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Intelligent Fault-Tolerant Active Power Control Using Reinforcement Learning for Offshore Wind Farms

Authors :
Xuanhe Zhang
Hamed Badihi
Saeedreza Jadidi
Ziquan Yu
Youmin Zhang
Source :
IEEE Access, Vol 12, Pp 83782-83795 (2024)
Publication Year :
2024
Publisher :
IEEE, 2024.

Abstract

Given the continuous development of society and the escalating demand for clean energy, there is an imperative focus on wind farm control to overcome the primary obstacle hindering wind farm development: high operation and maintenance costs. This paper presents innovative solutions for intelligent fault-tolerant active power control design based on reinforcement learning, aiming to optimize the balance between grid load and wind farm active power. The proposed solutions effectively handle a range of fault scenarios, addressing both active power control and frequency regulation while safeguarding faulty wind turbines against further deterioration. Through comprehensive simulations conducted on a wind farm benchmark model, the efficacy of these solutions and strategies is demonstrated, showcasing their ability to achieve both passive and active fault-tolerant control across diverse load and fault scenarios.

Details

Language :
English
ISSN :
21693536
Volume :
12
Database :
Directory of Open Access Journals
Journal :
IEEE Access
Publication Type :
Academic Journal
Accession number :
edsdoj.5fa0772e4f314da8bdc4fbf1b0943cdd
Document Type :
article
Full Text :
https://doi.org/10.1109/ACCESS.2024.3413339