Performance Assessment of Artificial Neural Networks-Based MPPT Technique for Photovoltaic Systems

Abstract

Perturb and Observe (P&O) is one of the common techniques used for Maximum Power Point Tracking (MPPT) of PV panels due to its implementation simplicity. However, this conventional technique suffers from slow peak tracking speed under fast-changing environment conditions. With the revolution of machine learning, Artificial Neural Networks (ANNs) show fast, robust, and time-efficient solutions in different fields that require online data monitoring and fast decision-making. This paper presents a performance assessment of a proposed Artificial Neural Network (ANN) technique for maximum power extraction of Photovoltaic (PV) systems. The proposed technique shows promising solutions for MPPT in fast-changing environments and overcomes the limitations of conventional MPPT techniques.