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《集美大学学报（自然科学版）》[ISSN:1007-7405/CN:35-1186/N]

卷:

第27卷

期数:

2022年第3期

页码:

253-259

栏目:

船舶与机械工程

出版日期:

2022-05-28

文章信息/Info

Title:

Fault Diagnosis of Rectifier Circuit Based on WPD-PSO Algorithm

作者:

王瀚1; 2; 周海峰1; 2; 郑东强3; 林忠华3; 张兴杰4; 关天敏5

（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）

Author(s):

WANG Han1; 2; ZHOU Haifeng1; 2; ZHENG Dongqiang3; LIN Zhonghua3; ZHANG Xingjie4; GUAN Tianmin5

(1.School of Marine Engineering,Jimei University, Xiamen 361021, China;2.Fujian Province Key Laboratory of Naval Architecture and Ocean Engineering，Xiamen 361021，China;3.School of Marine Equipment and Mechanical Engineering，Jimei University，Xiamen 361021, China;4.Navigation College, Jimei University，Xiamen 361021, China;5.School of Marine Information Engineering, Jimei University, Xiamen 361021, China)

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

Keywords:

power electronic circuit; fault diagnosis; wavelet packet decomposition; particle swarm algorithm; smoothing factor; probabilistic neural network

分类号:

-

DOI:

-

文献标志码:

A

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。

Abstract:

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.

参考文献/References:

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更新日期/Last Update: 2022-07-12