Cyber Physical Fusion-Based Defect Perception, Fault Diagnosis, and Reliability Analytics in Power Systems

Dear Colleagues,

Defect perception, fault diagnosis, and reliability analytics in power systems (e.g., substations, photovoltaic power plants, wind farms, and ultra-high-voltage DC transmission scenarios) based on Cyber Physical Fusion, the Internet of Things (IoT), and Artificial Intelligence (AI) are important topics. However, the IoT and AI are still in their infant stages when it comes to applications for the operation and maintenance of power systems. The implementation of the current IoT and AI-based techniques for power systems lacks a unified reference specification and architecture. Meanwhile, the depth of association and integration between cyber and physical characterization is still not enough. This Special Issue aims to create an international forum for scientists and practicing engineers throughout the world to publish the latest research findings and ideas in mechanism modeling, information-driven diagnosis, reliability analytics, real-time monitoring, and defect detection of power equipment. This Special Issue welcomes theoretical and practical contributions that help us to further understand intelligent techniques, including advanced signal processing, deep learning, fuzzy logic, evolutionary algorithms, swarm intelligence, and interdisciplinary topics. Moreover, this Special Issue also welcomes reports on innovative machines and power systems with applications for the IoT and AI, intelligent health assessment, diagnosis, and control techniques.

This Special Issue seeks to solicit original research articles as well as review articles. Potential topics include, but are not limited to:

• Advanced digital signal processing methodologies for big data to solve the Prognostic and Health Management (PHM) problem of power equipment;
• Real-time defect detection and performance evaluation based on physical information for critical components in power generation scenarios;
• Data-driven health indicator and threshold representation methodologies for fault detection, diagnosis, and isolation;
• AI-based approaches for fault diagnosis of renewable power generation plants;
• Advanced fault informative feature (e.g., time-domain analysis and time–frequency domain) representative methods for local defect detection;
• Spectrum-based capability evaluation of noise disturbance robustness, and weak diagnostic signal enhancement;
• Applications of AI techniques to imbalanced fault label recognition, and fault diagnosis problems under small sampling data;
• Big data analysis and processing of the PHM of power equipment combined with Industrial IoT.

Dr. Xian-Bo Wang
Dr. Zhi-Xin Yang
Dr. Yunfeng Yan
Guest Editors

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