Fault Diagnosis and State Evaluation of New Power Grid

Submission Deadline: 15 June 2023 Submit to Special Issue

Guest Editors

Bo Yang, Kunming University of Science and Technology, China. Email: yangbo_ac@outlook.com
Yaxing Ren, University of Lincoln, United Kingdom. Email: YRen@lincoln.ac.uk
Yixuan Chen, University of Hong Kong, Hong Kong. Email: yxchen@eee.hku.hk
Xiaoshun Zhang, Northeastern University, China. Email: zhangxiaoshun@mail.neu.edu.cn


The distributed generation mode of renewable energy will inevitably lead to the rapid growth of the operating equipment of new power systems. Therefore, it is necessary to combine digital technology and artificial intelligence to realize efficient, accurate, and timely perception and intelligent decision-making of power grid equipment status, so as to meet the new challenges brought by the operation and maintenance of new power system equipment. Meanwhile, with the continuous expansion of the power system scale and the increasing complexity of the structure, a large amount of alarm information flows into the dispatching center in a short time, which far exceeds the processing capacity of the operators. To adapt to the rapid and accurate identification of faults under various simple and complex accident situations, the power system fault diagnosis system is crucial for decision-making reference.

This research topic aims to collate original papers about state perception and fault diagnosis of new power systems, which aims to provide a platform to promote up-to-date research and share promising ideas in related fields. Review articles describing the state of the art are also welcomed. Potential topics include but are not limited to the following:
• Model-based and model-free fault detection and isolation, fault threshold selection for renewable energy systems, such as wind, solar, fuel cells, and so on;
• Fault-tolerant control and reconfiguration of renewable energy systems and grids;
• Artificial intelligence methods for fault detection and isolation;
• Parameters identification of renewable energy systems, such as solar, fuel cells, wave energy, etc;

• Life cycle prediction and management of batteries and energy storage;

• Multi-source fusion for state monitoring of renewable energy systems and grids;
• The application of machine learning technology and digital twins in smart grids;
• Construction of knowledge graph of HVDC transmission system.


Renewable energy systems, New power grid, Fault diagnosis/fault detection and isolation, Fault-tolerant control and reconfiguration, Parameter identification, Life cycle prediction and management, Machine learning technology, Digital twins, Knowledge graph.

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