SPECIAL SESSIONS

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Special Session 4



Advanced Technologies and Applications of Health Management

for Power Equipment


Organizers:

Ziwei Zhang, Sichuan Engergy Internet Research Institute, Tsinghua University

Tusongjiang Kari , School of Electrical engineering, Xinjiang University

Liang He, Datang Hydropower Science & Technology Research Institute Co., Ltd.


Brief Description:
The increasing integration of renewable energy and the growing complexity of power systems demand more intelligent and reliable health management solutions for critical power equipment. Recent advancements in artificial intelligence (AI), Internet of Things (IoT), edge computing, and digital twin technologies have significantly enhanced real-time condition monitoring, predictive maintenance, and fault diagnosis capabilities. However, challenges such as data heterogeneity, dynamic operating conditions, and interpretability of AI models remain key obstacles to widespread industrial adoption.

This special issue invites high-quality research on cutting-edge technologies and applications in power equipment health management. Topics of interest include, but are not limited to:

• AI-driven anomaly detection and fault diagnosis (e.g., deep learning, explainable AI, federated learning)
• IoT and edge computing for real-time equipment monitoring
• Digital twin-based degradation modeling and lifetime prediction
• Multi-sensor fusion and big data analytic for health assessment
• Resilience and risk management in power equipment operation
• Novel sensing technologies and non-destructive testing methods

This special session aims to integrate theoretical research with real-world implementations, driving advancements in health management for power equipment to foster a reliable and efficient electrical device future. This platform will be a convergence of experts, offering solutions to modern power equipment challenges while paving the way for innovative.

Keywords: Health Management; Condition Monitoring; Fault Diagnosis; Detection and Warning Technology; Power Equipment
Submission Portal: https://easychair.org/my/conference?conf=peet2025

About the Organizers:
ZIWEI ZHANG received the B.Eng. and Ph.D. degrees in electrical engineering and electronics from the University of Liverpool, Liverpool, U.K., in 2011 and 2016, respectively. She was a Postdoctoral Research Associate at Department of Electrical Engineering, Tsinghua University, Beijing, China between 2016 and 2018. She is currently a Research Professor at Sichuan Energy Internet Research Institute, Tsinghua University. Her research interests include condition monitoring, fault diagnosis and health assessment of high-voltage apparatus.

TUSONGJIANG KARI received the B.Sc. and M.Sc. degrees in Electrical engineering from Xi’an Jiao Tong University, Xi’an, China, in 2007 and 2010, respectively, and the Ph.D. degree in electrical engineering from the Tsinghua University, Beijing, China, in 2019. Currently, he is Professor with the School of Electrical Engineering, Xinjiang University, Urumqi, China. He focuses on technology of power system digitalization, health management of power equipment, and artificial intelligence and pattern recognition.

LIANG HE received B.Sc. and M.Sc. degrees in Electrical engineering from Xi’an Jiao Tong University, Xi’an, China, in 2010 and 2013, respectively.Currently, He is a Senior Engineer at the Datang Hydropower Science & Technology Research Institute Co., Ltd., Chengdu, China. He is engaged in condition monitoring and fault diagnosis technology research for power transmission and transformation equipment.