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Processes
Special Issues
AI-Driven Innovations for Enhancing Power System Stability and Operational Efficiency
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AI-Driven Innovations for Enhancing Power System Stability and Operational Efficiency

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: 10 November 2025 | Viewed by 2008

Special Issue Editor

Dr. Thair Mahmoud

E-Mail Website
Guest Editor
School of Engineering, Edith Cowan University, Joondalup, WA 6027, Australia
Interests: artificial intelligence; resource adequacy; electricity market modelling; forecasting modelling and network planning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid transformation of traditional power grids towards high levels of Inverter-Based Resource (IBR) penetration and dynamic distribution networks with the presence of smart loads and Electric Vehicles has forced system operators to investigate advanced operation optimization and planning techniques to maintain reliable and efficient operation.

In modern power systems, the electricity market is designed to enable reliable and efficient resource adequacy with the opportunity to attract investments into the market that achieve the system’s reliability objectives.

To ensure that energy markets are supported by a robust operation plan with high amounts of IBR penetration, there is a necessity to involve advanced optimization techniques to manage such complicated scenarios. Optimization techniques, which can receive help from sophisticated Artificial Intelligence-based system condition forecasts, with a focus on providing intelligent dispatch solutions for weak grid areas, enhanced IBR grid interconnection processes, and localized tunning-based generator settings, can be of a great interest to research in this area of study as well as to industry at a global scale.

Topics of interest for publication include, but are not limited to, the following:

  • Adaptive dispatch rules;
  • Economic dispatch optimization;
  • IBR integration process optimization;
  • IBR system-level control setting optimization;
  • Mixed-integer programming;
  • Power system stability prediction;
  • Energy market optimization;
  • Distribution Energy Markets;
  • Artificial Intelligence application in power systems;
  • Sizing of Battery Energy Storage Systems (BESSs) in power systems.

Dr. Thair Mahmoud
Guest Editor

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. Processes 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 2400 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

  • inverter-based resources (IBRs)
  • dispatch optimization
  • battery energy storage systems (BESSs)
  • power systems stability
  • distributed energy resources (DERs)
  • transmission and distribution planning
  • artificial intelligence (AI)
  • electricity markets

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Published Papers (3 papers)

