PV Optimization With Genetic Algorithm-Based MPPT Method - Hadi, Ayas Sutomo Hadi; Alawiy, Taqijjudin; Wirateruna, Efendi S (Universitas Islam Malang, 2025)
This research designs and implements a Maximum Power Point Tracking (MPPT) system based on genetic algorithm (GA) on buck-boost converter using Arduino microcontroller to increase energy conversion efficiency on PV system. The GA algorithm is used to adjust the PWM duty cycle to achieve the maximum power point (MPP) optimally. Tests were conducted to analyze the performance of the GA compared to the Perturb and Observe (P&O) algorithm and the system without MPPT. The results show that the GA is able to achieve a maximum power of 26.16 W, higher than the P&O algorithm (23.77 W) and the system without MPPT (1.59 W). The GA also reaches MPP faster, maintains output stability, and reduces power fluctuations. Voltage and current sensor testing showed high accuracy with Mean Absolute Percentage Error (MAPE) of 0.291% and 0.206%, respectively. The system was shown to improve energy conversion efficiency under various lighting and load conditions dynamically. With these results, the genetic algorithm proved to be more effective in optimizing the output power of solar panels than conventional methods.