Linear-Extrapolation-Based Gray-Wolf Optimization Algorithm for Global Maximum Power Tracking of Thermoelectric Generators

Abstract

This article proposes a linear-extrapolation-based gray wolf optimization algorithm (LEGWO) for tracking the global maximum power point (GMPP) of a thermoelectric generation (TEG) system in non-uniform temperature conditions (NTCs). The tracking accuracy and speed of the GMPP algorithm are critical to harvesting as much power as possible from the TEG system. The LEGWO searches a specific area that includes the GMPP based on a gray wolf optimization algorithm (GWO) and uses the inherent linear characteristic of TEG within this area. It enables direct GMPP tracking without additional iterations using the inherent characteristics and the linear extrapolation principle within the specific area. Therefore, the proposed algorithm can track the GMPP faster than the conventional GWO. The performance of the proposed algorithm was compared with the conventional metaheuristic algorithms and a perturbation and observation algorithm. This comparative study was conducted through MATLAB/Simulink simulations and hardware-in-the-loop experiments in various static NTCs and dynamic/stochastic temperature-change conditions. The results demonstrated that the proposed algorithm outperforms the existing algorithms regarding the ability of GMPP tracking, tracking time, energy loss, and efficiency.