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.
Highlights
Recent advances in technology, industry growth, increasing populations with their proportional energy needs highlight the problem of energy shortage.Leggett (2021) theorized that failure to meet the global energy quest and the consistent depletion of non-renewable energy resources resulted in renewable energy as a sustainable alternative for energy generation in the future
In order to assess the relationships that exist between the inputs and the outputs using the reservoir computing (RC)-maximum power point tracking (MPPT), 2D, several 3D curves were plotted as illustrated from Figures 11–14
The MPPT algorithm allows the creation of random networks that simulate and train the readout layer utilizing the echo state network (ESN)
Summary
Industry growth, increasing populations with their proportional energy needs highlight the problem of energy shortage.Leggett (2021) theorized that failure to meet the global energy quest and the consistent depletion of non-renewable energy resources resulted in renewable energy as a sustainable alternative for energy generation in the future. Solar photovoltaic (PV) technology has been identified as one of the most auspicious energy sources based on the Sun (Dajuma et al, 2016) as a natural resource. Several previous studies posited that solar PV can resourcefully substitute orthodox energy sources in addition to improving environmental conservation (Acakpovi and Asabere, 2017). According to the International Energy Agency (IEA) forecast, by 2050, PV technology is likely to become a very significant source of energy that will offer about 11–13% of global electricity and would be able to minimize about 2.3 gigatons of carbon dioxide (CO2) emissions per year. PV has attracted more attention due to the advantages of the sun as a source of energy characterized by inexhaustibility, omnipresence, absence of rotating parts, accessibility everywhere, and minimum required maintenance (Boukenoui et al, 2016).
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