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Research

25 pages, 3447 KiB  
Article
Research on Transformer Fault Diagnosis and Maintenance Strategy Generation Based on TransQwen Model
by Zichun Xue, Bo Wang, Hengrui Ma, Jiaxin Zhang, Hanqi Zhang and Jinhui Zhou
Processes 2025, 13(7), 1977; https://doi.org/10.3390/pr13071977 - 23 Jun 2025
Abstract
Currently, transformer fault diagnosis primarily relies on the subjective judgment of maintenance personnel, which entails significant human effort and expertise. Moreover, unstructured text data—such as historical defect logs and maintenance records—are not effectively leveraged for the intelligent generation of maintenance strategies, hindering accurate [...] Read more.
Currently, transformer fault diagnosis primarily relies on the subjective judgment of maintenance personnel, which entails significant human effort and expertise. Moreover, unstructured text data—such as historical defect logs and maintenance records—are not effectively leveraged for the intelligent generation of maintenance strategies, hindering accurate status evaluation and proactive risk management. This paper proposes TransQwen, a domain-adapted LLM tailored for transformer fault diagnosis and maintenance strategy generation. Built upon the Qwen-7B-Chat architecture, TransQwen is fine-tuned on a domain-specific corpus encompassing transformer fault cases aligned with technical standards and operational procedures. It integrates DoRA for efficient parameter adaptation and RoPE to enhance positional encoding during training. The model is evaluated in three core tasks: fault type classification, fault severity grading, and strategy generation. The results show significant improvements—over 10 percentage point gains in standard conditions and up to 30 percentage points in F1 score under extreme low-sample settings (e.g., 100 samples), demonstrating robust generalization. In the maintenance strategy generation experiment, all the evaluation results of the TransQwen model reached the optimal. Through a knowledge-driven approach, the model can perform question-and-answer tasks involving professional knowledge in the power vertical field, and customize and generate accurate maintenance strategies for specific fault scenarios. Full article
(This article belongs to the Special Issue AI-Driven Innovations for Enhancing Power System Stability and Operational Efficiency)
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19 pages, 4706 KiB  
Article
Load Restoration Based on Improved Girvan–Newman and QTRAN-Alt in Distribution Networks
by Chao Zhang, Qiao Sun, Jiakai Huang, Shiqian Ma, Yan Wang, Hao Chen, Hanning Mi, Jiuxiang Chen and Tianlu Gao
Processes 2025, 13(5), 1473; https://doi.org/10.3390/pr13051473 - 12 May 2025
Viewed by 357
Abstract
With the increasing demand for power supply reliability, efficient load restoration in large-scale distribution networks post-outage scenarios has become a critical challenge. However, traditional methods become computationally prohibitive as network expansion leads to exponential growth of decision variables. This study proposes a multi-agent [...] Read more.
With the increasing demand for power supply reliability, efficient load restoration in large-scale distribution networks post-outage scenarios has become a critical challenge. However, traditional methods become computationally prohibitive as network expansion leads to exponential growth of decision variables. This study proposes a multi-agent reinforcement learning (MARL) framework enhanced by distribution network partitioning to address this challenge. Firstly, an improved Girvan–Newman algorithm is employed to achieve balanced partitioning of the network, defining the state space of each agent and action boundaries within the multi-agent system (MAS). Subsequently, a counterfactual reasoning framework solved by the QTRAN-alt algorithm is incorporated to refine action selection during training, thereby accelerating convergence and enhancing decision-making efficiency during execution. Experimental validation using a 27-bus system and a 70-bus system demonstrates that the proposed QTRAN-alt with the Girvan–Newman method achieves fast convergence and high returns compared to typical MARL approaches. Furthermore, the proposed methodology significantly improves the success rate of full system restoration without violating constraints. Full article
(This article belongs to the Special Issue AI-Driven Innovations for Enhancing Power System Stability and Operational Efficiency)
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21 pages, 2951 KiB  
Article
Research on Power Quality Control Methods for Active Distribution Networks with Large-Scale Renewable Energy Integration
by Yongsheng Wang, Yaxuan Guo, Haibo Ning, Peng Li, Baoyi Cen, Hongwei Zhao and Hongbo Zou
Processes 2025, 13(5), 1469; https://doi.org/10.3390/pr13051469 - 12 May 2025
Viewed by 400
Abstract
With the proposal of carbon peaking and carbon neutrality goals, the proportion of distributed renewable energy generation in active distribution networks (ADNs) has been continuously increasing. While this has effectively reduced greenhouse gas emissions, it has also given rise to power quality issues [...] Read more.
With the proposal of carbon peaking and carbon neutrality goals, the proportion of distributed renewable energy generation in active distribution networks (ADNs) has been continuously increasing. While this has effectively reduced greenhouse gas emissions, it has also given rise to power quality issues such as excessive or insufficient voltage amplitudes. To effectively address this problem, this paper proposes a multi-resource coordinated dynamic reactive power–voltage coordination optimization method. Firstly, an improved Generative Convolutional Adversarial Network (GCAN) is used to generate typical wind and solar power output scenarios. Based on these generated typical scenarios, a voltage control model for ADNs is established with the objective of minimizing voltage fluctuations, fully exploiting the dynamic reactive power regulation resources within the ADN. In view of the non-convex and nonlinear characteristics of the model, an improved Gray Wolf Optimizer (GWO) algorithm is employed for model optimization and solution seeking. Finally, the effectiveness and feasibility of the proposed method are demonstrated through simulations using modified IEEE-33-bus and IEEE-69-bus test systems. Full article
(This article belongs to the Special Issue AI-Driven Innovations for Enhancing Power System Stability and Operational Efficiency)
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