Parameter Optimization Strategy for Wind Generator Converters Based on Actor-Critic Framework

LI Qiang; ZOU Xiaoming; REN Bixing; HE Yufan; DU Wenjuan

doi:10.19725/j.cnki.1007-2322.2023.0310

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Parameter Optimization Strategy for Wind Generator Converters Based on Actor-Critic Framework

更新时间：2025-10-22

- Parameter Optimization Strategy for Wind Generator Converters Based on Actor-Critic Framework
- Vol. 42, Issue 5, Pages: 1101-1110(2025)
- 作者机构：
  
  1. 国网江苏省电力有限公司电力科学研究院,江苏省,南京市,211103
  2. 四川大学电气工程学院,四川省,成都市,610065
- 作者简介：
- 基金信息：
- DOI：10.19725/j.cnki.1007-2322.2023.0310
  CLC： 5
- Published：2025
- 稿件说明：
移动端阅览
LI Qiang, ZOU Xiaoming, REN Bixing, et al. Parameter Optimization Strategy for Wind Generator Converters Based on Actor-Critic Framework[J]. 2025, 42(5): 1101-1110.
DOI：

LI Qiang, ZOU Xiaoming, REN Bixing, et al. Parameter Optimization Strategy for Wind Generator Converters Based on Actor-Critic Framework[J]. 2025, 42(5): 1101-1110. DOI： 10.19725/j.cnki.1007-2322.2023.0310.

摘要

随着可再生能源并网发电量的不断增加，由电力电子设备引发的电力系统次同步振荡问题逐渐凸显，给电力系统的安全稳定运行带来了新的挑战。除此之外，当目标电力系统规模较大时，常用的基于线性化模型的分析方法面临着维数灾难。为了解决上述问题，根据强化学习原理，通过动作-评价(Actor-Critic)学习框架提出一种对风机换流器控制参数的优化策略。通过搜集永磁直驱风机(permanent magnetic synchronous generator，PMSG)运行状态数据，训练强化学习代理(Agent)，以此评估风机运行状态及其稳定性，并寻找优化风机换流器参数的最优策略。该训练方法得到的代理能够基于时域采样数据对风机换流器参数进行优化，从而有效抑制由于换流器诱发的振荡现象，在没有建立线性化分析模型的情况下，能够有效优化并增强电力系统的稳定性。经实验验证，该优化策略在采样数据有噪声干扰的情况下仍然具有良好的优化性能。

Abstract

With the increasing power generation capacity of renewable energy connected to the grid

the issue of power system sub-synchronous oscillation caused by power electronic equipment becomes prominent

posing new challenges to the safe and stable operation of the power system. The commonly used analysis methods based on linearized models encounter the problem of dimensionality disaster when dealing with large-scale target power systems. To address the above issues

in this paper we propose an optimization strategy for the control parameters of wind generator converters based on the principle of reinforcement learning and utilizes the actor-critic learning framework. With the collected operating status data of permanent magnet synchronous generator (PMSG)

a reinforcement learning agent (Agent) is trained to evaluate the PMSG's operating status and stability based on the proposed return function and determines an optimal strategy for wind generator converter parameters optimization. The agent obtained by this training method can optimize the parameters of wind generator converters based on time-domain sampling data

effectively suppressing the oscillation phenomenon induced by the converters. Moreover

it is not only able to optimize but also enhance the stability of the power system without the need for a linearized analysis model. The optimization strategy demonstrates superior optimization performance in the presence of noise interference in the sampled data

as confirmed through verification.

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