Offline Reinforcement Learning for Wind Farm Control: A Wind Tunnel Study under Dynamic Wind Directions

2026-09-11

AI Summary

本文针对风向变化下的风电场功率最大化问题，提出了一种基于离线强化学习的MTD3-BC算法，通过偏航控制来解决，并通过风洞实验验证了其有效性。

Abstract

This paper addresses the wind farm power maximization problem in the presence of wind direction changes. Specifically, a model-free Modified Twin Delayed Deep Deterministic Policy Gradient with Behavior Cloning (MTD3-BC) algorithm is proposed to tackle this task through yaw control under varying wind direction conditions. MTD3-BC is an offline reinforcement learning (RL) algorithm that aims to infer good behavior from only a precollected offline dataset. Additionally, to ensure smooth and moderate yaw adjustments, a new action consistency term is introduced into the policy optimization objective. Unlike online RL methods, MTD3-BC does not require extensive interactions with a wind farm simulator during training, significantly reducing computational costs and training time. A wind tunnel experiment is conducted to validate the effectiveness of the algorithm under varying wind directions. The results demonstrate that MTD3-BC successfully mitigates wake effects, delivering farm-level power gains of approximately 10\% over the baseline greedy strategy and performance on par with a data-calibrated model-based wake-steering benchmark, while requiring no wake model and only a small fraction of the training cost of online RL. To our knowledge, this is the first time an offline RL wind farm control policy has been validated and demonstrated experimentally.

Problem

• Wind Farm Power Maximization
• Wind Direction Changes
• Offline Reinforcement Learning
• Yaw Control

Innovation

• Offline Reinforcement Learning
• Wind Farm Control
• Modified Twin Delayed Deep Deterministic Policy Gradient (MTD3-BC)
• Action Consistency