Maximum Power Point Tracking Using a Multimode Hunter-Prey Optimization Algorithm for Gradually Varying Power in Centralized Thermoelectric Generation System

The unstructured power outputs from thermoelectric generator (TEG) systems have precipitated the advancement of maximum power point tracking (MPPT) methods toward more intelligent solutions. This article concentrates on centralized TEG systems which work during nonuniform distribution of temperature cases and introduce the hunter-prey optimization algorithm with multimode updating (MMUHPO). Existing MPPT methods based on metaheuristic algorithms pay attention to improving tracking speed, efficiency, oscillation, and call frequency. However, in scenarios marked by gradual changes in power characteristics, the precision of MPPT algorithms can lead to frequent oscillatory searches and elevated computational demands. MMUHPO reduces power loss and simplifies algorithm complexity by improving the original mechanism and introducing termination mechanism and multimode update mechanism. Herein, the proposed algorithm is compared with several widely used MPPT algorithms based on metaheuristic algorithms under five different operating conditions. Remarkably, the MMUHPO algorithm demonstrates a substantial enhancement in energy collection efficiency, with the increase in efficiency reaching up to 109.8%. Finally, real-time data simulation experiments corroborate the exceptional tracking performance of the proposed algorithm.