DROP: Deep Reinforcement Learning Based Optimal Perturbation for MPPT in Wind Energy

The fluctuating nature of wind energy has inspired researchers to look for a fast, efficient Maximum Power Point Tracking (MPPT) algorithm. The MPPT method aims to harness maximum power at varying wind speeds by adjusting rotor speed. Our contribution to the wind MPPT task is twofold. First, we use a predictive model to map the current operating point of the turbine speed and output power to the optimal operating point (i.e., optimal turbine speed for maximum output power). Second, we propose a Deep Reinforcement Learning based solution that provides adaptive speed control to reach the MPP fast and precisely. Our experimental results demonstrate the superior performance of our method compared with the existing techniques.