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All Journal Jurnal Energi Baru dan Terbarukan Journal of Soft Computing Exploration
Faqih, Faiq Mananul
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Environmental Science

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Tren Algoritma InC, PID dan FLC untuk MPPT Pada Sistem Fotovoltaik: Sistematik Review Ananda, Briska Putra; Faqih, Faiq Mananul; Alkindi, Muhammad Faizal; Pribadi, Feddy Setio; Aprilianto, Rizky Ajie
Jurnal Energi Baru dan Terbarukan Vol 5, No 2 (2024): Juli 2024
Publisher : Program Studi Magister Energi, Sekolah Pascasarjana, Universitas Diponegoro, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jebt.2024.23089

Abstract

Terkadang ekstraksi daya pada penggunaan sistem fotovoltaik (PV) kurang maksimal, perubahan radiasi matahari dan temperatur lingkungan menjadi salah satu penyebabnya. MPPT adalah metode untuk memaksimalkan ekstraksi daya dari PV. Beberapa penggunaan algoritma kontrol untuk MPPT pada sistem PV diusulkan. Tujuan penelitian ini adalah untuk mengetahui seberapa efisien metode algoritma yang digunakan untuk MPPT. Kelebihan dari algoritma yang diusulkan juga dibahas. Penelitian ini melakukan tinjauan dari 15 artikel yang diambil dari sumber database Scopus dengan rentang tahun 2020 hingga 2024. Hasilnya menunjukkan bahwa kontroler berbasis PID paling banyak digunakan untuk MPPT. Penggunaan metode kombinasi hingga integrasi Neural Network (NN) menghasilkan nilai efisiensi yang tinggi dibandingkan dengan metode konvensional, tetapi memerlukan komputasi dan resource yang banyak. Systematic Literature Review (SLR) ini bisa menjadi pedoman untuk peneliti dalam mengembangkan algoritma untuk MPPT pada sistem PV di masa mendatang.
Bio-inspired metaheuristic MPPT algorithms for PV battery systems: a comparative performance study Faqih, Faiq Mananul; Aprilianto, Rizky Ajie
Journal of Soft Computing Exploration Vol. 7 No. 1 (2026): March 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i1.13

Abstract

Maximum Power Point Tracking (MPPT) has been proven to improve power extraction in photovoltaic (PV) systems. However, conventional MPPT methods such as Perturb and Observe (P&O) and Incremental Conductance (InC) have limitations, such as oscillations in steady state conditions, slow response, and a tendency to get stuck at local maxima when irradiation changes. This study aims to evaluate biology-inspired metaheuristic algorithms to improve tracking accuracy, convergence speed, and MPPT stability in PV systems. These algorithms include Grey Wolf Optimization (GWO), Sand Cat Swarm Optimization (SCSO), Horse Herd Optimization (HHO), Chameleon Swarm Algorithm (CSA), and Flying Squirrel Search Optimization (FSSO). The algorithms were tested using the same general parameters to ensure a fair comparison. Testing was conducted on PV models, DC boost converters with resistive loads and batteries under static and dynamic irradiation conditions using MATLAB/Simulink. The results show that HHO provides the best performance with an efficiency of 99.96% at 1000 W/m² and 98.03% at 800 W/m², a tracking time of <0.05 seconds, and power fluctuations of <0.3% in resistive load testing. In battery testing, CSA and FSSO showed the best performance with voltage stability, high charging current, and lower ripple. Overall, the results of this study indicate that the proposed metaheuristic-based MPPT algorithm can improve the accuracy of maximum power point tracking, accelerate convergence time, and minimize power oscillations in PV systems.
Co-Authors Alkindi, Muhammad Faizal Ananda, Briska Putra Aprilianto, Rizky Ajie Feddy Setio Pribadi