Power electronics anomaly detection and diagnosis with machine learning and deep learning methods: A survey

Hossein Rahimighazvini 1, *, Zeyad Khashroum 1, Maryam Bahrami 2 and Milad Hadizadeh Masali 1

1 Department of Electrical Engineering, Lamar University, Beaumont, TX 77710, USA.
2 Department of Industrial Engineering, Lamar University, Beaumont, TX 77710, USA.
 
Review
International Journal of Science and Research Archive, 2024, 11(02), 730–739.
Article DOI: 10.30574/ijsra.2024.11.2.0428
Publication history: 
Received on 30 January 2024; revised on 13 March 2024; accepted on 16 March 2024
 
Abstract: 
Power electronics pertains to the conception, regulation, and utilization of electronic power circuits to proficiently administer and transform electrical energy. Power electronics play a crucial role in maintaining the reliability, efficiency, and security of complex production systems. Also, increasingly important in various applications such as renewable energy systems, electric vehicles, and industrial automation. However, modern power electronics systems are vulnerable to both cyber and physical anomalies due to the integration of information and communication technologies. So far, different methods have been used to detect abnormalities. This survey provides an overview of the state-of-the-art in anomaly detection in power electronics using machine learning and deep learning methods. It highlights the potential of these techniques in addressing the growing complexity and vulnerability of power electronics systems.
 
Keywords: 
Power electronics; Anomaly detection; Anomaly diagnosis; Machine learning; Deep learning
 
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