Treffer: An efficient linear-extrapolation catch-fish algorithm for maximizing the harvested power from thermoelectric generators sources.

• LE-CFOA is proposed to design MPPT for TEG system at non-uniform heat distribution. • LE-CFOA based-tracker improves GMP tracking speed and overall dynamic performance. • LE-CFOA is compared to CSA, DFO, GHO, GWO, INC, P&O, PSO, and PSOGSA. • The robustness of LE-CFOA is confirmed by HIL to validates implementation feasibility. This paper proposes a novel catch-fish optimization algorithm with linear extrapolation (LE-CFOA) to efficiently track the global maximum power (GMP) of thermoelectric generator (TEG) systems under irregular temperature conditions (ITCs). The integration of TEGs with batteries not only provides more stable power output but also allows for the development of self-powered health monitoring systems, potentially slowing battery degradation and improving overall system longevity. The proposed LE-CFOA improves upon the traditional CFOA by using the linear characteristics of TEGs within a specific search region around the GMP, enabling faster and more accurate GMP tracking without extra iterations. The performance of the proposed method was rigorously evaluated against several state-of-the-art algorithms, including CSA, DFO, GHO, GWO, IMFO, P&O, INC, PSO, and PSOGSA, through hardware-in-the-loop (HIL) experiments and MATLAB/Simulink simulations. The proposed method demonstrated superior performance, achieving a tracking efficiency of 99.92 %, exceeding CSA (99.54 %) and PSOGSA (99.85 %), with a significantly reduced tracking time of 0.003 s compared to CSA (0.5 s) and IMFO (0.33 s). Additionally, it achieved a power output of 133 W in static ITC scenarios, surpassing DFO (124 W) and GWO (105 W), while minimizing energy loss to 0.003 W, substantially lower than CSA (0.3 W) and GHO (26.1 W). These findings hold significant promise for modern applications such as wheelchairs and electrical vehicles, where thermoelectric systems can be employed to power auxiliary systems, including temperature regulation, energy recovery, and battery charging. The results confirm that LE-CFOA outperforms other algorithms in terms of tracking speed, efficiency, tracking time and energy loss under static, dynamic, and stochastic temperature variations. Finally, this paper offers valuable insights for TEG specialists and researchers working on TEG MPPT techniques. [ABSTRACT FROM AUTHOR]

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