RIZAL, Dimas Saifur and Facta, Mochammad and Prastawa, Andhika (2026) ANALISIS KINERJA METODE PARTICLE SWARM OPTIMIZATION, PERTURB AND OBSERVE, DAN INCREMENTAL CONDUCTANCE UNTUK OPTIMASI DAYA PADA SISTEM FOTOVOLTAIK 202 kWp DI BAWAH KONDISI BAYANGAN PARTIAL. Masters thesis, UNIVERSITAS DIPONEGORO.

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Abstract

Efisiensi konversi energi pada panel surya dipengaruhi oleh intensitas iradiasi matahari dan suhu lingkungan sekitar. Untuk mengoptimalkan daya keluaran dari modul fotovoltaik, diterapkan teknik Maximum Power Point Tracking (MPPT). Beberapa algoritma MPPT yang banyak digunakan antara lain Perturb and Observe (P&O) serta Incremental Conductance (InC). Dalam penelitian ini, digunakan pendekatan algoritma Particle Swarm Optimization (PSO) guna meningkatkan efisiensi proses MPPT, khususnya dalam menghadapi dan mengatasi kompleksitas yang ditimbulkan oleh kondisi bayangan partial (PSC). Analisis dilakukan terhadap daya tracking dan efisiensi yang dihasilkan untuk setiap algoritma pada pengujian variasi nilai iradiasi dan suhu lingkungan. Hasil simulasi pada variasi iradiasi yang diujicobakan dalam 202 kWp, menunjukkan bahwa algoritma PSO secara konsisten berhasil melacak titik daya maksimum global (Global Maximum Power Point) dengan efisiensi konversi mencapai 100%. Angka ini secara signifikan melampaui efisiensi yang dicapai oleh P&O (94,30%) dan InC (94,37%) pada kondisi yang sama. Pengujian terhadap suhu lingkungan algoritma P&O dan PSO menunjukan hasil yang stabil di semua kondisi dengan efisiensi mencapai 100%. Algoritma InC cenderung kurang cocok pada suhu rendah sehingga efisiensinya menurun 99,4 – 99,7%.
Kata Kunci : Maximum Power Point Tracking (MPPT), Perturb and Observe (P&O), Incremental Conductance (InC), Particle Swarm Optimization (PSO), Efisiensi Panel Surya, Bayangan Partial (PSC)

The energy conversion efficiency of solar panels is influenced by solar irradiance intensity and ambient temperature. To optimize the output power of photovoltaic modules, the Maximum Power Point Tracking (MPPT) technique is applied. Commonly used MPPT algorithms include Perturb and Observe (P&O) and Incremental Conductance (InC). In this study, the Particle Swarm Optimization (PSO) algorithm is implemented to enhance the MPPT process efficiency, particularly in addressing the complexities caused by Partial Shading Conditions (PSC). The analysis focuses on tracking performance and efficiency for each algorithm under varying irradiance and temperature conditions. Simulation results under irradiance variation show that the PSO algorithm consistently tracks the Global Maximum Power Point (GMPP) with a conversion efficiency of 100%, significantly outperforming P&O (94.30%) and InC (94.37%) under the same conditions. Temperature variation tests indicate that P&O and PSO algorithms maintain stable performance across all temperature levels with 100% efficiency, while InC performs less effectively at lower temperatures, resulting in a reduced efficiency of 99.4–99.7%.
Keywords: Maximum Power Point Tracking (MPPT), Perturb and Observe (P&O), Incremental Conductance (InC), Particle Swarm Optimization (PSO), Solar Panel Efficiency, Partial Shading Condition (PSC)

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