Optimal control of centralized thermoelectric generation system under nonuniform temperature distribution using barnacles mating optimization algorithm

The need for renewable energy resources is ever-increasing due to the concern for environmental issues associated with fossil fuels. Low-cost high-power-density manufacturing techniques for the thermoelectric generators (TEG) have added to the technoeconomic feasibility of the TEG systems as an effective power generation system in heat recovery, cooling, electricity, and engine-efficiency applications. The environment-dependent factors such as the nonuniform distribution of heat, damage to the heat-transfer coating between sinks and sources, and mechanical faults create nonuniform current generation and impedance mismatch causing power loss. As a solution to this nonlinear multisolution problem, an improved MPPT control is presented, which utilizes the improvised barnacle mating optimization (BMO). The case studies are formulated to gauge the performance of the proposed BMP MPPT control under nonuniform temperature distribution. The results are compared to the grey wolf optimization (GWO), particle swarm optimization (PSO), and cuckoo search (CS) algorithm. Faster global maximum power point tracking (GMPP) within 381 ms, higher power tracking efficiency of up to 99.93%, and least oscillation ≈0.8 W are achieved by the proposed BMO with the highest energy harvest on average. The statistical analysis further solidifies the better performance of the proposed controller with the least root mean square error (RMSE), RE, and highest SR.