PV cell Parameters Modeling and Temperature Effect Analysis

With the wide acceptance of modeling a PV cell by a single diode, a series and parallel resistors; many researchers have discussed different mathematical forms and iterative techniques to extract the values of these model elements depending on the key parameters provided by the manufacturer datasheet. This paper avoids iterative techniques and obtains the values of the five parameters of the one diode model by developing closed form expressions. The maximum error produced by this technique is 10% when compared to the exact values of the one diode model circuit built by Spice. The 10% maximum error has occurred during the estimation of the reverse saturation current (Io) of the diode, nevertheless, it should be mentioned that even for this same parameter the model outperforms many iterative dependent works. Furthermore, this paper discusses the effect of temperature on the operation performance of PV cells. In particular, the temperature effect on the open circuit voltage, the short circuit current, the fill factor, the reverse saturation current, and the conversion efficiency was modeled and evaluated for different brand technologies

A Novel Method to Extract Single-Diode PV Parameters Based on Datasheet Values

Very few manufacturers that are providing a wide range of graphical data necessary to use high-performance predictive tools for PV systems. On the other hand, reliable graphical data require precise laboratory measurements that increase manufacturing costs. Furthermore, most of the methods used to determine photovoltaic performance require iterative techniques, additional mandatory information and users with a certain level of knowledge. For this reason, PV system designers have to choose between the use of cheap PV modules, lacking in technical data, and the reliable energy predictions that are possible only if the current–voltage characteristics are provided by the PV module manufacturers. This paper presents a novel method to extract the single diode five parameters model for different manufacturer's modules using the minimum set of technical data that are usually provided by all manufacturers. The proposed procedure uses an accurate and easy method that does not require iterative techniques, other obligatory information or users with special knowledge but only datasheet values from a technical catalog of photovoltaic modules to simulate photovoltaic performance. The method uses an empirical relationship that enables to calculate the initial values of series and shunt resistances and extracts the parameters of the model from datasheet values provided by manufacturers only. The capability of the proposed model to calculate the current–voltage characteristics was tested by comparing the results with data measured by different manufacturers’ panel models (mono-crystalline and poly-crystalline) silicon modules. The method was also compared with other similar methods. The results of the new model application confirm the reliability of the procedure.

Using genetic algorithms for estimating Weibull parameters with application to wind speed

Renewable energy has become a prominent subject for researchers since fossil fuel reserves have been decreasing and are not promising to meet the energy demand of the future. Wind takes an important place in renewable energy resources and there is extensive research on wind speed modeling. Herein, one of the most commonly used distributions for wind speed modeling is the Weibull distribution with its simplicity and flexibility. Maximum likelihood (ML) method is the most frequently used technique in Weibull parameter estimation. Iterative techniques such as Newton-Raphson (NR) use random initial values to obtain the ML estimators of the parameters of the Weibull distribution. Therefore, the success of the iterative techniques highly depends on the initial value selection. In order to deliver a solution to the initial value problem, genetic algorithm (GA) is considered to obtain the estimators of the model parameters. The ML estimators obtained using the GA and NR techniques are compared with the method of moments (MoM) estimators via Monte Carlo simulation and wind speed applications. The results show that the ML estimators obtained using GA present superiority over MoM and the ML estimators obtained using NR.

Constant Sign Solutions to Linear Fractional Integral Problems and Their Applications to the Monotone Method

This manuscript provides some results concerning the sign of solutions for linear fractional integral equations with constant coefficients. This information is later used to prove the existence of solutions to some nonlinear problems, together with underestimates and overestimates. These results are obtained after applying suitable modifications in the classical process of monotone iterative techniques. Finally, we provide an example where we prove the existence of solutions, and we compute some estimates.