Ms. Yan Zhang Profile

Yan Zhang

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Proceedings Article | 9 July 2011 Paper

Transformer fault prediction based on particle swarm optimization and SVM

Yan Zhang, Bide Zhang

Proceedings Volume 8009, 80090U (2011) https://doi.org/10.1117/12.896401

KEYWORDS: Transformers, Gases, Particle swarm optimization, Data modeling, Particles, Statistical modeling, Error analysis, Legal, Performance modeling, Neural networks

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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.

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