Multi-scenario Reactive Voltage Control Strategy for Wind Farms Based on Self-adaptive MPC

LAN Kunkai; WANG Wei; YANG Jian; WANG Hua; REN Xin

doi:10.19805/j.cnki.jcspe.2025.240380

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Multi-scenario Reactive Voltage Control Strategy for Wind Farms Based on Self-adaptive MPC

更新时间：2026-10-10

- Multi-scenario Reactive Voltage Control Strategy for Wind Farms Based on Self-adaptive MPC
- Chinese Journal of Power Engineering Vol. 45, Issue 8, Pages: 1251-1260(2025)
- 作者机构：
  
  1. 华北电力大学控制与计算机工程学院,北京,102206
  2. 中国华能集团清洁能源技术研究院有限公司,北京,102209
- 作者简介：
- 基金信息：
- DOI：10.19805/j.cnki.jcspe.2025.240380
  CLC：
- Online First：12 August 2025，
  
  Published：2025
- 稿件说明：
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蓝琨凯,王玮,杨健,王华,任鑫. 基于自适应MPC的风电场多场景无功电压控制策略动力工程学报, 2025, 45(8): 1251-1260 https://doi.

org/10.19805/j.cnki.jcspe.2025.240380
蓝琨凯,王玮,杨健,王华,任鑫. 基于自适应MPC的风电场多场景无功电压控制策略动力工程学报, 2025, 45(8): 1251-1260 https://doi. DOI： 10.19805/j.cnki.jcspe.2025.240380.

org/10.19805/j.cnki.jcspe.2025.240380 DOI：

摘要

为提升风电场在面对外界不确定因素时的灵活性和电压稳定性

提出了一种基于自适应模型预测控制(MPC)的多场景无功电压优化控制策略。基于灵敏度矩阵建立了无功电压预测模型

针对风速预测信息给出评价指标并进行场景划分

以预测周期内多个时刻的电压偏差和有功损耗最小为控制目标

自适应调整各场景下的权重配比

实现滚动优化;使用小龙虾优化算法(COA)求解优化模型

得到无功优化调度方案;最后根据寻优结果和预测误差进行反馈校正。以我国"三北"地区某风电场为例进行仿真。结果表明:相较于传统开环控制和MPC控制下

所提策略下全天平均电压偏差分别降低82.46%和67.74%

从而验证了该控制策略的可行性和有效性。

Abstract

In order to enhance the flexibility and voltage stability of wind farms facing external uncertainties

a multi-scenario reactive voltage optimization control strategy based on self-adaptive model predictive control (MPC) was proposed. A reactive voltage prediction model was established using sensitivity matrices

and scenario segmentation was conducted based on evaluation criteria derived from short-term wind speed forecasts. The control objective was to minimize voltage deviations and active power losses at multiple time points within the prediction horizon. Adaptive adjustment of weight ratios in various scenarios facilitated rolling optimization. Crawfish optimization algorithm (COA) was employed to solve the optimization model and derive reactive power optimization schedules. Feedback correction based on optimization results and prediction errors was implemented. Simulation was conducted using a wind farm in the "Three-North" regions of China. Results show that compared with traditional open-loop control and MPC

the proposed strategy reduces daily average voltage deviations by 82.46% and 67.74%

respectively

validating the feasibility and effectiveness of this control strategy.

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references

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