ANFIS Parallelization Control on Triple-Axis Sun Tracker to Minimize Solar Rays Incidence Angle on Photovoltaic

Abstract

This study proposes an Adaptive Neuro-Fuzzy Inference System (ANFIS) based on model with Parallelization Control Technique (PCT) implemented in a triple-axis sun tracker system. The system aims to maximize solar energy absorption by minimizing the angle of incidence of sunlight on Photovoltaic (PV) panels. It is designed with three manipulator arms, each controlled in parallel by a distinct ANFIS model. The primary goal is to adjust the PV panel orientation to optimal azimuth and altitude angles. The tetrahedron sensor is employed to detect the direction of sunlight. This study tests various types of consequent rules to find the optimal configuration for each ANFIS model, including first-order, second-order, and third-order polynomial functions. The analysis results show that the second-order consequent rule provides the highest accuracy across the three ANFIS models. The proposed system can cover azimuth range from 0° to 360° and altitude angle from 60° to 90°. The experiment also demonstrated that the system consistently moves toward a stable condition, even in the presence of minor disturbances. These findings indicate that ANFIS with PCT can minimize the angle of sunlight incidence, enhance system flexibility, and accelerate response time, making it a viable solution for increasing energy harvesting by PV panels.