Automatic Control Strategy and Technology in Power Electronics

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Power Electronics".

Deadline for manuscript submissions: 15 June 2026 | Viewed by 1369

Special Issue Editors


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Guest Editor
School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
Interests: energy storage control; power system stabilization control; power generation control

Special Issue Information

Dear Colleagues,

Modern power electronic systems play a critical role in enabling efficient energy conversion, renewable energy integration, electric vehicles, and advanced industrial automation. The performance, reliability, and intelligence of these systems are largely determined by their control strategy and technology. With the increasing penetration of distributed energy resources, semiconductor devices, and digital control platforms, new challenges and opportunities are emerging in the modeling, design, and implementation of high-performance control systems for power electronics. Automatic control strategies and technologies—ranging from classical linear and nonlinear control to modern AI-enhanced and model-predictive strategies—are essential to achieve fast dynamic response, robust stability, high efficiency, and functional flexibility in power converters. However, the growing complexity, scalability, and interoperability requirements of modern applications such as smart grids, electric vehicles, and renewable energy systems introduce new control challenges related to system-level stability, cyber-physical security, and real-time adaptability.

This Special Issue aims to present cutting-edge research on advanced control strategies, design methodologies, and implementation technologies that address the evolving needs of next-generation power electronic systems. This aligns with the journal’s overarching mission of fostering new research in power electronics and their intersecting disciplines. We welcome original research and review articles that present innovative solutions, theoretical insights, and practical applications in this dynamically advancing field.

This Special Issue invites original research papers that address the challenges and opportunities in the above fields. The potential topics include, but are not limited to, the following:

  • Advanced control methods for power converters;
  • Digital control and real-time implementation;
  • Cyber-physical security and resilient control in power electronic systems;
  • Stability analysis and stabilization techniques in multi-converter systems;
  • AI and machine learning applied to control in power electronics;
  • Sensorless control and state estimation techniques;
  • Control strategies for wide-bandgap (SiC, GaN) semiconductor-based converters;
  • Grid-forming and grid-supporting converter control;
  • Control of electric motor drives and traction systems;
  • Modeling and control of power electronics in renewable energy systems.

We look forward to receiving your contributions.

Dr. Bingyu Wang
Dr. Rui Wang
Guest Editors

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Keywords

  • power converters
  • digital control system
  • power electronic systems
  • converter control
  • advanced control strategy
  • energy systems
  • sensorless techniques

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Published Papers (2 papers)

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Research

23 pages, 2363 KB  
Article
A Hierarchical Distributed Method for Source-Grid-Load-Storage Coordinated Power and Energy Balance in Distribution Networks
by Jingyu Liu, Zhe Ming, Hongyun Shi, Haihong Zhang and Zifa Liu
Electronics 2026, 15(5), 1054; https://doi.org/10.3390/electronics15051054 - 3 Mar 2026
Cited by 1 | Viewed by 436
Abstract
The integration of distributed energy resources into modern multi-energy distribution networks introduces complexity, necessitating effective coordinated control to maintain stability. This study proposes a hierarchical distributed control strategy combining distributed autonomy with centralized coordination. A “self-balancing and self-smoothing” tie-line power control method is [...] Read more.
The integration of distributed energy resources into modern multi-energy distribution networks introduces complexity, necessitating effective coordinated control to maintain stability. This study proposes a hierarchical distributed control strategy combining distributed autonomy with centralized coordination. A “self-balancing and self-smoothing” tie-line power control method is introduced for small-scale networks, while a coordinated optimization model is established for regional networks, solved using the Evolutionary Particle Swarm Optimization (EPSO) algorithm. Simulation results based on a real-world Zhejiang distribution network demonstrate that the proposed method effectively regulates power fluctuations. Compared to centralized optimization, the hierarchical approach reduced total system operating costs and exhibited improved computational scalability as the number of distributed entities increased. The proposed method achieves economic optimality for various stakeholders and enhances the operational efficiency of “Source-Grid-Load-Storage-Heat” systems, providing a robust solution for energy complementarity in new power systems. Full article
(This article belongs to the Special Issue Automatic Control Strategy and Technology in Power Electronics)
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21 pages, 2765 KB  
Article
Dynamic Error-Modulated Prescribed Performance Control of a DC–DC Boost Converter Using a Neural Network Disturbance Observer
by Hezhang Feng, Teng Lv and Xinchun Jia
Electronics 2026, 15(2), 277; https://doi.org/10.3390/electronics15020277 - 7 Jan 2026
Viewed by 449
Abstract
This paper formulates a control framework grounded in prescribed performance control (PPC) and combined with a dynamic error modulation function. The proposed framework addresses the control challenges of DC–DC boost converters under sudden power variations caused by constant power loads (CPLs). A sine [...] Read more.
This paper formulates a control framework grounded in prescribed performance control (PPC) and combined with a dynamic error modulation function. The proposed framework addresses the control challenges of DC–DC boost converters under sudden power variations caused by constant power loads (CPLs). A sine kernel-based prescribed performance function with smoothly decaying characteristics is designed to form a dynamic performance boundary that gradually tightens as the system state evolves. Furthermore, to effectively eliminate the restriction of traditional PPC on the system’s initial state, a time-varying modulation function is introduced. This function dynamically scales the tracking error, thereby improving the system’s adaptability at the initial state. A neural network disturbance observer (NNDO) is employed to approximate and compensate for unknown nonlinearities and external disturbances, thereby enhancing system robustness and adaptability. Consequently, a prescribed performance controller that integrates dynamic error modulation and a dual-channel NNDO is proposed. The proposed controller not only guarantees that the tracking error satisfies the prescribed performance constraints but also avoids the computation of high-order derivatives. Simulation results demonstrate that the proposed method maintains bounded convergence of the tracking error and achieves smooth voltage regulation during CPL variations. The results further exhibit excellent dynamic response and steady-state performance. Full article
(This article belongs to the Special Issue Automatic Control Strategy and Technology in Power Electronics)
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