Implementation of the MPPT Particle Swarm Optimization Algorithm on a Hybrid PV-TEG Solar Panel with a DC-DC Buck Converter

Abstract

Abstract Solar panels necessitate power control to locate the optimal working point amidst quickly changing voltages and diverse weather circumstances. This enables the system to adapt and sustain optimal performance in real-time. In order to address this issue, the optimization methodology known as Maximum Power Point Tracking (MPPT) is employed using an algorithmic artificial intelligence (AI) method. Particle Swarm Optimization (PSO) is a swarm intelligence technique that has effectively tackled diverse optimization issues in intricate systems. The DC-DC Buck Converter, which incorporates Maximum Power Point Tracking (MPPT), serves as an interface between the load and the photovoltaic (PV) system to control the output voltage of the system. A comparison is made between the performance of the Maximum Power Point Tracking (MPPT) technique using the Particle Swarm Optimization (PSO) algorithm and the hill climbing (HC) algorithm developed through MATLAB/SIMULINK simulations. The findings indicate that the PSO algorithm exhibits superior performance in terms of tracking time, output power, and stability, with little fluctuations or noise, as compared to the HC method. The tracking time is 0.02 seconds and 2.52 seconds, respectively. The power at constant irradiation is 14.49 Watts and 14.43 Watts. The power at irradiation variation is 6.98 Watt and 6.96 Watt. The PSO algorithm achieved a remarkable accuracy of 99.76% in this investigation, surpassing that of HC. This enhancement makes the system more effective in acquiring maximum power values.