Work

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

Year: 2026

Type: article

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.

Source: Renewable and Sustainable Energy Reviews

DOI: 10.1016/j.rser.2026.116833

Authors: S. Ncwane, R.C. Bansal

Institutions: University of Pretoria, University of Sharjah

Language: English

Topic: Wind Turbine Control Systems

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $5,220