Mariano Sidrach-de-Cardona

A simple model for sizing stand alone photovoltaic systems

We consider a general model for sizing a stand-alone photovoltaic system, using as energy input data the information available in any radiation atlas. The parameters of the model are estimated by multivariate linear regression. The results obtained from a numerical sizing method were used as initial input data to fit the model. The expression proposed allows us to determine the photovoltaic array size, with a coefficient of determination ranging from 0.94 to 0.98. System parameters and mean monthly values for daily global radiation on the solar modules surface are taken as independent variables in the model. It is also shown that the proposed model can be used with the same accuracy for other locations not considered in the estimation of the model.

Performance analysis of a grid-connected photovoltaic system

Grid-connected photovoltaic systems are required to introduce photovoltaic solar energy into urban areas. To analyze these systems, a 2.0 kWp power system has been installed at the University of Malaga, 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%.

A new approach to obtain I-V and P-V curves of photovoltaic modules by using DC-DC converters

The achievement of I-V and P-V curves of photovoltaic modules gives the possibility of obtaining their characteristic parameters: the short-circuit current (I/sub sc/), the open-circuit voltage (V/sub oc/), the maximum power point (MPP) and the fill factor (FF). These values are significant for the design of a photovoltaic system. These curves depend on the global irradiance (G), the temperature (T) and the spectral distribution of the solar irradiation. In this paper, a new methodology to determine the afore mentioned curves by using DC-DC converters is proposed. This methodology allows carrying out the complete sweep of the voltage and the current (including V/sub oc/ and I/sub sc/). Regarding the traditional methods, this new one provides the following advantages: a) minimum power loss with regard to the systems that operate in lineal zone (active zone); this implies several advantages in size and cost; and b) this new method allows an automatic adaptation of the interpolation interval.

Evaluation of a grid-connected photovoltaic system in southern Spain

The results obtained in the evaluation of a 2 kWp grid-connected photovoltaic system in Malaga (Spain) are shown. The data set covers from January until December 1997. The energy losses of the system and the most relevant performances of the installation have been quantified. In this period, the system has suplied 2678 k Wh to the grid. This means a daily energy average of 7,4 k Wh, with a monthly average value of daily system efficiency between 6,1 and 8,0 %. According with the obtained results, the South of Spain is an optimum region for the development of grid-connected photovoltaic systems.

A new application of the coupled-inductors SEPIC converter to obtain I-V and P-V curves of photovoltaic modules

The achievement of I-V and P-V curves of photovoltaic modules gives the possibility to obtain the exact operation point, the short-circuit current (Isc), the open-circuit voltage (Voc), the maximum power point (MPP) and the fill factor (FF). These are significant parameters for the design and for the start-up of a photovoltaic installation. In this paper, we propose and analyse by means of simulation the capacity of the SEPIC (single-ended primary inductance converter) structure to implement I-V and P-V curves tracers of photovoltaic modules, mainly because the SEPIC topology presents the capability of buck-boost conversion. Thanks to this property, a complete sweep of the voltage and the current given by a photovoltaic panel can be made, including Voc and Isc . This structure is implemented by coupled inductors and presents two fundamental advantages: 1) it allows to emulate a resistance at the input converter within the range [0,infin) and 2) it guarantees a null ripple in the input current when the magnetic coupling of his two inductors fulfils ZRC (zero ripple condition)

Data mining and statistical techniques for characterizing the performance of thin-film photovoltaic modules

A method for characterizing the performance ratio of thin-film photovoltaic modules based on the use of data mining and statistical techniques is developed. In general, this parameter changes when modules are working in outdoor conditions depending on irradiance, temperature, air mass and solar spectral irradiance distribution. The problem is that it is usually difficult to know how to include solar spectral irradiance information when estimating the performance of photovoltaic modules. We propose five different solar spectral irradiance distributions that summarize all the different distributions observed in Malaga. Using the probability distribution functions of these curves and a statistical test, we first checked when two spectral distributions measured can be considered to have the same contribution of energy per wavelength. Hence, using this test and the k-means data mining technique, all the measured spectra, more than two hundred and fifty thousand, are clustered in only five different groups. All the spectra in each cluster can be considered as equal and the k-means technique estimates one centroid for each cluster that corresponds to the cumulative probability distribution function that is the most similar to the rest of the samples in the cluster. The results obtained proves that 99.98% of the functions can be considered equal to the centroid of its cluster. With these five types of functions, we have explained the changes in the performance ratio measured for thin-film photovoltaic modules of different technologies.

Modeling time series of climatic parameters with probabilistic finite automata

A model to characterize and predict continuous time series from machine-learning techniques is proposed. This model includes the following three steps: dynamic discretization of continuous values, construction of probabilistic finite automata and prediction of new series with randomness. The first problem in most models from machine learning is that they are developed for discrete values; however, most phenomena in nature are continuous. To convert these continuous values into discrete values a dynamic discretization method has been used. With the obtained discrete series, we have built probabilistic finite automata which include all the representative information which the series contain. The learning algorithm to build these automata is polynomial in the sample size. An algorithm to predict new series has been proposed. This algorithm incorporates the randomness in nature. After finishing the three steps of the model, the similarity between the predicted series and the real ones has been checked. For this, a new adaptable test based on the classical KolmogoroveSmirnov two-sample test has been done. The cumulative distribution function of observed and generated series has been compared using the concept of indistinguishable values. Finally, the proposed model has been applied in several practical cases of time series of climatic parameters. � 2004 Elsevier Ltd. All rights reserved.

Framework for Monitoring and Assessing Small and Medium Solar Energy Plants

The monitoring and assessment of small and medium solar energy plants were ruled out as the existing programs for these tasks are expensive and they are designed to run directly on the installation site, making it necessary to have both a monitoring system, such as data logger, and specialized staff capable of analyzing the monitoring data. To address these problems, this paper presents a framework that allows the development of programs for remote monitoring and automatic evaluation of solar energy plants without using any additional hardware. Software architecture based on separating the software functionalities into several layers and on using a hierarchical model of the plant elements is proposed. This framework allows the integration of different technologies and communication protocols of devices used in solar energy plants. A monitoring and assessing program for several dispersed solar energy installations has been developed as practical example.

Binding statistical and machine learning models for short-term forecasting of global solar radiation

A model for short-term forecasting of continuous time series has been developed. This model binds the use of both statistical and machine learning methods for short-time forecasting of continuous time series of solar radiation. The prediction of this variable is needed for the integration of photovoltaic systems in conventional power grids. The proposed model allows us to manage not only the information in the time series, but also other important information supplied by experts. In a first stage, we propose the use of statistical models to obtain useful information about the significant information for a continuous time series and then we use this information, together with machine learning models, statistical models and expert knowledge, for short-term forecasting of continuous time series. The results obtained when the model is used for solar radiation series show its usefulness.

Binding machine learning models and OPC technology for evaluating solar energy systems

This paper describes a framework to develop software to monitor and evaluate solar installations using machine-learning models and OPC technology. The proposed framework solves both the problem of monitoring solar installations when there are devices from different manufacturers and the problem of evaluating solar installations whose operation changes throughout the plant operation period. Moreover, the evaluation programs can be integrated with the monitoring problems. The proposed solution is based on the use of machine-learning models to evaluate the plants and on the use of OPC technology to integrate the monitoring program with the evaluation program. This framework has been used for monitoring and evaluating several real photovoltaic solar plants.