Systematic Literature Review and Benchmarking for Photovoltaic MPPT Techniques

Hsen Abidi (), Lilia Sidhom () and Ines Chihi ()
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Hsen Abidi: Laboratory of Energy Applications and Renewable Energy Efficiency (LAPER), Faculty of Sciences of Tunis, El Manar University, Tunis 1068, Tunisia
Lilia Sidhom: Laboratory of Energy Applications and Renewable Energy Efficiency (LAPER), Faculty of Sciences of Tunis, El Manar University, Tunis 1068, Tunisia
Ines Chihi: Department of Engineering, Faculty of Science, Technology and Medicine, University of Luxembourg, Campus Kirchberg, 1359 Luxembourg, Luxembourg

Energies, 2023, vol. 16, issue 8, 1-45

Abstract: There are a variety of maximum power point tracking (MPPT) algorithms for improving the energy efficiency of solar photovoltaic (PV) systems. The mode of implementation (digital or analog), design simplicity, sensor requirements, convergence speed, range of efficacy, and hardware costs are the primary distinctions between these algorithms. Selecting an appropriate algorithm is critical for users, as it influences the electrical efficiency of PV systems and lowers costs by reducing the number of solar panels required to achieve the desired output. This research is relevant since PV systems are an alternative and sustainable solution for energy production. The main aim of this paper is to review the current advances in MPPT algorithms. This paper first undertakes a systematic literature review (SLR) of various MPPT algorithms, highlighting their strengths and weaknesses; a detailed summary of the related reviews on this topic is then presented. Next, quantitative and qualitative comparisons of the most popular and efficient MPPT methods are performed. This comparison is based on simulation results to provide efficient benchmarking of MPPT algorithms. This benchmarking validates that intelligent MPPTs, such as artificial neural network (ANN), fuzzy logic control (FLC), and adaptive neuro-fuzzy inference system (ANFIS), outperform other approaches in tracking the MPPT of PV systems. Specifically, the ANN technique had the highest efficiency of 98.6%, while the ANFIS and FLC methods were close behind with efficiencies of 98.34% and 98.29%, respectively. Therefore, it is recommended that these intelligent MPPT techniques be considered for use in future photovoltaic systems to achieve optimal power output and maximize energy production.

Keywords: photovoltaic system; MPPT techniques; systematic literature review; comparative study; simulation results; benchmarking (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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