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.
