Grey wolf optimizer-based fractional MPPT for thermoelectric generator

The energy harvested from a thermoelectric generator (TEG) relies mostly on the difference in temperature between the hot side and cold side of the TEG along with the connected load. Hence, a reliable maximum power point tracker is needed to force the TEG to operate close to the maximum power point (MPP) with any variation during the operation. In the current work, an optimized fractional maximum power point tracker (OFMPPT) is proposed to improve the performance of the TEG. The proposed tracker is based on fractional control. The optimal parameters of the OFMPPT have been determined using the grey wolf optimizer (GWO). To prove the superiority of GWO, the results are compared with particle swarm optimization (PSO) and genetic algorithm (GA). The largest fitness function, the lowest standard deviation, and the maximum efficiency are achieved by GWO. The goal of the suggested OFMPPT is to overcome the two main issues in conventional trackers. They are the slow dynamic of the traditional incremental resistance tracker (INRT) and high steady-state fluctuation around the MPP in the very common perturb & observe tracker (POT). The main finding confirmed the superiority of OFMPPT compared with INRT and POT for both dynamic and steady-state responses.