Abstract:
Photovoltaic (PV) has seen rapid growth over the last decade because of its declining cost, minimal pollution, and easier maintenance. However, one major drawback of the PV systems is the non-linear characteristics of the output power caused by partial shading conditions (PSC). Varying weather phenomena, like temperature and solar irradiance, confront the PV with a multi-peak maximum power point tracking (MPPT) problem. To resolve this, many conventional and metaheuristic optimization MPPT algorithms have been proposed, some of which come with problems like slower tracking speed, increased oscillation, and no guarantee of accurate convergence at the global maximum power point (GMPP). This paper presents a hybrid quantum particle swarm optimization assisted variable incremental conductance (QPSOVIC) algorithm which efficiently tracks maximum power (Pmax) under varying weather conditions. The effectiveness of this method is validated through a comparative analysis among already established MPPT techniques like cuckoo search (CS), particle swarm optimization (PSO), and QPSO. This metaheuristic algorithm compensates for the conventional tracking algorithm’s inability to track the GMPP and bypasses premature convergence of the traditional PSO algorithm. MATLAB/Simulink has been used for modeling and demonstration of the proposed algorithm.
Description:
Supervised by
Dr. Fahim Abid,
Assistant Professor,
Department of Electrical and Electronic Engineering (EEE),
Islamic University of Technology (IUT),
Board Bazar, Gazipur-1704, Bangladesh.
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2022.
