Title: Parameters estimation of photovoltaic modules: comparison of ANN and ANFIS

Authors: Fawzan Salem; Mohamed A. Awadallah

Addresses: Power Electronics and Energy Conversion Department, Electronics Research Institute, Cairo 12622, Egypt ' Department of Electrical Power and Machines, University of Zagazig, Zagazig 44111, Egypt

Abstract: The paper presents an artificial intelligence (AI) technique for the estimation of the equivalent circuit parameters of photovoltaic (PV) modules. The parameters considered in the study are the series resistance, shunt resistance, and diode ideality factor. Adaptive neuro-fuzzy inference systems (ANFIS) and artificial neural networks (ANN) are independently employed for the estimation process. Training data for both paradigms are extracted from analytical solution of the PV mathematical model. Comparison of the testing results signifies that ANN outperforms ANFIS in estimating the required parameters.

Keywords: photovoltaics; PV modules; parameter estimation; adaptive neuro-fuzzy inference systems; ANFIS; artificial neural networks; ANNs; artificial intelligence; series resistance; shunt resistance; diode ideality factor; mathematical modelling.

DOI: 10.1504/IJIED.2014.059230

International Journal of Industrial Electronics and Drives, 2014 Vol.1 No.2, pp.121 - 129

Received: 15 Oct 2013
Accepted: 07 Nov 2013

Published online: 07 Feb 2014 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article