One solution for online condition monitoring of photovoltaic (PV) modules is to identify single-diode model parameter values from measured current–voltage (I–V) curves. By this way, use of expensive thermal cameras and radiometric sensors utilized in traditional monitoring methods can be avoided. Unfortunately, most of the parameter identification methods require measurements of the operating conditions, i.e., irradiance and tem-perature. This article proposes a novel procedure for identification of the single-diode model parameter values along with the operating irradiance and temperature values from measured I–V curves without needing any other measurement. The only inputs of the proposed procedure are the I–V curve measurements at the actual operating conditions together with the parameter values of the module model in standard test conditions. The proposed procedure is experimentally validated using I–V curves of three PV module types measured from two different locations. Both the whole I–V curves or only a part of them, in a limited voltage range, are considered. Moreover, I–V curve measurements with an emulated increase of the series resistance are used to demonstrate the correctness of the identified series resistance values. It is shown that the procedure identifies the operating irradiance and temperature with high accuracy even during sharp irradiance transitions and low irradiance conditions and identifies series and shunt resistances very reliably under nearly constant high irradiance conditions. Moreover, for the first time, a comprehensive comparison of various fitting approaches based on root-mean-square error (RMSE) minimization, including two novel approaches, is presented. The results show that the different fitting approaches based on RMSE minimization affect the accuracy of the parameters identification in a different way, this meaning that the used fitting approach is a factor that should be considered when implementing model parameter identification by curve fitting.

Experimental comparison between various fitting approaches based on RMSE minimization for photovoltaic module parametric identification

Piliougine Rocha, Michel;Spagnuolo, Giovanni
2022

Abstract

One solution for online condition monitoring of photovoltaic (PV) modules is to identify single-diode model parameter values from measured current–voltage (I–V) curves. By this way, use of expensive thermal cameras and radiometric sensors utilized in traditional monitoring methods can be avoided. Unfortunately, most of the parameter identification methods require measurements of the operating conditions, i.e., irradiance and tem-perature. This article proposes a novel procedure for identification of the single-diode model parameter values along with the operating irradiance and temperature values from measured I–V curves without needing any other measurement. The only inputs of the proposed procedure are the I–V curve measurements at the actual operating conditions together with the parameter values of the module model in standard test conditions. The proposed procedure is experimentally validated using I–V curves of three PV module types measured from two different locations. Both the whole I–V curves or only a part of them, in a limited voltage range, are considered. Moreover, I–V curve measurements with an emulated increase of the series resistance are used to demonstrate the correctness of the identified series resistance values. It is shown that the procedure identifies the operating irradiance and temperature with high accuracy even during sharp irradiance transitions and low irradiance conditions and identifies series and shunt resistances very reliably under nearly constant high irradiance conditions. Moreover, for the first time, a comprehensive comparison of various fitting approaches based on root-mean-square error (RMSE) minimization, including two novel approaches, is presented. The results show that the different fitting approaches based on RMSE minimization affect the accuracy of the parameters identification in a different way, this meaning that the used fitting approach is a factor that should be considered when implementing model parameter identification by curve fitting.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/4781426
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