Importance

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Abstract

The present invention concerns a method for controlling a wind farm comprising wind turbines, each turbine being suitable for taking a plurality of states, the method comprising the following steps:

obtaining configuration data,obtaining experience data,training a model for determining actions enabling to control each turbine of the wind farm depending on the state of each turbine, the model being trained in a training environment on the basis of the experience data so as to maximize a reward function, the training environment being a multi-agent reinforcement learning environment, each agent corresponding to a different turbine of the wind farm, the reward function being relative to the energy produced by the wind farm.

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First Claim