Maximum Power Point Tracking in Power System Control Using Reservoir Computing

Abstract

This article deals with an innovative approach to maximum power point tracking (MPPT) in power systems using the reservoir computing (RC) technique. Even though extensive studies have been conducted on MPPT to improve solar PV systems efficiency, there is still considerable room for improvement. The methodology consisted in modeling and programming with MATLAB software, the reservoir computing paradigm, which is a form of recurrent neural network. The performances of the RC algorithm were compared to two well-known methods of maximum power point tracking: perturbed and observed (P&O) and artificial neural networks (ANN). Power, voltage, current, and temperature characteristics were assessed, plotted, and compared. It was established that the RC-MPPT provided better performances than P&O-MPPT and ANN-MPPT from the perspective of training and testing MSE, rapid convergence, and accuracy of tracking. These findings suggest the need for rapid implementation of the proposed RC-MPPT algorithm on microcontroller chips for the widespread use and adoption globally.