Performance analysis of a grid-connected photovoltaic system

Abstract Grid-connected photovoltaic systems are required to introduce photovoltaic solar energy into urban areas. To analyze these systems, a 2.0 kW p power system has been installed at the University of Málaga, Spain. The array power output was estimated by using measured I-V curves for the installed modules with minimization of mismatch losses. The supplied grid energy and main performances are described. The effects on system yield of threshold-inverter and coupling losses of the inverter to the grid have been studied. During 1997, the system supplied 2678 kWh to the grid, i.e. the mean daily output, was 7.4 kWh. The annual performance ratio was 64.5% and the optimal value 67.9%

New microcogeneration with solar energy: stirling, thermal and photovoltaic systems.

https://www.icrepq.com/full-papers.htm --- he papers presented at the ICREPQ'24 could bepublished, free of charge, in the open access "Renewable Energies and Power Quality Journal (RE&PQJ)", ISSN: 2172-038XThis paper analyses the viability of a new microcogeneration system with a Stirling engine-based micro-CHP (combined heat and power) and renewable solar energy: thermal and photovoltaic system in order to supply the energy demand of an isolated home. The right sizing of solar energy systems should allow optimizing both the available solar resources and gas consumption for one specific climatological conditions. The present work shows the first results of the experimental study of a micro-cogeneration system (micro-CHP based on the Stirling motor) supported with renewable energy. The system of self-government covers the theoretical daily consumption of a house. In this results, the different contributions of the subsystems to the daily thermal and electric demand are displayed

Comparative Analysis of the Effects of Spectrum and Module Temperature on the Performance of Thin Film Modules on Different Sites

The electric behavior under natural sunlight of thin film PV modules is more difficult to predict than that of crystalline silicon ones owing to the higher sensitivity to the spectral distribution of the former when compared with the latter, among some other factors. The purpose of this work is aimed at looking into the influence of the spectral irradiance and the module temperature on the outdoor performance of recent commercially available a- Si, CdTe and a-Si/μc-Si modules in sites with different climates in Spain. This paper is addressed to present the results of a 12-month experimental campaign experienced by modules of these thin film technologies carried out in the utilities of the CIEMAT/DER (Madrid, continental climate) and those of the University of Málaga (Málaga, Mediterranean climate). For each one of the tested specimens, contour graphs of their performance ratio (PR) as a function of module temperature and average photon energy (APE) are shown. A strong dependence of PR on APE is noticeable at module temperatures below some 45º C so that as a general trend, the module performance improves as APE increases. However, the tested a-Si and a-Si/uc-Si modules show little sensitivity to module temperature within some specific ranges of values of APE which lie in the vicinity of the APE value for the AM1.5G spectrum. Last, spectral gains achieved at high values of APE together with cold temperatures yield figures of PR above 1 in some cases

Series resistance temperature sensitivity in degraded mono–crystalline silicon modules

Manufacturers of photovoltaic cells and modules usually provide temperature coefficients referring to the short–circuit current, the open–circuit voltage and the maximum power. Few studies analyse the variation of the series resistance with respect to the cell or module temperature. In this paper, this dependency is studied by suitably processing a set of current–voltage curves acquired on several modules working under outdoor conditions. The curves are measured at an increasing module temperature. The temperature coefficient of the series resistance is estimated by using the single diode model and the double diode one. Some hundreds of current vs voltage curves referring to degraded photovoltaic modules are used in this paper to analyse the effects of the degradation on the series resistance and on the temperature coefficient thereof

Multilayer perceptron applied to the estimation of the influence of the solar spectral distribution on thin-film photovoltaic modules

In this paper, we propose the use of a methodology to characterise the electrical parameters of several thin-film photovoltaic module technologies. This methodology allows us to use not only solar irradiance and module temperature as classical models do, but also spectral distribution of solar radiation. The methodology is based on the use of neural network models. From all measured I-V curves of a module, a previous selection of them has been used in order to train the neural network model. This selection is performed using a Kohonen self-organising map fed with spectral data. This spectral information has been added as an input to the neural network itself. The results show that the incorporation of spectral measurements to simulate thin-film modules improves significantly both the fitting of the predicted I-V curve to the measured one and the peak power point estimation. © 2013 Elsevier Ltd

Experimental system for current-voltage curve measurement of photovoltaic modules under outdoor conditions

This paper describes an experimental system developed in the photovoltaic laboratory at the University of Málaga (Spain) to measure the current-voltage curve of photovoltaic modules under outdoor conditions. The measurement is performed in an automated way by employing commercial instruments controlled by a computer using the GPIB standard. Several modules, selected sequentially through a set of relays, are biased by a four-quadrant power supply while a function generator synchronizes two multimeters in order to acquire voltage and current values. The measurement uncertainties were also estimated. The proposed method for synchronizing the voltage and current measurements ensures that these measurements are performed simultaneously; this means that the estimated uncertainty is lower than those obtained using other previously proposed methods. The main electrical parameters are estimated. A user-friendly application allows the user to configure several parameters, such as bias rate, voltage, and current limits and the number of points of the curve. Copyright © 2011 John Wiley & Sons, Ltd

An intelligent memory model for short-term prediction: An application to global solar radiation data

This paper presents a machine learning model for short-term prediction. The proposed procedure is based on regression techniques and on the use of a special type of probabilistic finite automata. The model is built in two stages. In the first stage, the most significant independent variable is detected, then observations are classified according to the value of this variable and regressions are re-run separately for each Group. The significant independent variables in each group are then discretized. The PFA is built with all this information. In the second stage, the next value of the dependent variable is predicted using an algorithm for short term forecasting which is based on the information stored in the PFA. An empirical application for global solar radiation data is also presented. The predictive performance of the procedure is compared to that of classical dynamic regression and a substantial improvement is achieved with our procedure. © 2010 Springer-Verlag

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

Empirical model to predict the operating temperature of the modules of a photovoltaic system

The aim of this work is the validation of a regression model to forecast the cell temperature of a photovoltaic module under different conditions from those for which it was fitted. In a previous article, a model which uses as inputs the air temperature, the incident irradiation and the wind speed was proposed to forecast the cell temperature. To know if this model could be universally used, the cell temperature of a module of the same technology installed in another location with different weather conditions has been computed using the coefficients estimated previously. From the obtained results, it can be concluded that the model is able to predict the operating temperature of the generator of a power plant and could be applied to this technology at any location regardless of the weather conditions.