{"id":11603,"date":"2026-09-06T15:22:40","date_gmt":"2026-09-06T07:22:40","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=11603"},"modified":"2026-09-06T16:50:07","modified_gmt":"2026-09-06T08:50:07","slug":"jase-202612-35-012","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202612-35-012","title":{"rendered":"Operational Status Assessment and Control of Key Equipment in Regionally Centralized Hydropower Plants from a Full-Lifecycle Perspective"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 35<\/a><\/p>\n<\/div>\n\n\n\n

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Jianxin Zhang<\/i><\/a>, Weilong Hu, Tao Hu, Renjun Li, Kui Yang, Zuhui Ren<\/p>\n\n\n\n

Hubei Energy Group Loushui Hydropower Co., Ltd., Enshi, Hubei, 445801, China<\/p>\n<\/div>\n\n\n\n

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Received: April 19, 2026
Accepted: June 11, 2026
Publication Date: September 06, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Abstract graph representation of a cascade hydropower plant cluster <\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation: BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202612_35.012<\/a> <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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This paper addresses the growing need for advanced operational and maintenance strategies in regionally centralized hydropower systems under modern clean energy frameworks. It proposes an integrated approach for equipment health assessment and intelligent control across multiple plants, overcoming the limitations of traditional O&M practices that rely mainly on operational data. The framework introduces a full-lifecycle data model that connects design, operation, and maintenance information, enabling more comprehensive insight into equipment conditions. A Spatio-Temporal Graph Attention Network (ST-GAT) is developed to assess equipment status by capturing both temporal degradation patterns and spatial interdependencies among units within a regional fleet. This allows a shift from isolated equipment monitoring to system-wide risk awareness. Building on this, a collaborative control strategy using Deep Reinforcement Learning (DRL) is designed. With a multi-objective reward function, the model balances maximizing power generation with minimizing equipment risk, enabling adaptive and optimized decision-making. Simulation results based on a cascade hydropower system demonstrate that the proposed ST-GAT framework achieves 96.7% fault prediction accuracy, outperforming conventional SVM and LSTM models by 11.4% and 5.5%, respectively, while also improving remaining useful life (RUL) estimation stability under non-stationary operating conditions. Furthermore, the DRL-based collaborative control strategy reduced regional equipment health degradation by 36.7% and improved the long-term comprehensive reward by 29.6% compared to conventional dispatch strategies. Additionally, the DRL-based control strategy reduces overall equipment degradation and maintenance costs while maintaining comparable power output. Overall, the study offers a novel data-driven paradigm for intelligent, predictive management of large-scale hydropower systems.<\/p>\n\n\n\n

Keywords: Regionally Centralized Hydropower, Full-Lifecycle, Operational Status Assessment, Intelligent Control, Spatio-Temporal Graph Network, Deep Reinforcement Learning<\/em><\/p>\n\n\n\n

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