Perbandingan Algoritma Particle Swarm Optimization dengan Ant Colony Optimization untuk Mengoptimasi Maximum Power Point Tracking pada Kondisi Partial Shading - Hasan, Fuad; Al-Rasyid, Hasan; Rachmatullah, Moch. Ichsan; Qorib, Moh. Fathul (Jurnal Sains dan Teknologi (JSIT), 2025)
The Solar Power Generation System (PLTS) is a renewable energy solution that is increasingly being adopted. However, its performance is greatly influenced by environmental conditions, particularly the phenomenon of partial shading, which can cause the power curve of solar panels to exhibit multiple local maximum points. This condition makes conventional Maximum Power Point Tracking (MPPT) algorithms struggle to identify the Global Maximum Power Point (GMPP). To address this challenge, various artificial intelligence–based algorithms have been applied. This study aims to compare the performance of two popular optimization algorithms, Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), in optimizing MPPT under partial shading conditions. A quantitative approach with an experimental method was used, where simulations of the solar panel system were conducted using MATLAB/Simulink. Partial shading scenarios were configured to evaluate the robustness of each algorithm in multi-peak conditions. Data collected from the simulations included the maximum power achieved, convergence time, and output stability. The results of this study are expected to provide comparative insights into the effectiveness of both algorithms in handling inconsistent irradiance in PLTS, as well as contribute to the development of more efficient and adaptive intelligent MPPT systems. This research also addresses the gap in comparative studies between PSO and ACO within the context of MPPT for renewable energy systems.