Development of Cuckoo Search MPPT Algorithm for Partially Shaded Solar PV SEPIC Converter

Abstract

Photovoltaic (PV) power generation is playing a prominent role in rural power generation systems due to its low operating and maintenance cost. The output properties of solar PV mainly depend on solar irradiation, temperature, and load impedance. Hence, the operating point of solar PV oscillates. Due to the oscillatory behavior of operating point, it is difficult to transform maximum power from the source to load. To maintain the operating point constant at the maximum power point (MPP) without oscillations, a maximum power point tracking (MPPT) technique is used. Under partial shading condition, the nonlinear characteristics of PV comprise of multiple maximum power points (MPPs). As a result, discovering true MPP is difficult. The traditional and neural network MPPT methods are not suitable to track the MPP because of oscillations around MPP and impreciseness in tracking under partial shading (PS) condition. Therefore, in this article, a biological intelligence cuckoo search optimization (CSO) technique is utilized to track and extract the maximum power of the solar PV at two PS patterns. MATLAB/Simulink is used to demonstrate the CSO MPPT operation on SEPIC converter.