Journal of Applied Science and Engineering

Published by Tamkang University Press

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Research on Real-time Monitoring and Adaptive Control of Intelligent Power Enhancement Systems for Wind Turbines Driven by Smart Sensing Technology

 2026

 Volume 33

Kuiyong Chen1, Yingchun Guan2, Haoran Luo2, Shengpeng Liu3, Xin Li4, and Dedi Li4

1Hubei Energy Group Co., Ltd., Wuhan, Hubei Province, 430000, China

2Hubei Energy Group New Energy Development Co., Ltd., Wuhan, Hubei Province, 430000, China

3Hubei Energy Group Huangshi Wind Power Co., Ltd., Huangshi, Hubei Province, 435000, China

4China Electric Power Construction Group East China Survey and Design Research Institute Co., Ltd., Hangzhou, Zhejiang
Province, 310000, China

Received: March 16, 2026
Accepted: May 11, 2026
Publication Date: July 18, 2026

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DQN Agent for Wind Turbine Control

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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 = 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.

Keywords: Wind Turbine Optimization; Model Predictive Control; Long Short-Term Memory; Reinforcement Learning; Deep Q-Network

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