A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Proof-of-concept of a reinforcement learning framework for wind farm energy capture maximization in time-varying wind
In this paper, we present a proof-of-concept distributed reinforcement learning framework for wind farm energy capture maximization. The algorithm we propose uses Q-Learning in a wake-delayed wind farm environment and considers time-varying, though not yet fully turbulent, wind inflow conditions. These algorithm modifications are used to create the Gradient Approximation with Reinforcement Learning and Incremental Comparison (GARLIC) framework for optimizing wind farm energy capture in time-varying conditions, which is then compared to the FLOw Redirection and Induction in Steady State (FLORIS) static lookup table wind farm controller baseline.
Proof-of-concept of a reinforcement learning framework for wind farm energy capture maximization in time-varying wind
In this paper, we present a proof-of-concept distributed reinforcement learning framework for wind farm energy capture maximization. The algorithm we propose uses Q-Learning in a wake-delayed wind farm environment and considers time-varying, though not yet fully turbulent, wind inflow conditions. These algorithm modifications are used to create the Gradient Approximation with Reinforcement Learning and Incremental Comparison (GARLIC) framework for optimizing wind farm energy capture in time-varying conditions, which is then compared to the FLOw Redirection and Induction in Steady State (FLORIS) static lookup table wind farm controller baseline.
Proof-of-concept of a reinforcement learning framework for wind farm energy capture maximization in time-varying wind
Stanfel, P. (author) / Johnson, K. (author) / Bay, C. J. (author) / King, J. (author)
2021-07-01
14 pages
Article (Journal)
Electronic Resource
English
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