A new ensemble-based classifier for IGBT open-circuit fault diagnosis in three-phase PWM converter

Protection and Control of Modern Power Systems

Yang Xia¹  Bin Gou¹  Yan Xu¹ 

[1] Electric Power Research Laboratory, School of Electrical and Electronic Engineering, Nanyang Technological University;

关键词: IGBT open-circuit fault;    Extreme learning machine (ELM);    Data-driven method;    Ensemble structure;   

DOI  :  10.1186/s41601-018-0109-x

来源: DOAJ

【 摘 要 】

Abstract Three-phase pulse width modulation converters using insulated gate bipolar transistors (IGBTs) have been widely used in industrial application. However, faults in IGBTs can severely affect the operation and safety of the power electronics equipment and loads. For ensuring system reliability, it is necessary to accurately detect IGBT faults accurately as soon as their occurrences. This paper proposes a diagnosis method based on data-driven theory. A novel randomized learning technology, namely extreme learning machine (ELM) is adopted into historical data learning. Ensemble classifier structure is used to improve diagnostic accuracy. Finally, time window is defined to illustrate the relevance between diagnostic accuracy and data sampling time. By this mean, an appropriate time window is achieved to guarantee a high accuracy with relatively short decision time. Compared to other traditional methods, ELM has a better classification performance. Simulation tests validate the proposed ELM ensemble diagnostic performance.

【 授权许可】

Unknown