机理数据混合驱动的多相环形无刷励磁系统旋转整流器故障诊断  

Hybrid Mechanism-Data-Driven Diagnosis of Rotating Diode Fault in Multiphase Annular Brushless Excitation Systems

作　　者：蔡宇昂 郝亮亮[1] 周艳真 段贤稳 王光[4] Cai Yuang;Hao Liangliang;Zhou Yanzhen;Duan Xianwen;Wang Guang(School of Electrical Engineering Beijing Jiaotong University,Beijing 100044 China;Department of Electrical Engineering Tsinghua University,Beijing 100084 China;China Nuclear Power Operations Co.Ltd,Shenzhen 518172 China;Nanjing NR Electric Co.Ltd,Nanjing 211102 China)

机构地区：[1]北京交通大学电气工程学院,北京100044 [2]清华大学电机工程与应用电子技术系,北京100084 [3]中广核核电运营有限公司,深圳518172 [4]南京南瑞继保电气有限公司,南京211102

出　　处：《电工技术学报》2025年第8期2643-2655,共13页Transactions of China Electrotechnical Society

基　　金：中央高校基本科研业务费专项资金项目(2023YJS162);中央高校基本科研业务费专项资金项目(2020JBM070);中广核集团公司科技项目(3100077013)资助。

摘　　要：旋转整流器故障诊断对多相环形无刷励磁系统的安全运行具有重要意义。而旋转整流器故障种类繁多且故障特征微弱,机理驱动的诊断方案具有较好的可解释性,但难以实现全故障的准确诊断;数据驱动的诊断方案快速准确,但面向应用时训练和调试难度大。因此,提出一种机理与数据混合驱动的旋转整流器故障诊断方案:首先根据故障机理推导故障后励磁电流的频域特征,利用有限元仿真确定机理诊断模型的阈值;然后引入快速动态时间规整(Fast-DTW)算法计算励磁电流时域波形相似度,结合k近邻(kNN)算法分类器得到数据驱动模型;最后利用集成学习的思想,融合机理与数据诊断方案。实验结果表明,混合驱动的诊断方案能够在降低训练难度的情况下,实现旋转整流器故障的准确诊断。The rotating rectifier is the key part of multiphase annular brushless excitation systems.Nevertheless,the rectifiers often experience faults caused by diode failures,which brings security risks in practice.Accurately diagnosing faults in the rotating rectifier is pivotal for ensuring the safe operation of multiphase annular brushless excitation systems.However,the types of rotating rectifier faults are diverse,and the characteristics of different faults are inherently weak.Traditional mechanism-driven diagnostic schemes offer interpretability but often struggle with precise fault diagnosis.New data-driven diagnostic schemes exhibit speed and accuracy but encounter challenges in training and debugging in practical applications.This paper proposes a hybrid mechanism-data-driven diagnostic scheme for rotating rectifier faults.Based on the fault mechanism,the frequency domain characteristics of the excitation current after the fault are derived,and the fault characteristic patterns are summarized.Then,thresholds of the mechanism diagnosis model are calculated using finite element simulation data.Extracting the frequency domain characteristics of the excitation current allows the fault mechanism to be clearly described,thus providing a solid foundation for subsequent fault diagnosis.The current waveform under normal operation and different fault conditions can be simulated by adjusting the models,which allows for determining thresholds for various operating conditions.Then,the fast dynamic time warping(Fast-DTW)algorithm is introduced to calculate the similarity of excitation current time-domain waveforms,subsequently forming a data-driven model combined with the k-nearest neighbors(kNN)classifier.The fast-DTW algorithm can align waveforms of different time lengths and start points to capture subtle differences between waveforms.By combining the fast-DTW algorithm with the kNN classifier,the data-driven model can realize the diagnosis of rotating rectifier faults.Mechanism-driven and data-driven diagnostic schemes ar

关 键 词：旋转整流器故障 机理驱动 数据驱动 快速动态时间规整(Fast-DTW) 集成学习 

分 类 号：TM341[电气工程—电机]