부산대학교 도서관

In the conventional yaw control, the wind direction measurements obtained from sensors installed on the wind turbine nacelle are used. However, the wind direction based on this primitive approach would result in a yaw misalignment, because the wind direction taken by the nacelle-mounted sensors are strongly influenced by the rotor rotation. To mitigate the yaw misalignment, we have developed a new system to estimate inflow into a wind turbine based on an AI (Artificial Intelligence) technique with DNN (Deep Neural Network). In this research, we implemented this new AI-based technique on an actual wind turbine control system by adding an external module. This paper reports the impact of using the AI-based inflow estimation system for wind turbine yaw control from an experiment using an upwind wind turbine. As a result, the annual energy production of the wind turbine using the system was assumed to increase.