Maximum energy harvesting of centralized thermoelectric power generation systems with non-uniform temperature distribution based on novel equilibrium optimizer

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Recently thermoelectric generation (TEG) systems are gaining popularity due to their increased power density and wide applications in industrial and energy conversion applications. This paper presents a novel optimal control technique for TEG systems under non-uniform temperature distribution (NUTD). Although centralized MPPT control can reduce the footprint and cost of MPPT of TEG systems and provide excellent advantages over the cross-tied, string, and distributive control configurations, it suffers from drawbacks caused by NUTD on the hot or cold sides of TEG modules. Specifically, the bypass diodes streamline the power flow in centralized TEG systems, which may generate multiple local maxima in electrical characteristics curves of TEG systems. To resolve such issues, this paper implements a novel MPPT control technique based on a swarm intelligence (SI)-based optimization algorithm, Equilibrium Optimization (EQO). To study the effectiveness of the proposed EQO technique, five distinct case studies were used. Additional sensitivity analysis and statistical studies were also done to analyze the comparative performance. The results show that the power tracking efficiency can be increased to 99.68% with 1.8–8% more energy harvesting. The stable voltage and current transients can be achieved with a min tracking time of 180 ms, achieving up to 46% faster GM tracking and 238% faster settling time than some conventional MPPT control techniques. In light of experimental simulations, it is safe to conclude that the proposed EQO-based technique outperforms the existing control techniques for TEG systems under all concerned operating conditions.