Power Optimization of Wind Farms Based on Improved Jensen Model and Deep Reinforcement Learning

doi:10.11930/j.issn.1004-9649.202410051

Abstract

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.

Key words: wind farm control, maximizing wind energy capture, deep reinforcement learning, model-free control, model-based control, neural network

WANG Guanchao, HUO Yuchong, LI Qun, LI Qiang. Power Optimization of Wind Farms Based on Improved Jensen Model and Deep Reinforcement Learning[J]. Electric Power, 2025, 58(4): 78-89.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202410051

https://www.electricpower.com.cn/EN/Y2025/V58/I4/78

Figures/Tables 17

Fig.1 Illustration of the Jensen wake model

Fig.2 Deep reinforcement learning control algorithm framework

Fig.3 Actor-Critic network structure

Fig.4 The proposed deep reinforcement learning framework combined model-based and model-free methods

Fig.5 The framework for combining wind farms with deep reinforcement learning

Fig.6 Wind farm topology

Fig.7 Comparison of output power between two models

Table 1 Simulation parameters setting

参数		数值
$D$ /m, $d_{H}$ /m, $ρ$ /(kg·m^–3), $A$ /m³		126, 500, 1.225, 7850
$n$ , $τ$ , $η_{a c t o r}$ , $η_{c r i t i c}$ , $m$ , $γ$		64, 0.001, 0.01, 0.01, 10⁴, 0.99
迭代轮数, 步数		1000, 10
$C_{T}^{min}$ , $C_{T}^{max}$		0.1,1

Table 1 Simulation parameters setting

参数		数值
$D$ /m, $d_{H}$ /m, $ρ$ /(kg·m^–3), $A$ /m³		126, 500, 1.225, 7850
$n$ , $τ$ , $η_{a c t o r}$ , $η_{c r i t i c}$ , $m$ , $γ$		64, 0.001, 0.01, 0.01, 10⁴, 0.99
迭代轮数, 步数		1000, 10
$C_{T}^{min}$ , $C_{T}^{max}$		0.1,1

Fig.8 Wind velocity curve

Fig.9 Comparison of rewards using different methods

Fig.10 Comparison of wind farm output power

Fig.11 Comparison of wind farm wake

Fig.12 Normalized thrust coefficient variation curve

Fig.13 The power curve of wind turbine output

Fig.14 Pitch angle curve

Fig.15 Rotational speed curve

Fig.16 Comparison of wind turbine capture power

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