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Application of Artificial Intelligence in Power and Energy Systems
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Application of Artificial Intelligence in Power and Energy Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: 25 September 2025 | Viewed by 829

Special Issue Editors

Dr. Haosen Yang

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong
Interests: AI application in power grid; cyber-physical system; power grid stability; battery management
Dr. Xin Shi

E-Mail Website
Guest Editor
Energy Internet Innovation Institute, Tsinghua University, Beijing 100085, China
Interests: AI application in power grid; power grid stability; fault detection and diagnosis
Dr. Ziqiang Wang

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong
Interests: microgrid control; cyber-physical system; power system control

Special Issue Information

Dear Colleagues,

Empowered by the ability of high-accuracy estimation and high-flexibility decision, artificial intelligence (AI) plays a crucial role in accelerating the decarbonization of power & energy systems. AI is capable of boosting the integration or renewable energies by predicting energy output, managing smart grids that adjust to supply and demand changes, predictive maintenance and dealing with extreme disasters. This Special Issue emphasizes recent advances in these areas and provides a platform for researchers and practitioners to share knowledge and discuss the latest developments. It aims to delve into various aspects of AI and power & energy systems. The topics of interest include, but are not limited to, the following:

  1. Energy generation forecasting using AI;
  2. AI based energy system dispatch and scheduling;
  3. Data-driven power & energy system state estimation;
  4. Predictive maintenance in power & energy systems using AI;
  5. Data analysis in cyber-physical system;
  6. AI for fault detection and diagnosis in power & energy systems;
  7. AI enhanced demand response;
  8. AI based methods of power & energy systems to deal with extreme disasters;
  9. Environmental impact assessment using AI;
  10. Other topics related to AI and power & energy systems.

Dr. Haosen Yang
Dr. Xin Shi
Dr. Ziqiang Wang
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. Energies is an international peer-reviewed open access semimonthly 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 2600 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

  • artificial intelligence
  • data-driven
  • power and energy system
  • energy forecasting
  • energy management system

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Published Papers (2 papers)

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Research

15 pages, 1766 KiB  
Article
Coordinated Thermal and Electrical Balancing for Lithium-Ion Cells
by Yuan Cao, Long Chen and Chunsheng Wang
Energies 2025, 18(16), 4231; https://doi.org/10.3390/en18164231 - 8 Aug 2025
Viewed by 211
Abstract
State-of-charge (SOC) and temperature inconsistencies among lithium-ion battery cells can significantly degrade the performance, safety, and lifespan of battery packs. To address this issue, this paper proposes a dynamic balancing strategy that simultaneously regulates both SOC and cell temperature in real time. Each [...] Read more.
State-of-charge (SOC) and temperature inconsistencies among lithium-ion battery cells can significantly degrade the performance, safety, and lifespan of battery packs. To address this issue, this paper proposes a dynamic balancing strategy that simultaneously regulates both SOC and cell temperature in real time. Each battery cell is connected to an individual Boost converter, enabling independent control of energy flow. An outer loop is adopted to stabilize the pack-level bus voltage. The balancing factors for SOC and temperature are adaptively fused using a Particle Swarm Optimization (PSO) algorithm, which dynamically adjusts the weightings based on real-time operating conditions. This approach allows the controller to prioritize either thermal or electrical balance when needed, ensuring robust performance under varying load and environmental disturbances. Simulation-based validation on a multi-cell lithium-ion pack demonstrates that the proposed method effectively reduces SOC and temperature deviation, improves pack-level energy utilization, and extends operational stability compared to fixed-weight balancing strategies. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Power and Energy Systems)
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32 pages, 9710 KiB  
Article
Early Detection of ITSC Faults in PMSMs Using Transformer Model and Transient Time-Frequency Features
by Ádám Zsuga and Adrienn Dineva
Energies 2025, 18(15), 4048; https://doi.org/10.3390/en18154048 - 30 Jul 2025
Viewed by 376
Abstract
Inter-turn short-circuit (ITSC) faults in permanent magnet synchronous machines (PMSMs) present a significant reliability challenge in electric vehicle (EV) drivetrains, particularly under non-stationary operating conditions characterized by inverter-driven transients, variable loads, and magnetic saturation. Existing diagnostic approaches, including motor current signature analysis (MCSA) [...] Read more.
Inter-turn short-circuit (ITSC) faults in permanent magnet synchronous machines (PMSMs) present a significant reliability challenge in electric vehicle (EV) drivetrains, particularly under non-stationary operating conditions characterized by inverter-driven transients, variable loads, and magnetic saturation. Existing diagnostic approaches, including motor current signature analysis (MCSA) and wavelet-based methods, are primarily designed for steady-state conditions and rely on manual feature selection, limiting their applicability in real-time embedded systems. Furthermore, the lack of publicly available, high-fidelity datasets capturing the transient dynamics and nonlinear flux-linkage behaviors of PMSMs under fault conditions poses an additional barrier to developing data-driven diagnostic solutions. To address these challenges, this study introduces a simulation framework that generates a comprehensive dataset using finite element method (FEM) models, incorporating magnetic saturation effects and inverter-driven transients across diverse EV operating scenarios. Time-frequency features extracted via Discrete Wavelet Transform (DWT) from stator current signals are used to train a Transformer model for automated ITSC fault detection. The Transformer model, leveraging self-attention mechanisms, captures both local transient patterns and long-range dependencies within the time-frequency feature space. This architecture operates without sequential processing, in contrast to recurrent models such as LSTM or RNN models, enabling efficient inference with a relatively low parameter count, which is advantageous for embedded applications. The proposed model achieves 97% validation accuracy on simulated data, demonstrating its potential for real-time PMSM fault detection. Additionally, the provided dataset and methodology contribute to the facilitation of reproducible research in ITSC diagnostics under realistic EV operating conditions. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Power and Energy Systems)
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