The Assessment and Enhancement of the Efficiency and Dynamic Responses of LSTM-Guided Yaw-Controlled NREL 5 MW Wind Turbines Subjected to Irregular Waves vs. İzmir–Samos Tsunami Waveform

Wind energy is one of the clean and sustainable energy sources that can be used to meet global energy demand. However, although wind turbines are used to harness energy in coastal and offshore areas, the extreme environmental conditions make it difficult to utilize these structures optimally and also negatively impact their safety. Therefore, in this study, the yaw angles of the National Renewable Energy Laboratory (NREL) 5 MW wind turbine mounted on a monopile platform were controlled using a Long Short-Term Memory (LSTM) artificial intelligence (AI) architecture to optimize power output and ensure structural stability against dynamic responses under irregular wave and Izmir–Samos tsunami conditions. First, the İzmir–Samos tsunami, the NREL 5 MW wind turbine mounted on a monopile platform, the analytical methods employed, the LSTM architecture, and the parameters used in the study were described. The results of the wind direction time series prediction and the aerodynamic, hydrodynamic, and structural responses of the LSTM-based yaw angle control strategy on the wind turbines were investigated and discussed. According to the results, the increase in aerodynamic power achieved using the LSTM-based strategy was approximately 5.18% under a scenario with constant wind speed and variable wind direction in two different sea conditions and 5.29% under conditions of irregular waves with fully variable wind speed and direction. In addition, more stable responses were observed for most of the parameters examined. The primary goal of this study is to serve as a reference for researchers working on AI-optimized power output and response analysis resulting from varying the yaw angles of wind turbines.