AI-Driven Control and Optimization in Power Electronics
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".
Deadline for manuscript submissions: 31 January 2026 | Viewed by 23
Special Issue Editors
Interests: renewable energy integration; power systems; microgrids
Interests: advancements in energy storage technologies; innovations in electrical protection systems; power quality enhancement strategies; power smoothing techniques in electrical networks; batteries
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Power electronics are essential in contemporary energy systems, enabling the efficient conversion, control, and integration of renewable sources. As these systems become increasingly complex—particularly in smart grids, electric mobility, and distributed generation—advanced strategies for control and optimization are becoming more necessary.
Artificial Intelligence (AI) offers a diverse range of tools that enhance performance, adaptability, and system responsiveness. Techniques such as machine learning, deep learning, reinforcement learning, and bio-inspired optimization have shown considerable potential in managing nonlinear behavior, uncertainty, and real-time operational requirements in power electronics.
This Special Issue seeks contributions that focus on recent algorithmic developments and AI applications related to the modeling, control, and optimization of power electronic converters, flexible AC transmission systems (FACTS), and intelligent control systems. We welcome theoretical studies, practical implementations, and case-based research that illustrate the integration of AI into power electronic applications.
The scope of this Special Issue includes, but is not limited to, the following topics:
- AI-based control of power converters (DC-DC, DC-AC, multilevel inverters, etc.);
- Predictive and adaptive control strategies using machine learning;
- AI-driven design and optimization of FACTS devices (e.g., STATCOM, UPFC, TCSC);
- Reinforcement learning and neural networks for real-time control;
- Fault detection, classification, and predictive maintenance in power electronic systems;
- Optimization algorithms for energy efficiency and thermal management;
- Digital twins and intelligent monitoring systems for power electronics;
- Applications in smart grids, microgrids, EV charging, and renewable energy integration.
Prof. Dr. Danny Ochoa
Prof. Dr. Wilian Paul Arevalo Cordero
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- power electronics
- artificial intelligence
- intelligent control
- optimization algorithms
- FACTS
- machine learning
- reinforcement learning
- smart energy systems
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