AI-Driven Monitoring and Control for Power Quality in Next-Generation Power Systems

Submission Deadline: 01 June 2026 (closed) View: 1384 Submit to Special Issue

Guest Editor(s)

Prof. Dr. Taha Selim Ustun

Email: selim.ustun@aist.go.jp

Affiliation: Power System Automation and Cybersecurity Lab, Fukushima Renewable Energy Institute, Advanced Industrial Science and Technology (AIST), Koriyama 963-0298, Japan

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Research Interests: power system analysis, power system quality, AI for smartgrids, smart grid control and cybersecurity

Summary

The rapid integration of renewable energy, distributed generation, and advanced power electronic devices is transforming modern power systems. While these developments enable cleaner and more resilient grids, they also introduce significant challenges to maintaining high power quality. Issues such as harmonics, voltage fluctuations, and missing or uncertain measurement data have become increasingly complex to address in this evolving environment. Novel monitoring, analysis, and control techniques are urgently needed to ensure the stable and reliable operation of next-generation grids.

This Special Issue aims to bring together cutting-edge research on methodologies and technologies that enhance power quality monitoring, diagnosis, and control. Particular emphasis is placed on artificial intelligence (AI)-based data processing, techniques for handling missing or corrupted data, and advanced diagnostic frameworks tailored to grid-forming and smart inverters. By bridging theory and practice, this issue seeks to highlight innovative approaches that ensure robust and sustainable power quality in modern energy systems.

Suggested themes include (but are not limited to):
- AI and machine learning for power quality monitoring and forecasting
- Missing data handling, data recovery, and uncertainty mitigation
- Grid-forming inverters, smart inverters, and novel converter technologies
- Advanced signal processing for harmonics and disturbances
- Cyber-physical approaches for resilient and adaptive power quality management

Keywords

Published Papers