Perancangan Sistem Charging Baterai pada Solar Tracker Menggunakan Fuzzy Genetic Algorithm Control

Perancangan sistem charging baterai yang terintegrasi dengan maximum power point tracking (MPPT) menggunakan fuzzy genetic algorithm control telah dilakukan pada penelitian ini. Dua metode kontrol dibandingkan pada penelitian ini yaitu metode kontrol fuzzy dan fuzzy genetic algorithm control. Metode kontrol ini digunakan untuk memandu proses tracking titik daya maksimum agar diperoleh performansi daya yang maksimum sehingga proses charging baterai dapat bekerja secara optimal. Hasil simulasi dan pengujian menunjukkan bahwa sistem yang dikembangkan ini berhasil melakukan tracking titik daya maksimum dari fotovoltaik yang memiliki karakteristik temperatur dan radiasi yang berubah cepat, dengan performansi nilai terhadap daya aktual sebesar 96,40% pada standard test condition (STC), 89,45% pada kondisi variasi temperatur, dan 95,01% pada kondisi variasi radiasi. Pada kondisi variasi temperatur dan variasi radiasi, fuzzy genetic algorithm control meningkatkan daya ekstrak sebesar 5,5% dibandingkan dengan kontrol fuzzy. Fuzzy genetic algorithm control mempunyai kecepatan 16,74%/jam sehingga hanya membutuhkan waktu 4,181 jam agar baterai 70 Ah bisa penuh dari state of charge baterai 30%, lebih cepat 0,597 jam atau 36 menit dibandingkan dengan kontrol fuzzy. ================================================================================================ The design of a battery charging system that is integrated with maximum power point tracking (MPPT) using a fuzzy genetic algorithm control was carried out in this study. Two control methods were compared in this study, namely fuzzy and fuzzy genetic algorithm control. These control methods are used to guide the tracking process of the maximum power point to obtain maximum power performance so that the battery charging process can work optimally. The simulation and testing result show that system developed successfully tracks the maximum power point (MPP) of photovoltaic which has a characteristic temperature and irradiance changes rapidly, with a performance value of actual power of 96.40% in Standart Test Condition, 89.45% in conditions of temperature variation, and 95.01% in conditions irradiance variation. While in conditions of temperature variation and irradiance variation, fuzzy genetic algorithm control increases extract power by 5.5% than fuzzy control. Fuzzy genetic algorithm control has charging speed of 16.74% / hour so it only takes 4.181 hours so that the 70 Ah battery can be full from state of charge 30% . This is 0.597 hours or 36 minutes faster than fuzzy control