(Universiti Teknikal Malaysia Melaka (UTeM), Malaysia)(2) Zuraida Abal Abas
(Universiti Teknikal Malaysia Melaka (UTeM), Malaysia)(3) Siti Azirah Asmai
(Universiti Teknikal Malaysia Melaka (UTeM), Malaysia)(4) Muhamad Nabil Hidayat
(Universiti Teknologi MARA Shah Alam, Malaysia)*corresponding author
AbstractParticle swarm optimization (PSO), a technique in Artificial Intelligence, is one of the MPPT methods used to optimize the output of a Photovoltaic (PV) system. The PSO is well known for its convergence in Maximum Power Point Tracking (MPPT). However, no comprehensive study has been conducted on the performance of the PSO and incremental-conductance (INC) MPPT combination for the NTR 5E3E PV module. This study aims to provide a detailed performance analysis of the convergence of PSO and INC combination compared to PSO MPPT during maximum power (MP) tracking on NTR 5E3E PV module. This research work studies the relationships among PV parameters and other parameters affected during the implementation of PSO-INC MPPT. The study found that, in terms of efficient power and time consumption during the Maximum Power (MP) tracking process, the PSO-INC MPPT combination provides the highest average peak power at the shortest time compared to standalone PSO. The efficiency of PSO-INC Average Power is near 98.9% to 99.93%, compared to PSO MPPT, which is between 95.7% and 99.3%. The PSO and INC MPPT were tested on a boost converter without altering the specific electrical component characteristics to ensure accurate output during testing. Furthermore, a boost converter is sufficient to meet the overall requirements for the research work and simulation testing. The characteristics of the PSO and INC MPPT are observed using MATLAB/Simulink. This research assesses the robustness of the PSO-INC combination, advancing hybrid MPPT technology by demonstrating its performance.
KeywordsPSO; MPPT; PV; INC; MATLAB/SIMULINK
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DOIhttps://doi.org/10.26555/ijain.v11i4.2143 |
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