基于无监督学习的MMC子模块开路故障诊断方法  

Diagnosis method for sub-module open-circuit fault in modular multilevel converter based on unsupervised learning

作　　者：张彼德 洪锡文 刘俊 蒋拯 刘铠 余海宁 ZHANG Bide;HONG Xiwen;LIU Jun;JIANG Zheng;LIU Kai;YU Haining(School of Electric Engineering and Electronic Information,Xihua University,Chengdu 610039,China;State Grid Chongqing Electric Power Company,Chongqing 400014,China)

机构地区：[1]西华大学电气与电子信息学院,四川成都610039 [2]国网重庆市电力公司,重庆400014

出　　处：《电力系统保护与控制》2021年第12期98-105,共8页Power System Protection and Control

基　　金：国家自然科学基金项目资助(61703345);四川省电力电子节能技术与装备重点实验室项目资助(SZJJ2015-064);四川省信号与信息处理重点实验室项目资助(SZJJ2017-049)。

摘　　要：模块化多电平换流器(MMC)子模块发生故障时,快速准确地检测并定位故障是提高换流器运行可靠性的关键。目前,机器学习在MMC故障诊断领域得到了一定的应用,但大多需要采集每种故障情况下的样本,而故障样本采集难度较大。针对此问题,提出一种无需采集故障样本,仅根据正常样本训练分类模型即可实现故障检测和定位的无监督故障诊断方法。首先,采用在线顺序极限学习机对变量预测模型进行改进,通过比较预测量与实际量的距离判断桥臂故障,实现故障检测。其次,以故障桥臂各子模块电容电压变化率为特征,通过K近邻异常值检测方法定位故障子模块。最后,搭建了三相五电平MMC仿真模型对所提方法进行了仿真研究。结果表明,与有监督的机器学习方法相比,所提方法在不需要故障样本集的情况下便能快速准确地检测并定位故障,为机器学习在MMC故障诊断实际工程中的应用提供了参考。When the sub-module of a MMC fails,detecting and locating the fault quickly and accurately is the key to improve its operational reliability.At present,machine learning has been applied to a certain extent in the field of MMC fault diagnosis,but most methods need to collect samples for each fault situation.This is difficult.Thus a fault diagnosis method based on unsupervised learning is proposed.This method avoids the difficulties of collecting fault samples,and only uses normal samples to train the classification model to achieve fault detection and location.First,the online sequential extreme learning machine is used to improve the variable prediction model,and the bridge arm fault is judged by comparing the distance between the predicted value and the actual value.Secondly,the change rates of the capacitance voltage of each sub-module of the bridge arm is extracted as features,and the faulty sub-module is located by the K-nearest neighbor abnormal value detection method.Finally,a three-phase five-level MMC simulation model is built to validate the proposed method.The results show that compared with the supervised machine learning method,the proposed method can quickly and accurately detect and locate faults without the need for fault sample sets.This provides a reference for the application of machine learning in the actual engineering of MMC fault diagnosis.

关 键 词：模块化多电平换流器 开路 故障诊断 无监督学习 机器学习 

分 类 号：TM46[电气工程—电器]