Hardware implementation and design of artificial neural network based dual axis solar tracker

The uncertainties and intermittencies characteristic have been a critical challenge to optimize the solar energy source. The solar energy utilization is not optimal because the actual position between sun and the solar panels is continuously changing. In order to maximize the harvesting power from sun irradiation, it is important to ensure the perpendicular position between sun and the solar panel. The solar panel should automatically follow the sun position in an effective and efficient way. Since the sun moves along the latitude and longitude paths, dual axis solar tracker is considered to track the sun movements. Many control algorithms have been proposed to optimize the dual axis solar tracking system. However, there are some limitations of conventional solar tracker controller in terms of slow response and non-efficient movement of solar panel. In this research, an intelligent control algorithm of dual axis solar tracker based on an artificial neural network (ANN) method is proposed. The ANN functions to predict the angle or direction of the sun according to collectable data of the movement patterns of the sun. Two sensor light-dependent resistor were used as detector as well as input from ANN neurons. Then the output of the program will drive a linear actuator motor so that the solar panel can follow the direction of the sun's movement. It was monitored that the proposed control algorithm is better than the conventional PID based dual axis solar tracker. Higher solar energy has been harvested using the proposed controller which indicated more efficient control system to track the sun movement.