Operation and Intelligent Fault Diagnosis of Hydropower Units Under New Power Systems
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water-Energy Nexus".
Deadline for manuscript submissions: 20 April 2026 | Viewed by 8
Special Issue Editors
Interests: hydropower units; safe and efficient operation; stability analysis; fault diagnosis; field testing; vibration monitoring; cavitation detection; hydraulic machinery; operational optimization
Interests: integrated modeling; simulation and control of renewable energy generation systems; new energy power generation forecasts; artificial intelligence applications
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Hydropower units are critical components in new power systems, and their stable, efficient, and safe operation is essential for supporting the integration of renewable energy and enhancing grid resilience. With the rapid development of sensing technologies, signal processing methods, and artificial intelligence, significant progress has been made in monitoring operational states, diagnosing faults, and predicting performance degradation. This Special Issue focuses on the operational challenges and intelligent fault diagnosis technologies used for hydropower units in the context of new power systems driven by carbon neutrality goals.
We welcome the submission of original research articles and reviews covering topics such as operational monitoring methods, noise reduction and signal denoising strategies, fault diagnosis and classification techniques, and predictive maintenance approaches for hydropower units. Studies involving vibration analysis, cavitation detection, sediment wear assessment, and characteristics of hydraulic transition processes are also encouraged. Special attention will be given to contributions that integrate experimental results, numerical simulations, and artificial intelligence algorithms (e.g., machine learning, deep learning) to enhance operational decision-making and fault prediction. By gathering interdisciplinary insights, this Special Issue aims to promote technological progress and best practices for the long-term reliability of hydropower facilities.
Dr. Xueli An
Dr. Wenlong Fu
Guest Editors
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Keywords
- hydropower units
- operation technology
- new power systems
- sediment wear
- cavitation
- numerical simulation
- machine learning
- intelligent monitoring
- hydraulic machinery
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