AI-Based Modelling and Control of Power Systems
A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".
Deadline for manuscript submissions: 10 March 2025 | Viewed by 582
Special Issue Editors
Interests: power system; smart grid; renewable energy integration; energy management; hybrid energy
Interests: power system; smart grid; renewable energy integration
Special Issue Information
Dear Colleagues,
The Special Issue is under the supervision of The Power Electrical Developing Advanced Research (PEDAR) Group.
Scope: This Special Issue will explore the integration of advanced artificial intelligence (AI) techniques, including artificial neural networks (ANNs), deep learning, and machine learning, into the field of power electronics. This Special Issue will focus on AI-driven solutions for detecting and mitigating cyber-attacks, optimizing control systems, and implementing advanced control strategies. We invite contributions that explore the innovative applications of AI in power electronic systems, including control strategies, optimization techniques, and integration with renewable energy sources.
Introduction: The rapid evolution of power electronics technologies has led to significant advancements in energy management and control systems. As these systems become increasingly complex and interconnected, the demand for robust cybersecurity measures, sophisticated control strategies, and optimization techniques has grown. Artificial intelligence, including ANNs, deep learning, and machine learning, provides powerful tools to address these challenges, enhancing system performance and resilience. This Special Issue will highlight innovative approaches that leverage AI to improve power electronics systems. The focus will be on optimizing control strategies, detecting and mitigating cyber threats, and advancing various control methodologies. By bridging the gap between power electronics and AI, this Special Issue will contribute to the development of more secure, efficient, and adaptive systems.
Objectives:
- Showcase Cutting-Edge Research: Present the latest advancements in applying artificial intelligence (AI) techniques to power electronics, with a particular focus on enhancing control systems and optimization strategies.
- Explore Cybersecurity Solutions: Investigate novel AI-driven methods for detecting and mitigating cyber-attacks. This includes exploring techniques such as artificial neural networks (ANNs), deep learning, and machine learning for improved cybersecurity in power electronic systems.
- Highlight Advanced Control Strategies: Demonstrate recent developments in control strategies and their integration with AI. Key areas of interest include the following:
- Model Predictive Control (MPC): For dynamic optimization and real-time control;
- Adaptive Control: Techniques for adjusting control parameters in response to changing system conditions;
- Deep Reinforcement Learning (DRL): Applying reinforcement learning to optimize decision-making and control;
- Fuzzy Logic Control (FLC) with AI Integration: Enhancing fuzzy logic controllers with AI for improved adaptability;
- Robust Control with AI Techniques: Using AI to enhance robustness and resilience in control systems;
- Distributed Control Systems: Innovations in decentralized control approaches and their integration with AI.
- Investigate Optimization Methods: Examine advanced optimization techniques to enhance the efficiency and effectiveness of power electronic systems and control strategies. This includes the following:
- Multi-Objective Optimization: Balancing multiple conflicting objectives in system design and operation;
- Metaheuristic Optimization Algorithms: Utilizing techniques like genetic algorithms and particle swarm optimization for complex problems;
- Convex Optimization: Applying convex programming methods for optimal system design;
- Dynamic Programming: Solving optimization problems by breaking them down into simpler subproblems;
- Stochastic Optimization: Incorporating randomness to handle uncertainties in system parameters;
- Hybrid Optimization Methods: Combining different optimization techniques to leverage their strengths;
- Machine Learning-Based Optimization: Using machine learning to model and optimize complex systems;
- Robust Optimization: Ensuring solutions remain effective under varying conditions and uncertainties;
- Evolutionary Strategies: Applying evolutionary algorithms inspired by biological processes for system optimization.
- Foster Collaboration: Encourage collaboration between researchers and practitioners in the fields of power electronics, AI, optimization, and cybersecurity. Promote the exchange of ideas and advancements to drive innovation and practical applications.
Suggested Topics for the Special Issue:
In this Special Issue, original research articles and reviews are welcome. Areas of study may include (but are not limited to) the following:
- Advanced Research on AI Applications in Power Electronics
- Artificial Neural Networks (ANNs): Applications in the design and control of power electronic systems;
- Deep Learning: Utilization for modeling and predicting the behavior of power electronic systems;
- Machine Learning: Use in optimizing and self-tuning control systems.
- Novel Methods for Detecting and Mitigating Cyber Attacks Using AI
- ANN for Cybersecurity: Techniques for detecting and countering threats and intrusions in control systems;
- Deep Learning for Attack Detection: Advanced methods for identifying and responding to cyber-attacks in power electronics;
- Machine Learning Approaches: Development of sophisticated defense mechanisms against cyber threats.
- Advanced Control Strategies and Their Integration with AI
- Model Predictive Control (MPC): Applications for real-time optimization and dynamic control;
- Adaptive Control: Techniques for adjusting control parameters based on varying system conditions;
- Deep Reinforcement Learning (DRL): Optimization of decision-making and control strategies using DRL;
- Fuzzy Logic Control (FLC) with AI Integration: Enhancements to fuzzy logic controllers through AI techniques;
- Robust Control with AI Techniques: Improving robustness and resilience in control systems using AI;
- Distributed Control Systems: Innovations in decentralized control approaches and their integration with AI.
- Optimization Methods to Enhance Efficiency and Effectiveness
- Multi-Objective Optimization: Balancing multiple conflicting objectives in system design and operation;
- Metaheuristic Optimization Algorithms: Techniques like genetic algorithms and particle swarm optimization for complex problems;
- Convex Optimization: Using convex programming methods for optimal system design;
- Dynamic Programming: Breaking down optimization problems into simpler subproblems for solutions;
- Stochastic Optimization: Incorporating randomness to handle uncertainties in system parameters;
- Hybrid Optimization Methods: Combining various optimization techniques to leverage their strengths;
- Machine Learning-Based Optimization: Leveraging machine learning for modeling and optimizing complex systems;
- Robust Optimization: Ensuring solutions are effective under varying conditions and uncertainties;
- Evolutionary Strategies: Applying evolutionary algorithms for system optimization inspired by biological processes.
Call for Papers: Authors are invited to submit original research articles, review papers, and case studies that align with the scope of this Special Issue. Manuscripts should be submitted through the MDPI submission system, adhering to the journal’s guidelines.
Dr. Mohammad Reza Maghami
Dr. Javad Rahebi
Dr. Mehdi Zareian Jahromi
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. 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
-
power system
- smart grid
- renewable energy integration
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.
Further information on MDPI's Special Issue polices can be found here.
