Application of Machine Learning in Power Electronics

Dear Colleagues,

The objective of this Special Issue, "Application of Machine Learning in Power Electronics", is to investigate the integration of modern machine learning (ML) methods in the area of power electronics. Machine learning offers effective methods to analyze the complex, non-linear, and dynamic behaviors that are characteristic in power electronic systems. This issue encourages papers that focus on the efficient use of machine learning techniques to improve the design, functioning, and reliability of power electronics applications, such renewable energy systems, electric vehicles, smart grids, power converters, and motor drives. Submissions demonstrating novel methods, modeling, or real applications in industries are especially appreciated.

Topics of interest :

• Design and optimization: Utilizing ML algorithms for design and optimization of power electronic converters and other electronics components.
• Control strategies: Developing intelligent control systems with ML for improving performance, stability, and adaptability.
• Fault detection and diagnosis: Implemented machine learning algorithms for early fault detection, anomaly identification, and system deterioration monitoring.
• Energy management: Applying machine learning to optimize energy consumption and integrate renewable energy sources.
• Predictive maintenance: Using machine learning to increase equipment durability.
• Advanced ML techniques: Applying advanced machine learning techniques such as deep learning, reinforcement learning, and other complex methodologies in the field of power electronics.
• Simulation and modeling: The application of data-driven modeling and simulation techniques to forecast the behaviors of systems under various conditions.
• Case studies and applications: Demonstrating practical applications and example methodologies in the power electronics field.

Prof. Dr. Manuela Panoiu
Prof. Dr. Caius Pǎnoiu
Guest Editors

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