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文章摘要
基于小波神经网络与KNN机器学习算法的六相永磁同步电机故障态势感知方法
Fault State Perception Method for Six Phase PMSM Based on Wavelet Neural Network and KNN Machine Learning Algorithm
Received:November 28, 2017  Revised:November 28, 2017
DOI:
中文关键词: 永磁同步电机  神经网络  机器学习  小波包分解  故障态势感知
英文关键词: permanent  magnet synchronous  motor (PMSM), Neural  network, machine  learning, wavelet  packet decomposition, fault  state perception
基金项目:
Author NameAffiliationE-mail
Zhang Haoyu Key Laboratory of Control of Power Transmission and Conversion Ministry of Education,Shanghai Jiao Tong University zhy380534537@sjtu.edu.cn 
Yao Gang* Key Laboratory of Control of Power Transmission and Conversion Ministry of Education,Shanghai Jiao Tong University yaogangth@sjtu.edu.cn 
Yin Zhizhu Shanghai Electric Group Co., Ltd., Central Academe yinzhzh@shanghai-electric.com 
Zhou Lidan Key Laboratory of Control of Power Transmission and Conversion Ministry of Education,Shanghai Jiao Tong University zhoulidan@sjtu.edu.cn 
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中文摘要:
      为了避免六相永磁同步电机在运行过程中因缺相引发更严重电机故障和系统崩坏,需对电机在故障发生前进行提前预测判断和在故障发生后识别故障类型。本文根据故障下定子磁动势不变原理,推导Y移30°中性点隔离型六相永磁同步电机在各缺相故障下的数学模型。通过小波包分析方法提取故障工况下的特征值,构建小波神经网络模型对故障发生进行预测判断,避免系统误触发;构建KNN机器学习系统,对故障类型进行快速识别,以实现对故障态势的感知。利用MATLAB软件和Python的Scikit-Learn机器学习库进行仿真实验,对比验证该方法在六相永磁同步电机故障态势感知中可靠有效。
英文摘要:
      In order to avoid the more serious motor fault and system breakdown which were caused by open phase during six-phase permanent magnet synchronous motor (PMSM) running, it is necessary to do the fault prediction and diagnosis. This paper derived mathematical model of neutral point isolation six-phase PMSM shifted by 30° with the principle of stator magnetomotive force invariance. Wavelet packet analysis was used to collect the feature values and wavelet neural network was built to do the fault prediction and avoid system spurious triggering. The K-Nearest Neighbor (KNN) machine learning system had also been built to diagnose the fault types quickly which could realize fault state perception. MATLAB and Scikit-Learn library of Python were used to do simulations which could verify the strategy reliable and effective.
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