Evaluation and Validation of Equivalent Circuit Photovoltaic Solar Cell Performance Models

Abstract

The "five-parameter model" is a performance model for photovoltaic solar cells that predicts the voltage and eurrent output by representing the eells as an equivalent electrical circuit with radiation and temperature-dependent components. An important feature of the five-parameter model is that its parameters can be determined using data commonly provided by module manufaeturers on their published datasheets. This paper doeuments the predictive capability of the five-parameter model and proposes modifieations to improve its performance using approximately 30 days of field-measured meteorological and module data from a wide range of eell technologies, including monocrystalline, polycrystalline, amorphous silicon, and copper indium diselenide (CIS). The standard five-parameter model is eapable of predicting the performance of monocrystalline and polycrystalline silicon modules within approximately 6% RMS but is slightly less accurate for a thin-film CIS and an amorphous silicon array. Frrors for the amorphous technology are reduced to approximately 5% RMS by using input data obtained after the module underwent an initial degradation in output due to aging. The robustness and possible improvements to the five-parameter model were also evaluated. A sensitivity analysis of the five-parameter model shows that all model inputs that are difficult to determine and not provided by manufacturer datasheets such as the glazing material properties, the semiconductor band gap energy, and the ground reflectance may be represented by approximate values independent of the PV teehnology. Modifications to the five-parameter model tested during this research did not appreciably improve the overall model performance. Additional dependence introdueed by a seven-parameter model had a less than 1% RMS effect on maximum power predictions for the amorphous technology and increased the modeling errors for this array 4% RMS at open-circuit conditions. Adding a current sink to the equivalent eireuit to better model recombination currents had little effect on the model behavior.

Monitoring and Diagnostics of Photovoltaic Power Plants

“…First, the I-V characteristics of the glass-glass PV module have been measured, then the five parameters have been calculated starting from STC values using relations proposed in [22] and using the three different methods here proposed; in all cases, the I-V characteristics have been estimated. Figure 45.6a and b shows the I-V curves measured, the I-V curves estimated using the parameters calculated from STC values, the I-V curves estimated using the parameters calculated through the AB-DE algorithm, the I-V curves estimated using the parameters calculated through the analytical algorithm, and the I-V curves estimated using the parameters calculated through the analytical algorithm starting from the partial I-V characteristic.…”

“…Anyhow, the reference value of each parameter can be calculated starting from its value in the operating conditions and vice versa [22]. Once the parameters have been obtained, the I-V characteristic can be estimated by model (45.1):…”

“…• Calculation of the five parameters starting from data provided by manufacturers, which are referred to as STC • Measurement of solar radiation and cell temperature (in the real working conditions) • Calculation of the five parameters in the real working conditions through relations proposed in [22] • Estimation of the I-V characteristic in real working conditions using the calculated parameters • Measurement of the partial I-V curve • Calculation of the error ( mse) between the measured and the estimated I-V curve:…”

Use of monthly average solar radiation data for assessing the efficiency of a photovoltaic array

“…: T a " 20˝C; incident solar radiation of G T " 800 W/m 2 ; wind speed of 1 m/s, in absence of any external loads (η c " 0) [2]. All three models selected for this study have been extensively quoted in the literature but, to the authors' best knowledge, no one was validated as much as the model of King et al Boyd [11] reports that, depending on the conditions of the experiments, the model of King et al reproduces experimental data within 2.4% to 5.4%.…”

“…The reason for the choice is twofold: King et al's model was validated against experimental data (2.4% to 5.4%, according to [11]) and it proved to reproduce Duffie et al's prediction within the accuracy of the method. Hence King's model was preferred to be used in the second part of the study; i.e.…”