AI Applications for Smart Grid Energy Management and Industrial Electrical Systems

Dear Colleagues,

The rapid evolution of electrical systems, from large-scale power grids to renewable energy plants and advanced electrical machines, has created new challenges in monitoring, control, optimization, transactive energy frameworks, and fault diagnosis. These systems are increasingly complex, interconnected, and subject to demanding operational requirements, making intelligent, reliable, and efficient solutions more essential than ever.

Artificial Intelligence (AI) techniques, such as machine learning, deep learning, fuzzy systems, evolutionary computation, and other bio-inspired approaches, have shown remarkable potential to address these challenges. By leveraging powerful computational models and data-driven analysis, AI is transforming how electrical systems are designed, monitored, and maintained, enabling predictive maintenance, enhancing energy efficiency, and improving overall system reliability.

This Special Issue aims to gather state-of-the-art research contributions in the development and application of AI methods for electrical engineering. We encourage works addressing the analysis of electrical signals, fault diagnosis, optimization, and intelligent decision-making in various contexts, including smart grids, renewable energy systems, power quality monitoring and control, transactive energy frameworks, and industrial electrical applications. Contributions combining AI with modern technologies, such as the Internet of Things (IoT), edge computing, digital twins, and embedded systems, are also welcome, as they enable scalable, real-time, and interconnected solutions for electrical systems.

Some research areas may include (but are not limited to) the following:

• Intelligent algorithms for electrical signal processing and analysis;
• AI-based monitoring and control of smart grids and renewable energy systems;
• Power quality assessment and enhancement using computational intelligence;
• Optimization and decision-making methods for transactive energy frameworks;
• Fault detection, diagnosis, and prognosis in electrical machines and systems;
• Applications of deep learning and machine learning in electrical engineering;
• Integration of AI with IoT, edge computing, and digital twin technologies;
• Embedded and real-time AI solutions for industrial electrical systems.

We invite original research papers, comprehensive reviews, and case studies that demonstrate novel AI-based approaches for electrical systems.

Dr. Martin Valtierra-Rodriguez
Dr. David Granados-Lieberman
Guest Editors

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