Improving the Performance and Saving of Energy via Imperialism Competitive Algorithm in a Novel Dual Axis Sun-tracking System

The basis of the photovoltaic systems is the conversion of solar energy into electrical energy. Therefore, the maximum efficiency of photovoltaic systems is achieved by placing the system at a vertical angle to the sun. To achieve the maximum power and efficiency of the photovoltaic cell, tracking the position of the sun with high precision is important. A new linear-rotational dual axis sun tracker is designed and built to be robust against environmental impacts and to minimize the energy consumption of motors. Meanwhile, controller parameters have been optimized with Imperialism Competitive Algorithm (ICA) in order to track faster and more energy saving. Optimizing control system parameters for two cases--one axis and two axes-- has optimzed power production so much so that in addition to reducing power and time, the energy consumption of motors is also reduced. Meanwhile, in order to demonstrate the efficiency and performance of the system, a comparison was made between the tracking the speed and energy consumption of motors with Genetic Algorithms (GA), Artificial Bee Colony (ABC), Chaotic Particle Swarm Optimization algorithm (CPSO) and Imperialism Competitive Algorithm (ICA). The results indicate that the ICA has a better performance than other algorithms.