4.7 Article

Multi-Agent Reinforcement Learning Control of a Hydrostatic Wind Turbine-Based Farm

Journal

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
Volume 14, Issue 4, Pages 2406-2416

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSTE.2023.3270761

Keywords

Wind farms; Wind turbines; Reinforcement learning; Power generation; Wind farm control; hydrostatic wind turbines; multi-agent reinforcement learning; power generation

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This paper proposes a control system for a wind farm with a new type of wind turbines using multi-agent reinforcement learning. The multi-agent policy optimization algorithm allows the turbines to gradually improve their control policies, leading to increased power generation in the wind farm.
This paper leverages multi-agent reinforcement learning (MARL) to develop an efficient control system for a wind farm comprising a new type of wind turbines with hydrostatic transmission. The primary motivation for hydrostatic wind turbines (HWT) is increased reliability, and reduced manufacturing, operating, and maintaining costs by removing troublesome components and reducing nacelle weight. Nevertheless, the high system complexity of HWT and the wake effect pose significant challenges for the control of HWT-based wind farms. We therefore propose a MARL algorithm named multi-agent policy optimization (MAPO), which allows agents (turbines) to gradually improve their control policies by repeatedly interacting with the environment to learn an optimal operation curve for wind farms. Simulation results based on a wind farm simulator, FAST.Farm, show that MAPO outperforms the greedy policy and a popular learning-based method, multi-agent deep deterministic policy gradient (MADDPG), in terms of power generation.

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