Performance Analysis Of Ant Colony Optimization

Abstract

From the past two decades the swift advancement in the field of power electronics, especially in photovoltaic (PV) power generation has led to the ever-increasing demand of highly efficient and robust power supplies. Thus Research & Development in this field has led to the use Switching Mode Power Supplies (SMPs). Thus, they provide minimal power losses which reduces the heat stresses on the system and enhances the power produced. Now the performance of power converter mainly depends on the control algorithm and the type of converter used. Each converter behaves differently under different shading conditions through the day. During the partial shading condition, we require such converter equipped with a fast-converging control algorithm that it senses the partial shading condition and tracks the maximum power point. Therefore, this research thesis represents an advance MPPT technique of Ant Colony Algorithm to optimize the maximum power tracked in minimum time. For this purpose, an ACO algorithm with optimal colony size was developed and tested on two advanced DC-DC converters for performance analysis which are Boost and SEPIC. Both converters were tested on three different conditions of solar irradiation to simulate a day response of converter. As each PV module daily receives different solar irradiation due to weather and shading conditions. Therefore, each converter with ACO was tested for Uniform Shading Condition, Partial Shading Condition and Extreme Partial Shading Conditions. Additionally, this thesis also represents a comparative performance analysis of Particle Swarm Optimization Technique and Perturb and Observe Technique on the same converters under same shading conditions. In the end a comprehensive conclusion based upon the results of simulation is made.