Dataset Preprocessing for MPPT Algorithms Simulation and Thermoelectric Generatorsunder Multiple Thermal Gradient Scenarios

Thermoelectric generators (TEGs), based on the Seebeck effect, offer a promising solution for energy harvesting in environments with thermal gradients. Despite their structural simplicity and modular scalability, TEGs face limitations in conversion efficiency, which are compounded under dynamic conditions. This study presents a comprehensive comparative analysis of four maximum power point tracking (MPPT) algorithms\u2014Perturb and Observe (P&O), Incremental Conductance (InC), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA)\u2014implemented in MATLAB\/Simulink and applied to a boost converter coupled with TEG arrays. Simulations were conducted under thirteen thermal scenarios, including ideal and asymmetrical gradient distributions, some presenting multiple power peaks. The results indicate that P&O and InC perform adequately in single-peak conditions but struggle with oscillation and accuracy in complex scenarios. In contrast, PSO and GA exhibit superior robustness and convergence speed under multiple local maxima, with slightly reduced precision in isolated cases. Limitations of the simulation include idealized thermal and electrical models, which may affect real-world replicability. Future directions involve developing physical prototypes, refining thermal models with ambient interactions, and exploring hybrid MPPT strategies. The findings contribute to optimizing control algorithms for thermoelectric systems under variable operating conditions.