Data-Driven Fault Classification for Non-Inverting Buck–Boost DC–DC Power Converters Based on Expectation Maximisation Principal Component Analysis and Support Vector Machine Approaches

Fu, Yichuan, Gao, Zhiwei, Wu, Haimeng, Yin, Xiuxia and Zhang, Aihua (2021) Data-Driven Fault Classification for Non-Inverting Buck–Boost DC–DC Power Converters Based on Expectation Maximisation Principal Component Analysis and Support Vector Machine Approaches. In: 1st IEEE International Power Electronics and Application Symposium, IEEE PEAS'2, 12-15 Nov 2021, Shanghai, China. (In Press)

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Abstract

Data-driven fault classification for power converter systems has been taking more into considerations in power electronics, machine drives, and electric vehicles. It is challenging to classify the different topologies of faults in the real time monitoring control systems. In this paper, a data-driven and supervised machine learning-based fault classification technique is adopted by combining and consolidating with Expectation Maximisation Principal Component Analysis (EMPCA) and Support Vector Machine (SVM) to substantiate the availability of fault classification. The proposed methodology is applied to the non-inverting Buck–Boost DC–DC power converter systems subjected to the incipient fault and serious fault, respectively. Finally, the feasibility of the approach is validated by intensive simulations and comparison studies.

Item Type: Conference or Workshop Item (Paper)
Additional Information: The authors would like to thank the research support from the National Nature Science Foundation of China (NNSFC) under Grant 61673074, and the Faculty of Engineering and Environment, Northumbria University, Newcastle upon Tyne, Tyne and Wear, NE1 8ST, United Kingdom.; 1st IEEE International Power Electronics and Application Symposium , IEEE PEAS'2021 ; Conference date: 12-11-2021 Through 15-11-2021
Uncontrolled Keywords: Data-driven, Fault classification, expectation maximisation principal component analysis, support vector machine, Buck–Boost DC–DC power converters
Subjects: G500 Information Systems
H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Related URLs:
Depositing User: John Coen
Date Deposited: 17 Sep 2021 14:25
Last Modified: 17 Sep 2021 14:30
URI: http://nrl.northumbria.ac.uk/id/eprint/47253

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