A review of wind power generation steady-state reactive power support requirements and improvement strategies

The penetration of wind power generation (WPG) facilities into power systems continues to increase globally. Wind power generation facilities have become increasingly important in providing reactive power support to help regulate power system voltage. To ensure that WPG facilities can provide adequate support, grid codes have been developed with specific requirements that must be met before they can reach commercial operation. However, WPG facilities are sometimes unable to meet the required reactive power support levels. Controller based solutions are commonly used to improve the reactive power capability of WPG facilities. This paper reviews recent developments in control strategies. Their response speed, benefits, and limitations are discussed to identify gaps and to propose future improvements. Current control strategies are not implemented using hybrid control structures, and mostly rely on classical and metaheuristic optimization algorithms. These control strategies can be slow, and sometimes increase the operation of the WPG facility's grid integration transformer on-load tap changer. Machine learning based hybrid control strategies have the potential to improve performance and enable WPG facilities to efficiently provide reactive power support. • Wind power generation reactive power control grid code requirements are reviewed. • Wind power generation reactive power control strategies are discussed. • Reactive power control strategies are classified into three control structures. • Machine learning based hybrid control strategies are proposed to control reactive power production.

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Publication Details

Journal
Renewable and Sustainable Energy Reviews
Published
2026-02-18
DOI
https://doi.org/10.1016/j.rser.2026.116833
Primary Topic
Wind Turbine Control Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

A review of wind power generation steady-state reactive power support requirements and improvement strategies

R.C. Bansal, S. Ncwane
Renewable and Sustainable Energy Reviews
Wind Turbine Control Systems
article

A review of wind power generation steady-state reactive power support requirements and improvement strategies

R.C. Bansal, S. Ncwane
article en

Abstract

The penetration of wind power generation (WPG) facilities into power systems continues to increase globally. Wind power generation facilities have become increasingly important in providing reactive power support to help regulate power system voltage. To ensure that WPG facilities can provide adequate support, grid codes have been developed with specific requirements that must be met before they can reach commercial operation. However, WPG facilities are sometimes unable to meet the required reactive power support levels. Controller based solutions are commonly used to improve the reactive power capability of WPG facilities. This paper reviews recent developments in control strategies. Their response speed, benefits, and limitations are discussed to identify gaps and to propose future improvements. Current control strategies are not implemented using hybrid control structures, and mostly rely on classical and metaheuristic optimization algorithms. These control strategies can be slow, and sometimes increase the operation of the WPG facility's grid integration transformer on-load tap changer. Machine learning based hybrid control strategies have the potential to improve performance and enable WPG facilities to efficiently provide reactive power support. • Wind power generation reactive power control grid code requirements are reviewed. • Wind power generation reactive power control strategies are discussed. • Reactive power control strategies are classified into three control structures. • Machine learning based hybrid control strategies are proposed to control reactive power production.

Renewable and Sustainable Energy ReviewsVol. 232
University of Sharjah (AE), University of Pretoria (ZA)
Affordable and clean energy
Openalex Percentile: Top 51%
Wind Turbine Control Systems
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