Based on PSO-BP Network Algorithm for Fault Diagnosis of Power Transformer
Dissolved gas analysis is an effective method for the early detection of incipient fault in power transformers. To improve the capability of interpreting the result of dissolved gas analysis, a technology is proposed in this paper. The Particle Swarm Optimization (PSO) technique is used to integrate with Back Propagation (BP) neural networks, and using particle swarm to optimize the networks weights and biases, the fault of transformers is simulated and discussed. The results show that the accuracy of PSO-BP method is significantly higher than that of the conventional three-ratio method. So the Algorithm based on PSO-BP network model provides a more accurate, safe and reliable result for the fault diagnosis of transformers.
component transformer fault diagnosis dissolved gas-in-oil analysis particle swarm optimization algorithm
Hairu Li DaowuYang ZhuoRen ZhewenLi
School of Chemical and Biological Engineering Changsha University of Science and Technology Changsha Ultrahigh Voltage Power Transmission and Transformer Company Changsha 410100, P.R.China
国际会议
长春
英文
484-487
2010-08-24（万方平台首次上网日期，不代表论文的发表时间）