Roach Infestation Optimization MPPT Algorithm for Solar Photovoltaic System

Abstract

Solar photovoltaic (PV) power has several advantages such as free availability, absence of rotating parts, can be easily integrated with building architecture, and need little maintenance. However, the PV cell current–voltage (I–V) characteristics are non-linear and power generated from a PV array depends on solar insolation/irradiation and panel temperature. The extracted PV output power is influenced by the accuracy with which the nonlinear power–voltage (P–V) characteristic curve is traced by the maximum power point tracking (MPPT) controller. In this paper, a bio-inspired roach infestation optimization (RIO) algorithm is proposed to extract the maximum power from the PV system (PVS). To validate the usefulness of the RIO MPPT algorithm, MATLAB/Simulink simulations are performed under varying environmental conditions, for example, step changes in solar irradiance, partial shading, and the presence of system uncertainties and load variation conditions of the PV array. Furthermore, the search performance of the RIO algorithm is examined on different unconstrained benchmark functions, and it is realized that the RIO algorithm has improved search performance in terms of finding the optimal solution and faster convergence characteristics than Particle swarm optimization (PSO). The results demonstrated that the RIO-based MPPT performs remarkably in tracking with high accuracy as the PSO, perturb and observe (P&O), and incremental conductance (IC)-based MPPT schemes.