Autonomous drone for wind turbine blade maintenance
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The rapid growth of wind energy as a renewable energy source has shown the need for safer, faster, and more cost-effective solutions for wind turbine maintenance. It’s essential to inspect turbine blades regularly to detect defects such as cracks and erosion, which can reduce the turbine’s efficiency, increase its operational costs, or even produce safety risks if left unaddressed. The thesis proposes an autonomous drone-based solution for inspecting wind turbine blades in operation, to replace old inspection methods that are costly and time-consuming. The key challenge that this thesis tries to solve is the synchronization of the drone’s movement with the rotating wind turbine blades, allowing the drone to recognize the defects in the turbine blades and to collect images or other sensor data of potential defects. Precise tracking of a moving blade point requires advanced control of the drone and careful consideration of the environmental conditions. To explore this, proof-of-concept simulation setups were developed in MATLABSimulink, using a PID-based controller design to control the drone. Different controller designs were tested and compared to evaluate their effect on the drone’s capability to follow the blade point. Tracking performance was evaluated using root mean square error (RMSE) and standard deviation (SD). The results show that synchronization between the drone and the moving wind turbine blade can be achieved under simplified simulation conditions. The results indicate that simulation design and control configuration play a more significant role in tracking accuracy than the environmental conditions alone. These findings provide a foundation for further research toward more realistic autonomous wind turbine blade inspection systems that are capable of inspecting real wind turbines, offering the potential to reduce their maintenance costs and increase their reliability.