中国电力2025，Vol.58Issue(4)：78-89,12.DOI:10.11930/j.issn.1004-9649.202410051
基于深度强化学习与改进Jensen模型的风电场功率优化
Power Optimization of Wind Farms Based on Improved Jensen Model and Deep Reinforcement Learning
摘要
Abstract
The power capture capability of wind farms is constrained by various factors.To maximize the power output of wind farms and address the impacts of wake effects and turbulent wind speeds,this paper proposes a wind farm control scheme based on deep reinforcement learning.This scheme combines both model-based and model-free control methods and integrates them into a deep reinforcement learning deep deterministic policy gradient network with an Actor-Critic architecture.In terms of control accuracy,Jensen wake model consider time delay is adopted to enhance the precision of wake effects and effectively captures the long-term impact on the wind farm's power output.Simulation results show that,compared to traditional model-based or model-free methods,this scheme significantly increases the maximum power output of the wind farm while maintaining control accuracy,and significantly reduces training time and computational resource consumption,thereby improving the overall performance of the control strategy.关键词
风电场控制/最大化风能捕获/深度强化学习/无模型控制/有模型控制/神经网络Key words
wind farm control/maximizing wind energy capture/deep reinforcement learning/model-free control/model-based control/neural network引用本文复制引用
王冠朝,霍雨翀,李群,李强..基于深度强化学习与改进Jensen模型的风电场功率优化[J].中国电力,2025,58(4):78-89,12.基金项目
国家电网有限公司科技项目(攻关团队项目)(含多构网型变流器的中远海风电场经柔直并网主动频率支撑关键技术,5108-202218280A-2-241-XG). This work is supported Science and Technology Project of SGCC(Reseach Team Project)(Active Frequency Support for Mid and Long Distance Offshore Wind Farm with Multiple Grid-Forming Converter Connected via VSC-HVDC,No.5108-202218280A-2-241-XG). （攻关团队项目）
