A Fault Detection and Location Strategy for Sub-module Open-circuit Fault in Modular Multilevel Converters Based on Random Forest Binary Classifier

被引：0

作者：

Yang H. ^{[1
]}

Xing W. ^{[1
]}

Chen C. ^{[1
]}

Zhang W. ^{[2
]}

Li C. ^{[3
]}

Xiang X. ^{[1
]}

Li W. ^{[1
]}

机构：

[1] Zhejiang University, Zhejiang Province, Hangzhou

[2] Inner Mongolia Power Research Institute, Inner Mongolia, Hohhot

[3] École Polytechnique Fédérale de Lausanne (EPFL), Lausanne

来源：

Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2023年 / 43卷 / 10期

基金：

中国国家自然科学基金;

关键词：

binary classifier; modular multilevel converter (MMC); open-circuit fault detection; random forest; time-domain feature extraction;

D O I：

10.13334/j.0258-8013.pcsee.221115

中图分类号：

学科分类号：

摘要：

Open-circuit fault of sub-module attracts more attention with the increasing applications of modular multilevel converter (MMC) in high-voltage direct-current (HVDC). This paper proposes a fault detection and location strategy based on machine learning to diagnose the open-circuit fault of sub-module in a modular multilevel converter. According to the open-circuit fault characteristics, the sub-module capacitor voltages are selected as the key indicator for fault detection and location. With feature extraction, the proposed strategy constructs a random forest binary classifier to discriminate the working state of each sub-module. The strategy can locate the faulty sub-module quickly and accurately without extra sensors or mathematical models of the circuit. In order to verify the effectiveness of this strategy, a three-phase MMC prototype is built for data collection. ©2023 Chin.Soc.for Elec.Eng.

引用

页码：3916 / 3927

页数：11

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