Title: Intelligent perturb and observe control based on support vector machine for photovoltaic pumping system

Authors: Omar Dahhani; Ismail Boumhidi

Addresses: Electronics, Signals-Systems and Informatics Laboratory LESSI, Faculty of Sciences Dhar El-Mehraz, Sidi Mohammed Ben Abdellah University, Fes 30050, Morocco ' Electronics, Signals-Systems and Informatics Laboratory LESSI, Faculty of Sciences Dhar El-Mehraz, Sidi Mohammed Ben Abdellah University, Fes 30050, Morocco

Abstract: In this paper, an intelligent maximum power point tracking control is proposed for a photovoltaic (PV) water pumping system. This strategy combines the least squares support vector machines (LS-SVM) technique with the exponential adaptive perturb and observe (EAP&O) control. The reason for combining these two techniques is to overcome the steady states oscillations, low convergence rate as well as failure problems in standard P&O. The main purpose of the LS-SVM in this work, is to design an accurate off-line MPP model, which gives back the optimal value of duty cycle at present illumination intensity. These former values serve to initialise the proposed EAP&O in online implementation. To validate and to show the effectiveness of the proposed control, both strategies, EAP&O based on LS-SVM and standard P&O, are applied to the PV pumping system, and finally some important simulation results are presented.

Keywords: adaptive perturb and observe; maximum power point tracking; MPPT; support vector machine; SVM; photovoltaic power system control.

DOI: 10.1504/IJMIC.2019.101966

International Journal of Modelling, Identification and Control, 2019 Vol.32 No.1, pp.60 - 69

Accepted: 08 Nov 2018
Published online: 02 Sep 2019 *

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