Optimizing Maximum Power Point Tracking on Photovoltaic Arrays using Ant Colony Optimization and Particle Swarm Optimization Algorithms

Hasan, Fuad and Suyono, Hadi and Lomi, Abraham (2022) Optimizing Maximum Power Point Tracking on Photovoltaic Arrays using Ant Colony Optimization and Particle Swarm Optimization Algorithms. JOURNAL OF SCIENCE AND APPLIED ENGINEERING, 5 (1). p. 7. ISSN 2621-3745

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Abstract

Solar power plants, in general, cannot produce maximum power by themselves; the characteristics of the PV voltage generally follow the battery voltage or the load that is connected directly to the PV. The intensity of light received by the PV modules does not all get uniform irradiation, so the power produced is not optimal and causes multi-peak. A Maximum Power Point Tracking (MPPT) system is needed to optimize power from PV. However, the often used methods are still trapped in local peaks and long convergence times. In this study, we compare the performance of each algorithm to find the maximum power point (MPP) and tracking time using two methods, namely Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). This study uses 6 selected cases that can occur in 6 solar panel modules arranged in series. Characteristic curves in 6 cases were generated using MATLAB SIMULINK for tracking to find the maximum power point using the ACO and PSO algorithms. The ACO has an efficiency of 99.4910% and tracking failure 7 times in 6 cases in 10 trials of each case, while the PSO algorithm has an efficiency of 99.1043% and tracking failure seven times in 6 cases in 10 trials each case. The efficiency comparison of the ACO algorithm is 0.39% better than the PSO algorithm, while the PSO method is faster in tracking

Item Type:	Article
Uncontrolled Keywords:	Ant Colony Optimization; Particle Swarm Optimization; Maximum Power Point Tracking; Convergent Time.
Subjects:	Engineering > Electrical Engineering
Divisions:	Fakultas Teknologi Industri > Teknik Elektro S1
Depositing User:	Mr Sayekti Aditya Endra
Date Deposited:	25 Apr 2023 12:08
Last Modified:	25 Apr 2023 12:08
URI:	http://eprints.itn.ac.id/id/eprint/11525

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