Authors : Meng Chung Tiong; Thomas Shan Yau Moh; Ling Ai Wong; Jonny Ti Siong Tie

Volume/Issue : Volume 10 - 2025, Issue 3 - March

Google Scholar : https://tinyurl.com/byhs3wpk

Scribd : https://tinyurl.com/4mw3zs39

DOI : https://doi.org/10.38124/ijisrt/25mar098

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Abstract : This paper presents a study in maximum power point tracking (MPPT) algorithm in solar photovoltaic (PV) using Binary Search Algorithm (BSA). With the increasing popularity in solar power generation, the effort of extracting maximum power from the installed capacity remains a challenge. This study aims to identify the performance of the Binary Search Algorithm under constant irradiance conditions and dynamic change irradiance conditions. A simulation model of BSA was developed and implemented using DC/DC boost converter in MATLAB Simulink. For the purpose of comparison, the performance of the BSA was evaluated together with another well-established algorithm, Particle Swarm Optimization (PSO). Both algorithms were evaluated under 10 constant irradiance test cases and 6 dynamic changing irradiance test cases. The BSA has shown its capability in tracking for maximum power under both constant and dynamic changing irradiance conditions. For most of the cases, the BSA was able to achieve the maximum power operating point with efficiency up to 99%. It was found that both BSA and PSO were having the tendency to experience premature convergence, which leads to slight power losses during the operation.

Keywords : Maximum Power Point Tracking, Solar Photovoltaic, Binary Search, Particle Swarm Optimization.

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