Rancang Bangun Stand Alone Pv Charging Sistem Pada Penerangan Jalan Umum Berbasis Solar Tracker Menggunakan Algoritma Ant Colony Optimization -Fuzzy Logic Controler

Pada penelitian ini dilakukan perancangan charging system dengan kontrol Fuzzy Logic yang di optimisasi dengan ant colony optimization pada Penerangan Jalan Umum Tenaga Surya berbasis solar tracker. Konverter yang digunakan adalah buck converter dengan tegangan input sebesar 17.2 V – 20 V dan output 14.2. Kontrol yang digunakan berupa Fuzzy Logic dan Ant Colony Optimization Fuzzy Logic Control (ACO-FLC), kontrol mengunakan Fuzzy Logic mempunyai nilai maximum overshoot sebesar 53.65%, settling time 0.01 detik dan error steady state 2.81%, Kontrol menggunakan ACO-FLC mempunyai nilai maximum overshoot sebesar 49.85%, settling time 0.004 detik dan error steady state 1.9%. Hasil pengambilan data menunjukan kontrol ACO-FLC mempunyai performansi yang lebih baik dengan effisiensi konverter 38.45%, efisiensi sistem 4.56% dan SOC sebesar 79% sedangkan untuk effisiensi konverter, efisiensi sistem dan SOC baterai pada kontrol Fuzzy sebesar 31.42%, 3.48% dan 68%. ======================================================================================================= In this study, a charging system design with Fuzzy Logic control was carried out which was optimized with ant colony optimization on Solar Tracker-based Public Street Lighting. The converter used is a buck converter with an input voltage of 17.2 V – 20 V and an output of 14.2. The controls used are Fuzzy Logic and Ant Colony Optimization Fuzzy Logic Control (ACO-FLC), control using Fuzzy Logic has a maximum overshoot value 53.65%, settling time 0.01 seconds and steady-state error 2.81%, Control using ACO-FLC has a maximum value overshoot 49.85%, settling time 0.004 seconds and steady-state error 1.9%. The results of data collection show that the ACO-FLC control has better performance with efficieny converter, efficiency system and SOC of 38.45%, 4.56% and 79% while the efficiency converter, eficiency system and SOC of the battery on the Fuzzy control is 31.42%, 3.48% and 68%