基于IPSO-BP神经网络的变压器故障诊断方法

Transformer Fault Diagnosis Method Based on IPSO-BP Neural Network

标准粒子群优化(PSO)算法对惯性权重采取简单的线性衰减方案,无法获得全局最优点.为了弥补该方法的缺陷,提出了一种改进的粒子群优化(IPSO)算法,并将该算法与误差反向传播神经网络(BPNN)相结合,进而提出一种基于IPSO-BPNN的变压器故障诊断新方法.该方法将单个粒子连续被选为最优解的次数作为自适应变量,并根据粒子的性能分类结果,自适应地调整各粒子的惯性权重,从而达到平衡局部和全局搜索能力的目的.大量仿真表明该算法性能明显优于基于BPNN和PSO-BPNN的变压器故障诊断系统,变压器故障诊断正确率更高.

Standard particle swarm optimization （PSO） algorithm just takes simple linear attenuation way to update the inertia weight, so it can not get the global optimum value. In order to solve this problem, in the paper, an improved particle swarm optimization （IPSO） algorithm is proposed, which is combined with the error back propagation neural network （BPNN）, then a new transformer fault diagnosis method based on IPSO-BPNN is presented. The method gets the number of times for which the individual particle is continuously selected as the optimal point, which is taken as an adaptive variable and is used to adaptively adjust the inertia weight along with the particle＇s performance classification, so as to balance the local and global search capabilities. A large number of simulation shows that the algorithm is better than the BPNN and PSO-BPNN based transformer fault diagnosis system, and it can get a higher correct rate of transformer fault diagnosis.