Print Email Facebook Twitter Multi Agent Deep Deterministic Policy Gradient for Active Wake Control Title Multi Agent Deep Deterministic Policy Gradient for Active Wake Control Author van der Schaaf, Guus (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor de Weerdt, M.M. (mentor) Neustroev, G. (mentor) Pawełczak, Przemysław (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2023-06-29 Abstract In wind farms wind turbines are often placed close to each other. Each turbine generates a turbulent wake field, this field negatively affects subsequent turbines. This can cost more than 12% efficiency. To decrease this loss we can steer the turbines away from the wind direction, this will decrease the individual turbine power output, but can increase the total power output of the farm. As the size of the farm increases the number of possible actions increase exponentially. Due to this a numerical solution is not feasible. A reinforcement learning technique has been proven useful in the past, but a standard single agent implementation is still very computationally expensive. We evaluate the effectiveness of MADDPG on the active wake control problem. MADDPG is a multi agent reinforcement learning algorithm. MADDPG will be compared to the numerical solver FLORIS and to the already implemented and proven TD3 (which is a variation on a single agent DDPG algorithm). We compare the eventual output power of the algorithms with MADDPG. From the results we can see that MADDPG does improve on the learning performance of TD3, but since MADDPG needs to manage more neural networks the overhead is larger. MADDPG reaches an optimum solution in less training steps, but these steps take significantly more time. Subject Reinforcement LearningMulti Agent Reinforcement LearningMARLActive Wake ControlDeep Learning To reference this document use: http://resolver.tudelft.nl/uuid:0915d6b9-f8a0-4097-bfa9-490cbe5e942b Part of collection Student theses Document type bachelor thesis Rights © 2023 Guus van der Schaaf Files PDF CSE3000_Research_Paper_Gu ... Schaaf.pdf 288.53 KB Close viewer /islandora/object/uuid:0915d6b9-f8a0-4097-bfa9-490cbe5e942b/datastream/OBJ/view