基于WPD-PSO算法的整流电路故障诊断  

Fault Diagnosis of Rectifier Circuit Based on WPD-PSO Algorithm

作　　者：王瀚 周海峰[1,2] 郑东强 林忠华[3] 张兴杰[4] 关天敏[5] WANG Han;ZHOU Haifeng;ZHENG Dongqiang;LIN Zhonghua;ZHANG Xingjie;GUAN Tianmin(School of Marine Engineering,Jimei University,Xiamen 361021,China;Fujian Province Key Laboratory of Naval Architecture and Ocean Engineering,Xiamen 361021,China;School of Marine Equipment and Mechanical Engineering,Jimei University,Xiamen 361021,China;Navigation College,Jimei University,Xiamen 361021,China;School of Marine Information Engineering,Jimei University,Xiamen 361021,China)

机构地区：[1]集美大学轮机工程学院,福建厦门361021 [2]福建省船舶与海洋工程重点实验室,福建厦门361021 [3]集美大学海洋装备与机械工程学院,福建厦门361021 [4]集美大学航海学院,福建厦门361021 [5]集美大学海洋信息工程学院,福建厦门361021

出　　处：《集美大学学报（自然科学版）》2022年第3期253-259,共7页Journal of Jimei University：Natural Science

基　　金：国家自然科学基金项目(51179074);福建省自然科学基金项目(2021J01839,2018J01495);集美大学安麦信产学研项目(S20127);福建省教育厅项目(JAT200242,JAT170318)。

摘　　要：针对电力电子电路故障类型多、诊断正确率低的问题,提出基于小波包分解和粒子群算法优化概率神经网络的方法。建立三相桥式全控整流电路仿真模型,利用小波包分解技术对故障电压信号进行三层小波包分解与重构,提取特征值,并对数据进行归一化处理;用粒子群算法优化概率神经网络寻找合适的平滑因子,对数据进行训练和诊断;将该方法与未优化的概率神经网络作对比。仿真结果表明,该方法在训练效果和诊断正确率上都要优于未优化的概率神经网络。Aiming at the problem of multiple fault types and low diagnostic accuracy of power electronic circuits,a method of optimizing probabilistic neural network based on wavelet packet decomposition and particle swarm optimization was proposed.Firstly,the simulation model of three-phase bridge rectifier circuit was established,and the fault voltage signal was decomposed and reconstructed by three-layer wavelet packet decomposition technique,and the characteristic values were extracted and normalized.Then PSO was used to optimize the probabilistic neural network to find the appropriate smoothing factor,and the data was trained and diagnosed.Finally,compared with the unoptimized probabilistic neural network,the simulation results show that the method is superior to the unoptimized probabilistic neural network on both training effect and diagnosis accuracy.

关 键 词：电力电子电路 故障诊断 小波包分解 粒子群算法 平滑因子 概率神经网络 

分 类 号：TN711[电子电信—电路与系统] TP183[自动化与计算机技术—控制理论与控制工程]