Wind Farm Power Generation Control Via Double-Network-Based Deep Reinforcement Learning

Jingjie Xie, Hongyang Dong, Xiaowei Zhao, Aris Karcanias

Research output: Journal article publicationJournal articleAcademic researchpeer-review

24 Citations (Scopus)

Abstract

A model-free deep reinforcement learning (DRL) method is proposed in this article to maximize the total power generation of wind farms through the combination of induction control and yaw control. Specifically, a novel double-network (DN)-based DRL approach is designed to generate control policies for thrust coefficients and yaw angles simultaneously and separately. Two sets of critic-Actor networks are constructed to this end. They are linked by a central power-related reward, providing a coordinated control structure while inheriting the critic-Actor mechanism's advantages. Compared with conventional DRL methods, the proposed DN-based DRL strategy can adapt to the distinctive and incompatible features of different control inputs, guaranteeing a reliable training process and ensuring superior performance. Also, the prioritized experience replay strategy is utilized to improve the training efficiency of deep neural networks. Simulation tests based on a dynamic wind farm simulator show that the proposed method can significantly increase the power generation for wind farms with different layouts.

Original languageEnglish
Pages (from-to)2321-2330
Number of pages10
JournalIEEE Transactions on Industrial Informatics
Volume18
Issue number4
DOIs
Publication statusPublished - 1 Apr 2022
Externally publishedYes

Keywords

  • Model-free control
  • power generation control
  • reinforcement learning (RL)
  • wind farm control

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering

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