Abstract
Wind farm layout optimization is formulated as a deep reinforcement learning (DRL) problem by defining the state, action, reward variables in the setup. A policy gradient reinforcement learning algorithm, proximal policy optimization (PPO), is applied and a deep convolutional neural network is designed to process the high-dimensional state and generate policy (actions given states) and value estimations. The DRL is compared with traditional genetic algorithm (GA) and shows promising results in the wind turbine layout optimization.
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