Dynamic tracking of thermoelectric MPPT using a combination of particle swarm optimization and fuzzy logic

The real-time variation of the maximum power point (MPP) in thermoelectric power generation systems and the performance limitations of traditional tracking strategies are the main problems affecting their energy utilization efficiency. To address this, this paper proposes a dynamic MPP tracking strategy based on particle swarm optimization (PSO) and adaptive adjustment of fuzzy logic control parameters. This method combines PSO-FLC (Particle Swarm Optimization-Fuzzy Logic Control) collaborative control with online adaptive optimization, utilizing the nonlinear adjustment characteristics of fuzzy logic control to handle the complex dynamic behavior of thermoelectric power generators. The system takes the slope and rate of change of the power-voltage curve as input and outputs the duty cycle adjustment. The PSO algorithm iteratively optimizes the membership function parameters and scaling factor of the fuzzy controller by minimizing the fitness function of the tracking error, achieving adaptive updates of the control rules. Experimental results show that the proposed strategy achieves a MPP tracking accuracy of 99.68% under steady-state conditions, a dynamic response time of 0.19 s during temperature step changes, and achieves an energy capture of 5964.00 joules and an average tracking efficiency of 99.40% under long-term dynamic temperature difference conditions. The results show that the proposed method has high control performance in terms of dynamic response, steady-state accuracy, energy capture and system robustness, and has practical significance for improving the energy utilization efficiency of thermoelectric power generation systems.