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Algorithms
Special Issues
AI-Driven Control and Optimization in Power Electronics
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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 1107

Special Issue Editors

Prof. Dr. Danny Ochoa

E-Mail Website
Guest Editor
Faculty of Engineering, Universidad de Cuenca, Cuenca 010107, Ecuador
Interests: renewable energy integration; power systems; microgrids
Prof. Dr. Wilian Paul Arevalo Cordero

E-Mail Website
Guest Editor
Department of Electrical Engineering, University of Jaen, 23700 Linares, Spain
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 1800 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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Published Papers (1 paper)

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Research

36 pages, 6566 KB  
Article
Algorithmic Optimal Control of Screw Compressors for Energy-Efficient Operation in Smart Power Systems
by Kassym Yelemessov, Dinara Baskanbayeva, Leyla Sabirova, Nikita V. Martyushev, Boris V. Malozyomov, Tatayeva Zhanar and Vladimir I. Golik
Algorithms 2025, 18(9), 583; https://doi.org/10.3390/a18090583 - 14 Sep 2025
Viewed by 815
Abstract
This work presents the results of a research study focused on the development and evaluation of an algorithmic optimal control framework for energy-efficient operation of screw compressors in smart power systems. The proposed approach is based on the Pontryagin maximum principle (PMP), which [...] Read more.
This work presents the results of a research study focused on the development and evaluation of an algorithmic optimal control framework for energy-efficient operation of screw compressors in smart power systems. The proposed approach is based on the Pontryagin maximum principle (PMP), which enables the synthesis of a mathematically grounded regulator that minimizes the total energy consumption of a nonlinear electromechanical system composed of a screw compressor and a variable-frequency induction motor. Unlike conventional PID controllers, the developed algorithm explicitly incorporates system constraints, nonlinear dynamics, and performance trade-offs into the control law, allowing for improved adaptability and energy-aware operation. Simulation results obtained using MATLAB/Simulink confirm that the PMP-based regulator outperforms classical PID solutions in both transient and steady-state regimes. Experimental tests conducted in accordance with standard energy consumption evaluation methods showed that the proposed PMP-based controller provides a reduction in specific energy consumption of up to 18% under dynamic load conditions compared to a well-tuned basic PID controller, while maintaining high control accuracy, faster settling, and complete suppression of overshoot under external disturbances. The control system demonstrates robustness to parametric uncertainty and load variability, maintaining a statistical pressure error below 0.2%. The regulator’s structure is compatible with real-time execution on industrial programmable logic controllers (PLCs), supporting integration into intelligent automation systems and smart grid infrastructures. The discrete-time PLC implementation of the regulator requires only 103 arithmetic operations per cycle and less than 102 kB of RAM for state, buffers, and logging, making it suitable for mid-range industrial controllers under 2–10 ms task cycles. Fault-tolerance is ensured via range and rate-of-change checks, residual-based plausibility tests, and safe fallbacks (baseline PID or torque-limited speed hold) in case of sensor faults. Furthermore, the proposed approach lays the groundwork for hybrid extensions combining model-based control with AI-driven optimization and learning mechanisms, including reinforcement learning, surrogate modeling, and digital twins. These enhancements open pathways toward predictive, self-adaptive compressor control with embedded energy optimization. The research outcomes contribute to the broader field of algorithmic control in power electronics, offering a scalable and analytically justified alternative to heuristic and empirical tuning approaches commonly used in industry. The results highlight the potential of advanced control algorithms to enhance the efficiency, stability, and intelligence of energy-intensive components within the context of Industry 4.0 and sustainable energy systems. Full article
(This article belongs to the Special Issue AI-Driven Control and Optimization in Power Electronics)
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