Assessing Maximum Power Point Tracking Intelligent Techniques on a PV System with a Buck–Boost Converter

Maria I. S. Guerra, Fábio M. Ugulino de Araújo, Mahmoud Dhimish and Romênia G. Vieira
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Maria I. S. Guerra: Department of Engineering and Technology, Semi-Arid Federal University, Mossoró 59625-900, Brazil
Fábio M. Ugulino de Araújo: Department of Computer and Automation Engineering, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil
Mahmoud Dhimish: Department of Electronic Engineering, University of York, York YO10 5DD, UK
Romênia G. Vieira: Department of Engineering and Technology, Semi-Arid Federal University, Mossoró 59625-900, Brazil

Energies, 2021, vol. 14, issue 22, 1-21

Abstract: Classic and intelligent techniques aim to locate and track the maximum power point of photovoltaic (PV) systems, such as perturb and observe (P&O), fuzzy logic (FL), artificial neural networks (ANNs), and adaptive neuro-fuzzy inference systems (ANFISs). This paper proposes and compares three intelligent algorithms for maximum power point tracking (MPPT) control, specifically fuzzy, ANN, and ANFIS. The modeling of a single-diode equivalent circuit-based 3 kWp PV plant was developed and validated to achieve this purpose. Then, the MPPT techniques were designed and applied to control the buck–boost converter’s switching device of the PV plant. All three methods use the ambient conditions as input variables: solar irradiance and ambient temperature. The proposed methodology comprises the study of the dynamic response for tracking the maximum power point and the power generated of the PV systems, and it was compared to the classic P&O technique under varying ambient conditions. We observed that the intelligent techniques outperformed the classic P&O method in tracking speed, tracking accuracy, and reducing oscillation around the maximum power point (MPP). The ANN technique was the better control algorithm in energy gain, managing to recover up to 9.9% power.

Keywords: photovoltaic systems; MPPT; ANN; fuzzy; ANFIS; power recovery (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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