重庆理工大学学报(自然科学) ›› 2023, Vol. 37 ›› Issue (7): 265-271.

• 电气·电子 • 上一篇    下一篇

基于 MIECHPOPNN的高压断路器故障诊断研究

张 莲,贾 浩,赵梦琪   

  1. (1.重庆市能源互联网工程技术研究中心,重庆 400054; 2.重庆理工大学 电气与电子工程学院,重庆 400054)
  • 出版日期:2023-08-15 发布日期:2023-08-15
  • 作者简介:张莲,女,教授,主要从事远程测试与控制技术、信号处理等方面的研究,Emailzh_lian@cqut.edu.cn;贾浩,男, 硕士研究生,主要从事高压断路器在线监测与故障诊断等方面研究,Email:812690925@qq.com。

Research on fault diagnosis of high voltage circuit breakers based on MI-ECHPO-PNN

  • Online:2023-08-15 Published:2023-08-15

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。

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

Abstract: 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.

中图分类号: 

  • TM7561