通过双延迟深度确定性策略梯度(TD3)对 PMSG 风机控制进行深度强化学习

Darkhan Zholtayev, Matteo Rubagotti, Ton Duc Do
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引用次数: 0

摘要

本文提出在使用永磁同步发电机(PMSG)的风能转换系统中使用深度强化学习方法--确切地说,是深度确定性策略梯度(DDPG)方法的一种变体,即孪生延迟 DDPG 或 TD3--来实现最大功率点跟踪。本文概述了 TD3 算法,并详细描述了该算法的实施和针对所考虑应用的训练。还提供了仿真结果,包括与基于反馈线性化和线性二次调节的模型控制方法的比较。与所提出的基于模型的方法相比,所提出的基于 TD3 的控制器实现了令人满意的控制性能,并且对 PMSG 参数变化具有更强的鲁棒性。据作者所知,本文首次提出了一种利用 DRL 为风能转换系统生成速度和电流控制回路的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Deep reinforcement learning for PMSG wind turbine control via twin delayed deep deterministic policy gradient (TD3)

Deep reinforcement learning for PMSG wind turbine control via twin delayed deep deterministic policy gradient (TD3)
This article proposes the use of a deep reinforcement learning method—and precisely a variant of the deep deterministic policy gradient (DDPG) method known as twin delayed DDPG, or TD3—for maximum power point tracking in wind energy conversion systems that use permanent magnet synchronous generators (PMSGs). An overview of the TD3 algorithm is provided, together with a detailed description of its implementation and training for the considered application. Simulation results are provided, also including a comparison with a model‐based control method based on feedback linearization and linear‐quadratic regulation. The proposed TD3‐based controller achieves a satisfactory control performance and is more robust to PMSG parameter variations as compared to the presented model‐based method. To the best of the authors' knowledge, this article presents for the first time an approach for generating both speed and current control loops using DRL for wind energy conversion systems.
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