Al-Rafidain Engineering Journal (AREJ)

Detection and Diagnosis of Inter-Turn Short Circuit Faults of PMSM for Electric Vehicles Based on Deep Reinforcement Learning

Document Type : Research Paper

Authors

Electrical Engineering Department, Collage of Engineering, University of Mosul, Mosul, Iraq

Abstract

The fault diagnosis of electric vehicle motors is one of the exciting topics, and machine learning-based artificial intelligence proved its worth in this field. The primitive methods of machine learning, such as the support vector machine (SVM) and Artificial nural network (ANN) suffered from feature extraction problems, the efficiency of the system depended on the quality of these extracted features until deep learning and deep neural networks came to solve This problem, Although the efficient performance of the deep neural network, it needs excellent experience in selecting parameters and building the structure of the neural network. The emergence of deep reinforcement learning, capable of handling raw data directly and constructing end-to-end systems to link raw fault data with its corresponding mode, represents a significant advancement in the field of machine learning. Furthermore, deep reinforcement learning exhibits greater intelligence compared to previous methods. In this Research, Deep reinforcement learning will be applied in diagnosing inter-turn short circuit faults and finding the level of fault in the built-in wheel permanent magnet synchronous motor of electric vehicles. The proposed method proved highly effective for detecting faults, with an efficiency of 99.9 % and has a promising future in building a general system capable of predicting faults in the early stages.

Keywords

Main Subjects

References
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Al-Rafidain Engineering Journal (AREJ)
Volume 28, Issue 2
September 2023
Page 75-85
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