Parameters extraction of single diode model for degraded photovoltaic modules. (February 2021)

• Record Type: Journal Article
• Authors: Piliougine, M.; Guejia-Burbano, R.A.; Petrone, G.; Sánchez-Pacheco, F.J.; Mora-López, L.; Sidrach-de-Cardona, M.
• Is Part Of: Renewable energy. Volume 164(2021)
• Page Start: 674
• Page End: 686
• Publication Date: 2021-02
• Subjects: Photovoltaic diagnosis -- Single diode model parameters identification -- Photovoltaic module simulation; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2020.09.035
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: The single–diode model is widely used for the analysis of photovoltaic systems and reproducing accurately the I–V curve. Numerical or analytical methods can be employed to estimate the model parameters; among them explicit methods are well assessed providing precise results and low computational complexity, thus suitable to be developed on embedded systems. Due to their approximated nature, the accuracy of such methods may be affected by the operating conditions and by the state of health of the photovoltaic modules that have been characterised. The main contribution of this paper is to analyse a selection of explicit methods with the aim of testing their capability to detect degradation in photovoltaic modules. Since different degradation phenomena are reflected in a variation of the series resistance of the single diode equivalent circuit, the study is mainly focused on the estimation of this parameter. The comparison of different explicit methods has been done by using outdoor experimental I–V curves of a photovoltaic module operating in normal as well as degraded conditions. The analysis shows that only few methods exhibit enough reliability to estimate correctly the model parameters in presence of degradation and are less sensible to the environmental operating conditions. Highlights: Methods for parametric identification of PV models. Robustness of parameter identification methods in presence of PV degradation. Explicit methods for reducing computational burden and memory. Suitable for the online diagnosis of PV arrays.