Application of Artificial Intelligence in Power System Monitoring and Fault Diagnosis II

Dear Colleagues,

The ever-increasing demands of automatic management in power systems and energy storage systems have garnered significant attention in both industry and academia. To systematically present the recent developments in related fields, this Special Issue focuses on the latest advances in operation monitoring and safety control, most notably using emerging techniques such as artificial intelligence, big data analysis, deep learning, characteristic modeling, performance control and fault-diagnosing applications.

The scope of this Special Issue includes, but is not limited to, the following:

• Data-based abnormalities analysis of thermal power system and nuclear power system;
• Fault diagnosis and prediction of wind turbines based on SCADA data;
• Modeling, monitoring and diagnosing of waste-to-energy, biomass power, and tidal power systems;
• Data-based fault characteristics analysis of power generation equipment;
• Power equipment health monitoring based on vibration signal, sound signal, image signal, thermal infrared signal, etc.;
• Control and performance monitoring of photovoltaic power generation systems;
• Modeling, scheduling, control and monitoring of microgrid systems;
• SOC estimation, SOH estimation, fault detection, isolation and localization of lithium battery systems;
• State estimation and performance evaluation of large-scale energy storage systems

Dr. Guang Wang
Dr. Jiale Xie
Prof. Dr. Shunli Wang
Guest Editors

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