problem of nonlinearity and small sample. However, it is different to choice the best parameters of the SVM ,In this study, support vector machine is proposed to forecast dissolved gases content in power transformer oil, among which Particle Swarm Optimization (PSO) are used to determine free parameters of support vector machine. The experimental data from the electric power company in Sichuan are used to illustrate the performance of proposed PSO-SVM model. The experimental results indicate that the proposed PSO-SVM model can achieve greater forecasting accuracy than grey model (GM) under the circumstances of small sample. Consequently, the PSO-SVM model is a proper alternative for forecasting dissolved gases content in power transformer oil.

Citation Download Citation

Yan Zhang and Bide Zhang "Transformer fault prediction based on particle swarm optimization and SVM", Proc. SPIE 8009, Third International Conference on Digital Image Processing (ICDIP 2011), 80090U (8 July 2011); https://doi.org/10.1117/12.896401

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