{"id":9241,"date":"2026-07-18T16:07:13","date_gmt":"2026-07-18T08:07:13","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=9241"},"modified":"2026-07-18T17:29:50","modified_gmt":"2026-07-18T09:29:50","slug":"jase-202610-33-045","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202610-33-045","title":{"rendered":"Research on Real-time Monitoring and Adaptive Control of Intelligent Power Enhancement Systems for Wind Turbines Driven by Smart Sensing Technology"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 33<\/a><\/p>\n<\/div>\n\n\n\n

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Kuiyong Chen1<\/sup>, Yingchun Guan2<\/sup>, Haoran Luo2<\/sup>, Shengpeng Liu3<\/sup>, Xin Li4<\/sup>, and Dedi Li4<\/sup><\/i><\/a><\/p>\n\n\n\n

1<\/sup>Hubei Energy Group Co., Ltd., Wuhan, Hubei Province, 430000, China<\/p>\n\n\n\n

2<\/sup>Hubei Energy Group New Energy Development Co., Ltd., Wuhan, Hubei Province, 430000, China<\/p>\n\n\n\n

3<\/sup>Hubei Energy Group Huangshi Wind Power Co., Ltd., Huangshi, Hubei Province, 435000, China<\/p>\n\n\n\n

4<\/sup>China Electric Power Construction Group East China Survey and Design Research Institute Co., Ltd., Hangzhou, Zhejiang
Province, 310000, China<\/p>\n<\/div>\n\n\n\n

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Received: March 16, 2026
Accepted: May 11, 2026
Publication Date: July 18, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

DQN Agent for Wind Turbine Control<\/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:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202610_33.045<\/a>\u00a0\u00a0<\/p>\n\n\n\n

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

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Wind turbine optimization under variable aerodynamic conditions is challenging, as traditional PID controllers fail to adapt to rapid wind fluctuations, leading to efficiency loss and component wear. To address this, a hybrid adaptive control framework combining Model Predictive Control (MPC), Long Short-Term Memory (LSTM) forecasting, and Reinforcement Learning (RL) with Deep Q-Network (DQN) and Deep Neural Network (DNN) is proposed. MPC provides predictive optimization, LSTM forecasts future turbine states, and RL enables adaptive real-time adjustments. Experimental results demonstrate high prediction accuracy with MAE = 0.0244, RMSE = 0.0011, and R2<\/sup> = 0.995, confirming the reliability of the system. This integrated
approach enhances turbine efficiency, reduces operational costs, and stabilizes performance under dynamic wind conditions. Scalable and sustainable, the framework offers a high-resolution solution for large wind farms, meeting global demands for renewable energy integration while ensuring robust and efficient turbine operation.<\/p>\n\n\n\n

Keywords: Wind Turbine Optimization; Model Predictive Control; Long Short-Term Memory; Reinforcement Learning; Deep Q-Network<\/em><\/p>\n\n\n\n

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