An intelligent MPPT approach based on neural-network voltage estimator and fuzzy controller, applied to a stand-alone PV system

Author

Bendib, B. ; Krim, Fateh ; Belmili, H. ; Almi, M.F. ; Bolouma, S.

Author_Institution

EPST-CDER, Unite de Dev. des equipements solaires, Bou-Ismail, Algeria

fYear

2014

fDate

1-4 June 2014

Firstpage

404

Lastpage

409

Abstract

This paper presents an intelligent maximum power point tracking (MPPT) method for a stand-alone photovoltaic (PV) system using artificial neural networks (ANN) modelling and a fuzzy logic controller (FLC). The ANN is trained for various conditions of solar irradiance and temperature to estimate the MPP voltage. This voltage is then used by the FLC as a reference voltage to generate the appropriate control signal for the DC-DC converter. The proposed technique is implemented in Matlab/Simulink and compared with the conventional method of incremental conductance (IncCond). Simulation results show a good performance of the ANN based fuzzy MPPT controller.

Keywords

fuzzy control; maximum power point trackers; neural nets; photovoltaic power systems; power engineering computing; ANN; DC-DC converter; FLC; Matlab-Simulink; appropriate control signal; artificial neural networks; fuzzy logic controller; intelligent MPPT approach; maximum power point tracking; reference voltage; solar irradiance; stand-alone photovoltaic system; voltage estimator; Artificial neural networks; Mathematical model; Maximum power point trackers; Neurons; Niobium; Radiation effects; Voltage control; DC-DC converter MPPT; IncCond; PV system; artificial neural network (ANN); fuzzy logic controller (FLC);

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Electronics (ISIE), 2014 IEEE 23rd International Symposium on

Conference_Location

Istanbul

Type

conf

DOI

10.1109/ISIE.2014.6864647

Filename

6864647