Investigating The Effect of Various Converters & Implementation of Golden Eagle Optimization in Determining MPP in Solar Cells.

Abstract

Solar panels are frequently subjected to varying degrees of irradiance which causes the panels to work at a lower efficiency than normal. Maximum power point tracking (MPPT) attempts to maximize the power produced by the solar panels. Recent methods have used intelligent optimization algorithms in MPPT to reduce the tracking time to as low as possible. The Golden Eagle Optimization (GEO) algorithm is a recent algorithm inspired by the prey selection and hunting criteria of Golden Eagles which has shown promising results in other benchmark tests. The purpose of this thesis is to investigate the viability of GEO as a possible algorithm for use in MPPT along with finding the effect of different converters on the MPPT circuit. The GEO algorithm was tested by constructing a MATLAB Simulink model of a typical solar panel circuit and simulating the circuit under different irradiances. The converter used in the circuit was varied between Buck, Boost & Buck-Boost for every test case. In order to test the viability and performance of the GEO algorithm, two other well-known algorithms that are used in MPPT were also simulated in the constructed circuit using the same test cases that were used for GEO. The results obtained for all three algorithms were compared in terms of settling time, convergence time, and maximum obtained power. The outputs of the algorithms were also compared in terms of converters and it was found that the boost converter provided the most desirable output. The comparisons also show that even though GEO tends to oscillate at the beginning of the search procedure, it outperforms the other two algorithms in terms of converging time and settling time in almost all of the test cases.