Intelligent algorithm-based maximum power point tracker for an off-grid photovoltaic-powered direct-current irrigation system

Abstract

Abstract This research aims to enhance the performance of photovoltaic (PV) systems on a 2-fold basis. Firstly, it introduces an advanced deep artificial neural network algorithm for accurate and fast maximum power point tracking, ensuring optimal extraction of electrical power from PV arrays. Secondly, it proposes the use of 96-V, 2.98-kW direct-current (DC) water pumps for farm irrigation, aiming to improve efficiency, reduce cost and complexity, and overcome challenges associated with connecting faraway farm irrigation systems to the utility grid. In this study, it has been demonstrated that the use of DC pumps greatly improves system performance and efficiency by eliminating the need for isolation transformers, power passive filters and inverters, therefore simplifying the architecture of the system. The efficacy of the proposed methodology is confirmed by MATLAB®/Simulink® simulation results, whereby the proposed algorithm attains a mean squared error of 6.5705 × 10–5 and a system efficiency approaching 99.8%, ensuring a steady voltage under varying load conditions.