The electricity produced by photovoltaic sources is mainly influenced by the solar irradiance and temperature of the cells. The literature presents methods to estimate these parameters through the measurements of open-circuit voltage and short-circuit current of the array. However, in an actual photovoltaic system, changing the operating point of the module to short circuit or open circuit requires the array to be disconnected from the inverter, thus causing an impact on the electrical energy production. Such a problem could be avoided if the irradiance and the cell temperature were computed by means of the maximum power point coordinates, which is the focus of this article. To perform the calculation of the irradiance and the temperature of a photovoltaic module, a study of the relationship between the electric parameters and the operating conditions has been carried out. This concerns the modeling of the photovoltaic cell with respect to the identification and translation of parameters. In addition, IEC-60 891, which proposes equations for the voltage and current translation of photovoltaic generators, has been considered. Finally, application and performance evaluation of a new equation using the thermal coefficient of power, which relates the maximum power to the irradiance and cell temperature, is also presented. Experimental verification of the methods has been carried out, presenting consistent results.

Solar irradiance and cell temperature are the most significant aspects when assessing the production of a photovoltaic system. To avoid the need of specific sensors for quantifying such parameters, recent literature presents methods to estimate them through electrical measurements, using the photovoltaic module itself as a sensor. This work presents an application of such methods to data recorded using a research platform at University of Corsica, in France. The methods and the platform are briefly presented and the results are shown and discussed in terms of normalized mean absolute errors (nMAE) and root mean square errors (nRMSE) for various irradiance and cell temperature levels. The nMAE (and nRMSE) for solar irradiance are respectively between 3.5% and 3.9% (4.2% and 4.7%). Such errors on computed irradiance are in the same order of magnitude as those found in the literature, with a simple implementation. For cell temperatures estimation, the nMAE and nRMSE were found to be in the range 3.4%–8.2% and 4.3%–10.7%. These results show that using such methods could provide an estimation for the values of irradiance and cell temperature, even if the modules are not new and are not regularly cleaned, but of course not partially shaded.

Modeling photovoltaic modules using the single-diode model presents an adequate balance between complexity and accuracy; however, the current–voltage characteristic has to be calculated every time the operating condition changes. In turn, IEC-60891 provides procedures for direct translation from one operating condition to another, allowing a more straightforward application, provided that specific parameters are known. That international standard describes a procedure to quantify such parameters; however, their calculation requires complex equipment or extensive experimental tests. This article presents an alternative and simplified method to compute the parameters of IEC-60891, specifically the second procedure presented in the standard. It requires a reduced amount of measurements to be used as references. The validation of the method has been carried out taking extensive experimental data as reference, considering thousands of measurements taken for 16 months. The application of the proposed method to translate the reference curves presented mean absolute error and root-mean-square error below 3%, considering two different photovoltaic modules.

Bifacial photovoltaic modules are able to convert the solar radiation reaching their front and rear sides, which means that more electricity can be produced using the same array area as monofacial modules with similar ratings. In some locations, the cost per power unit for such a technology has already become cost-competitive with conventional monofacial modules. The so-called effective irradiance and the bifacial gain are useful metrics, respectively, to assess the solar resource and the performance of bifacial arrays. To calculate the effective irradiance, studies previously published employ rear-side irradiance measurements, whereas to compute the bifacial gain, other works make use of monofacial modules with rating similar to those of the bifacial modules under analysis. In this article, a straightforward method is presented, allowing to calculate the effective irradiance from the maximum power current, and to calculate the bifacial gain using a power scaling relation. The proposed method was experimentally tested using an outdoor platform with a dual-axis tracking system with bifacial modules. The effective irradiance was calculated using the novel method presented nRMSE of 2.88%, relative to the results obtained using the consolidated method. The bifacial gains obtained were 6.24% and 6.69%, respectively, using the proposed and traditional calculation methods. The procedure presented in this study might be useful for the quantification of the effective irradiance and the bifacial gain for PV installations, which do not have extensive monitoring hardware.

A common method of measuring the temperature of a photovoltaic module is by attaching a sensor to its back surface. However, since this method of measurement is punctual, the temperature gradient along the module surface is not considered. In addition to that, the temperature of the sensor is usually considered equal to the temperature of the photovoltaic cells, thus ignoring the temperature drop along the materials in between. This paper focuses on the problem of computing the average cell temperature of photovoltaic modules, based on information available on their respective datasheets and on measurements of the open-circuit voltage and solar irradiance. For that, different methods regarding identification and translation of the single-diode model parameters are used in conjunction, aiming to establish a simple and accurate procedure for computing the average temperature. The best performing combinations are then compared with IEC-60904-5, which presents a procedure for calculating the equivalent cell temperature of a module. Such computing procedures for the average module temperature have been experimentally tested, presenting coherent results.

Photovoltaic (PV) module manufacturers specify their products’ performance under the standard test condition. However, such a condition is hardly found outdoors, that is, during actual operation of photovoltaic systems. Despite that, many modeling methods proposed in the literature rely on standard test condition data for parametric identification. This paper presents an experimental study concerning different modeling approaches for photovoltaic modules, focusing on assessing model performance in different scenarios. The goal is to assess the influence of the data used for the model parametric adjustment when predicting the maximum power point of two photovoltaic modules, allowing the most advantageous methods to be identified. By using an experimental outdoor photovoltaic platform, over 30,000 I-V curves referring to two photovoltaic modules were measured for 16 months. Different models were applied for cases where their parameters were identified using field measurements. It was found that using measurements obtained under real operating conditions to adjust the model parameters provides significantly better prediction of the maximum power in comparison to cases employing data sheet-extracted information. For the all methods and cases studied, using in-field measurements to adjust the model parameters provided average root-mean-square error of 3.4% for power predictions, whereas the average error level found when using data sheet information was 8.8%. An error level as low as 2.83% was reached by adopting the most complex model, although using a significantly simpler modeling approach provided error of 3.25%. Therefore, using field-measured data to adjust model parameters produced the best results, but increasing method complexity did not increase performance in the same scale.