摘要
为了提高断路器故障状态诊断的准确性,精准识别故障,提出一种基于互信息特征选择和改进猎食者算法优化概率神经网络的高压断路器故障诊断方法(MI-ECHPO-PNN)。利用变分模态分解振动信号,选择其中频率较高的分量提取故障特征,利用互信息算法进行特征筛选,作为诊断模型的输入;运用改进的猎食者算法优化概率神经网络的平滑因子,将优化后的参数输入概率神经网络搭建ECHPO-PNN故障诊断模型。仿真结果表明:ECHPO-PNN模型相比其他PNN模型,诊断效果更好,准确率可达100%,具有良好的准确性和稳定性。
In order to improve the accuracy of fault diagnosis of circuit breakers and realize accurate fault identification,this paper proposes a fault diagnosis method of high voltage circuit breakers(MI-ECHPO-PNN)based on mutual information feature selection and improved prey algorithm optimized probabilistic neural network.After the vibration signal is decomposed by variable mode decomposition,the components with higher frequency is selected to extract the fault feature,and the feature is screened by the mutual information algorithm as the input of the diagnosis model.Using the improved predator algorithm to optimize the smoothing factor of the probabilistic neural network,the optimized parameters are input into the probabilistic neural network to build an ECHPO-PNN fault diagnosis model.The simulation results show that the ECHPO-PNN model has better diagnostic effect than other PNN models do,and the accuracy can reach 100%,showing good accuracy and stability.
作者
张莲
贾浩
赵梦琪
张尚德
季鸿宇
李多
ZHANG Lian;JIA Hao;ZHAO Mengqi;ZHANG Shangde;JI Hongyu;LI Duo(Chongqing Energy Internet Engineering Technology Research Center,Chongqing 400054,China;School of Electrical and Electronic Engineering,Chongqing University of Technology,Chongqing 400054,China)
出处
《重庆理工大学学报（自然科学）》
北大核心
2023年第7期265-271,共7页
Journal of Chongqing University of Technology：Natural Science
基金
国家自然科学基金项目(61402063)。
关键词
高压断路器
故障诊断
互信息算法
猎食者算法
概率神经网络
high voltage circuit breaker
fault diagnosis
mutual information algorithm
predator algorithm
probabilistic neural network
