Title: Adaptive neuro-fuzzy inference system based maximum power point tracking for stand-alone photovoltaic system

Authors: Karima Amara; Ali Malek; Toufik Bakir; Arezki Fekik; Ahmad Taher Azar; Khaled Mohamad Almustafa; El-Bay Bourennane; Dallila Hocine

Addresses: Laboratory of Advanced Technologies of Electrical Engineering (LATAGE), Faculty of Electrical and Computer Engineering, Mouloud Mammeri University (UMMTO), BP 17 RP, 15000 Tizi-Ouzou, Algeria ' Centre de Développement des Energies Renouvelables, BP. 62 Route de l'Observatoire, Bouzareah 16340 Alger, Algeria ' ImViA Laboratory, University of Bourgogne, B.P. 47870, 21078 Dijon Cedex, France ' University Akli Mohand Oulhadj-Bouira, 10000, Algeria ' Robotics and Internet-of-Things Lab (RIOTU), Prince Sultan University, Riyadh, 11586, Saudi Arabia; Faculty of Computers and Artificial Intelligence, Benha University, Egypt ' College of Computer Science and Information Systems (CCIS), Riyadh, 11586, Saudi Arabia ' ImViA Laboratory, University of Bourgogne, B.P. 47870, 21078 Dijon Cedex, France ' Laboratory of Advanced Technologies of Electrical Engineering (LATAGE), Faculty of Electrical and Computer Engineering, Mouloud Mammeri University (UMMTO), BP 17 RP, 15000 Tizi-Ouzou, Algeria

Abstract: The maximum power point tracking (MPPT) plays a very important role to extract the maximum power of the photovoltaic (PV) system by ensuring its optimal production under sunshine and temperature variations. This study presents an algorithm based MPPT named an adaptive neuro-fuzzy inference system (ANFIS) which is built with the combination of the artificial neural network (ANN) and the fuzzy logic controller (FLC). The efficiency of the ANFIS algorithm is tested under Matlab/Simulink and compared with the fixed step conventional perturb and observe (P&O) and the gradient descent techniques under temperature and irradiance change. The obtained results showed a significant improvement in performances of the PV system using the ANFIS-MPPT technique which provides also faster convergence, stability in steady state, less oscillations around the MPP and higher efficiency to track the maximum power from the PV system compared to other techniques under different operating conditions.

Keywords: photovoltaic system; P&O; perturb and observe; MPPT; maximum power point tracking; gradient descent; ANFIS; adaptive neural-fuzzy inference system; FLC; fuzzy logic control; DC-DC and DC-AC converters.

DOI: 10.1504/IJMIC.2019.107480

International Journal of Modelling, Identification and Control, 2019 Vol.33 No.4, pp.311 - 321

Received: 06 Sep 2019
Accepted: 17 Oct 2019

Published online: 29 May 2020 *

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