基于CMPSO-BP算法的变压器故障诊断

Power Transformer Fault Diagnosis Based on Modified CMPSO-BP Algorithm

将基于遗传算法交叉变异思想的改进粒子群优化(CMPSO)算法和误差反向传播(BP)算法相结合构成的CMPSO-BP混合算法用于训练神经网络,该混合算法有效克服常规BP和PSO-BP算法独立训练神经网络的缺陷,并应用于变压器溶解气体分析的智能故障诊断实验。诊断结果表明,CMPSO-BP混合算法较BP及PSO-BP算法具有较高的诊断准确率。

Aiming at the imperfection and limitation of the conventional transformer fault diagnosis method in practical applications, the hybrid algorithm which combines the improved particle swarm optimization algorithm based on crossover and mutation （CMPSO） thought with error back. propagation （BP） algorithm is used to train neural network. The hybrid algorithm can effectively avoid the defects of independently training neural network in conventional BP algorithm and PSO algorithm, and can be used to analyze dissolved gas in transformer for intelligent fault diagnosis. The experimental results showed that CMPSO - BP hybrid algorithm gains higher diagnosis accuracy.