Hybrid Neural Network Approach Based Tool for the Modelling of Photovoltaic Panels. (22nd February 2015)

• Record Type: Journal Article
• Authors: Laudani, Antonino; Lozito, Gabriele Maria; Riganti Fulginei, Francesco; Salvini, Alessandro
• Other Names: Ho Cheuk-Lam Academic Editor.
• Is Part Of: International journal of photoenergy. Volume 2015(2015)
• Publication Date: 2015-02-22
• Subjects: Photochemistry -- Periodicals; Photobiology -- Periodicals; Chemistry, Physical and theoretical -- Periodicals; Photochimie; Photobiologie; Chimie physique et théorique; Chemistry, Physical and theoretical; Photobiology; Photochemistry; Electronic journals; Periodicals; 541.35
• DOI: 10.1155/2015/413654
• Languages: English
• ISSNs: 1110-662X

Abstract

Abstract : A hybrid neural network approach based tool for identifying the photovoltaic one-diode model is presented. The generalization capabilities of neural networks are used together with the robustness of the reduced form of one-diode model. Indeed, from the studies performed by the authors and the works present in the literature, it was found that a direct computation of the five parameters via multiple inputs and multiple outputs neural network is a very difficult task. The reduced form consists in a series of explicit formulae for the support to the neural network that, in our case, is aimed at predicting just two parameters among the five ones identifying the model: the other three parameters are computed by reduced form. The present hybrid approach is efficient from the computational cost point of view and accurate in the estimation of the five parameters. It constitutes a complete and extremely easy tool suitable to be implemented in a microcontroller based architecture. Validations are made on about 10000 PV panels belonging to the California Energy Commission database.