Experimental measurement system and neural network model to simulate photovoltaic modules

Para la simulación de la curva l-V de los módulos fotovoltaicos se ha propuesto la utilización de un perceptrón multicapa. Se han evaluado cómo contribuyen a esta simulación distintos parámetros de entrada, como son el ángulo de incidencia, el Índice de transparencia atmosférico y la distribución espectral de la radiación. Fecha de lectura de Tesis Doctoral: 30 de septiembre 2011.El objetivo de esta tesis es el desarrollo de una metodología de medida, caracterización y simulación de módulos fotovoltaicos que pueda ser de utilidad para los investigadores e ingenieros del campo de la tecnología solar fotovoltaica. Para la parte de medida se ha desarrollado un nuevo sistema de medida de curvas l-V para módulos fotovoltaicos. En la parte de caracterización y simulación, se ha propuesto un modelo basado en redes neuronales que permite extrapolar estas curvas a distintas condiciones reales de funcionamiento. El sistema de medida propuesto resuleve los problemas detectados en los métodos que se están utilizando en la actualidad. En concreto, y como más importante deficiencia a la que se da solución en esta tesis, está el problema de obtener los valores de los dos parámetros que configuran estas curvas, a saber, corriente y tensión, de manera simultánea. El sistema propuesto está basado en la utilización de una carga electrónica de cuatro cuadrantes y dos multimetros digitales sincronizados con un generador de ondas que crea una señal cuadrada para disparar ambos multímetros. Este método de sincronización asegura que las medidas de tensión y corriente se efectúan de manera simultánea: esto no se asegura con otros métodos previamente usados que utilizan una sincronización vía GPIB. Además, se hace una propuesta de utilizar esquemas XML para el formato de los datos registrados en laboratorios fotovoltaicos. Este formato puede contribuir a una estandarización de los datos que se utilizan para la caracterización de módulos fotovoltaicos por distintos laboratorios de medida. Esto facilitará el intercambio de información entre estos laboratorios

New software tool to characterize photovoltaic modules from commercial equipment

A software platform has been developed in order to unify the different measurements obtained from different manufacturers in the photovoltaic system laboratory of the University of Malaga, Spain. These measurements include the current-voltage curve of PV modules and several meteorological parameters such as global and direct irradiance, temperature and spectral distribution of solar irradiance. The measurements are performed in an automated way by a stand-alone application that is able to communicate with a pair of multimeters and a bipolar power supply that are controlled in order to obtain the current–voltage pairs. In addition, several magnitudes, that can be configured by the user, such as irradiance, module temperature or wind speed, are incorporated to register the conditions of each measurement. Moreover, it is possible to attach to each curve the spectral distribution of the solar radiation at each moment. Independently of the source of the information, all these measurements are stored in a uniform relational database. These data can be accessed through a public web site that can generate several graphics from the data.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Junta de Andalucía. Proyecto de Excelencia P11-RNM-711

Characterisation of hourly temperature of a thin-film module from weather conditions by artificial intelligence techniques

The aim of this paper is the use and validation of artificial intelligence techniques to predict the temperature of a thin-film module based on tandem CdS/CdTe technology. The cell temperature of a module is usually tens of degrees above the air temperature, so that the greater the intensity of the received radiation, the greater the difference between these two temperature values. In practice, directly measuring the cell temperature is very complicated, since cells are encapsulated between insulation materials that do not allow direct access. In the literature there are several equations to obtain the cell temperature from the external conditions. However, these models use some coefficients which do not appear in the specification sheets and must be estimated experimentally. In this work, a support vector machine and a multilayer perceptron are proposed as alternative models to predict the cell temperature of a module. These methods allow us to achieve an automatic way to learn only from the underlying information extracted from the measured data, without proposing any previous equation. These proposed methods were validated through an experimental campaign of measurements. From the obtained results, it can be concluded that the proposed models can predict the cell temperature of a module with an error less than 1.5 °C.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Photovoltaic module series resistance identification at its maximum power production

Analysis of measured current–voltage curves offers a cost-effective option for online condition monitoring of photovoltaic (PV) modules. The current–voltage curves of PV modules can be modeled accurately using the well-known electrical single-diode model. In practical applications, condition monitoring should be based on measurements performed near the maximum power point (MPP) by affecting PV power production negligibly. The series resistance is the most important single-diode model parameter in assessing the condition of PV modules; this paper proposes a novel method for its determination by using measurements acquired near the MPP only. The proposed method can be used with any series resistance identification procedure based on current–voltage curve measurements. The proposed method is experimentally validated using current–voltage curves of two PV modules measured in Malaga, Spain. This study allows to assess that the series resistance can be accurately determined from measurements performed near the MPP. Especially the results obtained with an ISOFOTON ISF-145 PV module are very promising: the scaled series resistances obtained from measurements done without lowering the PV power more than 2% of the maximum power differ on the average by no more than 2% of the series resistances obtained from the whole current–voltage curves.Peer reviewe

Analysis of the degradation of amorphous silicon-based modules after 11 years of exposure by means of IEC60891:2021 procedure 3

The degradation of two amorphous silicon-based photovoltaic (PV) modules, namely, of single junction amorphous silicon (a-Si) and of micromorph tandem (a-Si/μ-Si), after 11 years of exposure in the south of Spain is analyzed. Their I-V curves were measured outdoors to study the changes of the electrical parameters in the course of three different periods: during the initial days of exposure, during the first year, and in the subsequent 10-year period. The translation of the curves to an identical set of operating conditions, which enables a meaningful comparison, was done by the dif ferent correction procedures described in the standard IEC60891:2021, including the procedure 3, which does not require the knowledge of module parameters, whose values are typically not available. The annual power degradation rates over the entire 11-year period are 1.12% for the a-Si module, which is 3.02% for the first year, and 0.98% for the a-Si/μ-Si, which is 2.29% for the initial yearThis work is supported by Ministero dell'Istruzione, dell'Università e della Ricerca (Italy) (grant PRIN2020-HOTSPHOT 2020LB9TBC and grant PRIN2017-HEROGRIDS 2017WA5ZT3_003); Università degli Studi di Salerno (FARB funds); Ministerio de Ciencia, Innovacion y Universidades (Spain) (grant RTI2018-095097-B-I0)

Mismatching and partial shading identification in photovoltaic arrays by an artificial neural network ensemble

Photovoltaic arrays may suffer from a number of temporary and permanent faults. Partial shading and soiling belong to the former group, while cell cracking and delamination fall within the latter one. In these cases, the shape of the current vs voltage curve around the maximum power point shows features that are different from those ones of an array operating in normal conditions. The shape change should allow triggering, in case of a temporary fault, control actions that might improve the electrical power production. Instead, if the fault is permanent, the identification of the modified shape should activate a procedure for a more in-depth analysis of the problem and a maintenance action. In this paper, the conditions leading to a change in the array behavior during its delivering of the maximum power are examined. The change of curvature of the current vs voltage curve around its maximum power point is suitably detected to trigger the fault mitigation action. The feature is caught through an ensemble of artificial neural networks, which analyzes the current vs voltage curve and classifies the module as healthy or faulty. It is demonstrated that few samples around the maximum power point are required, this meaning that the proposed approach is compatible with the operation of any perturbative maximum power point tracking algorithm and its application does not lead to any power production drop. In addition, the approach does not require neither temperature nor irradiance measurements as inputs. The neural networks are trained through synthetic data, so that their application is not limited to arrays including a specific photovoltaic module. The method is also validated through experimental data

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

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 temperature. 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.publishedVersionPeer reviewe