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Improving DFIG performance under fault scenarios through evolutionary reinforcement learning based control

Improving DFIG performance under fault scenarios through evolutionary reinforcement learning... The doubly fed induction generator (DFIG) usually experiences high rotor current and DC capacitor link voltage spikes during system fault events. In this paper, a novel data‐driven approach is proposed to enhance DFIG performance under fault scenarios. An advanced reinforcement learning algorithm called guided surrogate‐gradient‐based evolution strategy (GSES) is used to control the DFIG power and capacitor DC‐link voltage by adjusting the optimal reference signals. This controller is able to prevent the DFIG rotor from over‐current risk and maintain grid‐connected operation. The proposed GSES‐based control algorithm was evaluated through simulations on a 3.6‐MW DFIG in the PSCAD/EMTDC software. Results have validated the effectiveness of the proposed GSES‐based control algorithm in improving DFIG performance under various fault scenarios. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png "IET Generation, Transmission & Distribution" Wiley

Improving DFIG performance under fault scenarios through evolutionary reinforcement learning based control

Improving DFIG performance under fault scenarios through evolutionary reinforcement learning based control

"IET Generation, Transmission & Distribution" , Volume 16 (19) – Oct 1, 2022

Abstract

The doubly fed induction generator (DFIG) usually experiences high rotor current and DC capacitor link voltage spikes during system fault events. In this paper, a novel data‐driven approach is proposed to enhance DFIG performance under fault scenarios. An advanced reinforcement learning algorithm called guided surrogate‐gradient‐based evolution strategy (GSES) is used to control the DFIG power and capacitor DC‐link voltage by adjusting the optimal reference signals. This controller is able to prevent the DFIG rotor from over‐current risk and maintain grid‐connected operation. The proposed GSES‐based control algorithm was evaluated through simulations on a 3.6‐MW DFIG in the PSCAD/EMTDC software. Results have validated the effectiveness of the proposed GSES‐based control algorithm in improving DFIG performance under various fault scenarios.

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Publisher
Wiley
Copyright
© 2022 The Institution of Engineering and Technology.
eISSN
1751-8695
DOI
10.1049/gtd2.12563
Publisher site
See Article on Publisher Site

Abstract

The doubly fed induction generator (DFIG) usually experiences high rotor current and DC capacitor link voltage spikes during system fault events. In this paper, a novel data‐driven approach is proposed to enhance DFIG performance under fault scenarios. An advanced reinforcement learning algorithm called guided surrogate‐gradient‐based evolution strategy (GSES) is used to control the DFIG power and capacitor DC‐link voltage by adjusting the optimal reference signals. This controller is able to prevent the DFIG rotor from over‐current risk and maintain grid‐connected operation. The proposed GSES‐based control algorithm was evaluated through simulations on a 3.6‐MW DFIG in the PSCAD/EMTDC software. Results have validated the effectiveness of the proposed GSES‐based control algorithm in improving DFIG performance under various fault scenarios.

Journal

"IET Generation, Transmission & Distribution"Wiley

Published: Oct 1, 2022

References