Maximum Power Point Tracking in Partial Shaded Photovoltaic System using Smell Agent Optimization Algorithm

Abstract

With partial shading conditions, it is essential to acquire Maximum Power Point at which the Photovoltaic systems (PV) operate effectively despite the variation in the cell temperature and incident angle of sunlight rays on the panels. This study explores the use of a Smell Agent Optimization (SAO) algorithm for Maximum Power Point Tracking (MPPT) in partial shaded PV systems. The proposed MPPT system is composed of a PV model, a DC-DC converter model and a control part. The Smell Agent Algorithm (SAA) was adopted in the control part of the MPPT system to implement the optimization algorithm using four different shading patterns (SPs) and to calculate the optimal switching duty cycle of the DC-DC converter. The effectiveness of the proposed system was verified using simulations in the MATLAB/Simulink environment. The SAO respectively track maximum values for Power, Voltage and Current as 845.8476 W, 211.7308 V, 3.99492 A while the maximum values for Power, Voltage and Current for Perturb and Observe (P and O) are 845.0465 W, 211.6305 V, 3.993028 A respectively during SP1. The results showed that the SAO algorithm has excellent tracking results in terms of convergence speed, accuracy, power extracted stability, and dynamic response in reaching the optimum point.