Journal of Engineering, Project, and Production Management, 2026, 16(4), 2026-74
An Intelligent Decision-Support System for Hydroelectric Power Unit Operation and Maintenance Empowered by Digital Twins
1 General
Manager and Chief Engineer, R & D Center, Guangdong HongDaXin Electronic
Technology Co., Ltd., Guangzhou, 510045, China
2 Senior Engineer, Hydropower
Branch, Guangdong Energy Group Co., Ltd., Heyuan, 517099, China
3 Associate Professor, School of
Automation and Information Technology, Guangdong Polytechnic of Water
Resources and Electric Engineering, Guangzhou, 510635, China, E-mail:
ShaoZL@126.com (corresponding author)
Project Management
Received January 18, 2026; revised April 21, 2026; accepted May 5, 2026
Available online May 29, 2026
Abstract: With the continuous rise in global energy demand, the Operation and Maintenance (O&M) of large hydropower plant units is becoming increasingly intelligent and digital. However, current O&M management still suffers from low efficiency and limited fault-prediction accuracy under complex operating conditions and equipment failures. To address these challenges, this study proposes an intelligent decision support system that integrates Digital Twin Technology with deep learning techniques, adopting a full life cycle approach for hydropower unit O&M. Numerous experiments have been conducted to demonstrate that the effectiveness of the system is 96.4% concerning fault diagnosis, while the fault reaction time is maintained at twelve minutes. The availability of the unit exceeds 95% under noise conditions of zero and 20 dB. It approaches 100%. All this information indicates that the designed decision support system can monitor the units effectively, manage any faults, and provide optimal control of hydropower units. This work is valuable since it may be used as the basis for implementing intelligent O&M practices in other energy fields.
Keywords: Digital twin technology, hydropower plant units, operation and maintenance, intelligent decision-making, deep learning.
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Citation: Zhong, G., Zhou, X., and Shao, Z. (2026). An Intelligent Decision-Support System for Hydroelectric Power Unit Operation and Maintenance Empowered by Digital Twins. Journal of Engineering, Project, and Production Management, 16(4), 2026-74.
