基于IPPA优化PNN的变压器故障诊断研究

Research on transformer fault diagnosis based on IPPA optimization PNN

针对变压器故障诊断精度低的问题,本文提出一种改进寄生捕食算法(IPPA)优化概率神经网络(PNN)的电力变压器故障诊断模型,首先利用主成分分析(PCA)对故障数据进行数据降维减少无效特征,然后利用混沌反向学习,柯西变异算子和融合贝塔分布的线性递减函数的权重等多策略改进寄生捕食算法(IPPA),提高其优化能力,并使用改进后的IPPA算法优化PNN网络的平滑因子,以提高PNN的分类精度和鲁棒性。最后将PCA处理后的数据输入到IPPA-PNN模型中进行故障诊断并以变压器数据为依据进行测试,测试结果表明,IPPA-PNN模型准确率达到93%相比于PPA-PNN和PSO-PNN模型提高了7%和10%能够有效地提高变压器的故障诊断性能。

Aiming at the problem of low accuracy of transformer fault diagnosis,this paper proposes a power transformer fault diagnosis model based on improved parasitic predation algorithm(IPPA)and optimized probabilistic neural network(PNN).Firstly,principal component analysis(PCA)is used to reduce the dimensionality of fault data to reduce invalid features,then use multiple strategies such as chaotic reverse learning,Cauchy mutation operator and the weight of linear decreasing function fused with beta distribution to improve the hunt-prey algorithm(IPPA)and its optimization ability,and use the improved IPPA algorithm to optimize the smoothing factor of the PNN network to improve the classification accuracy and robustness of the PNN.Finally,the PCA-processed data is input into the IPPA-PNN model for fault diagnosis and testing based on the transformer data.The test results show that the accuracy of the IPPA-PNN model reaches 93%,which is 7%and 10%higher than that of the PPA-PNN and PSO-PNN models,and can effectively improve the fault diagnosis performance of the transformer.