Erosion-safe operation using double deep Q-learning

Abstract

Abstract Leading edge erosion on wind turbine blades can reduce aerodynamic efficiency and cause increased maintenance costs, potentially impacting the overall economic viability. Erosion-safe operation is the concept of reducing the blade tip speed during periods of heavy rain, thereby significantly reducing the erosion development and progression. This study explores the application of reinforcement learning, namely using a double deep Q-network, to implement erosion-safe operation. The proposed methodology involves learning a policy for tip speed control that maximizes revenue over a specific period of time. We demonstrate the concept based on 5 years of simulation of the DTU 10MW reference turbine and mesoscale weather simulation from Horns Rev. The trained model was found to increase the cumulative revenue by 1.6 % compared to not using erosion-safe operation. The model was able to effectively adapt to varying weather conditions and stochastic damage progression. Based on 10,000 random simulations, the trained model outperforms two baseline models in more than 98 % of the simulations.