Application of the Genetic Algorithms for Identifying the Electrical Parameters of PV Solar Generators

The determination of model parameters plays an important role in solar cell design and fabrication, especially if these parameters are well correlated to known physical phenomena. A detailed knowledge of the cell parameters can be an important way for the control of the solar cell manufacturing process, and may be a mean of pinpointing causes of degradation of the performances of panels and photovoltaic systems being produced. For this reason, the model parameters identification provides a powerful tool in the optimization of solar cell performance. The algorithms for determining model parameters in solar cells, are of two types: those that make use of selected parts of the characteristic (Chan et al., 1987; Charles et al., 1981; Charles et al., 1985; Dufo-Lopez and Bernal-Agustin, 2005; Enrique et al., 2007) and those that employ the whole characteristic (Haupt and Haupt, 1998; Bahgat et al., 2004; Easwarakhanthan et al., 1986). The first group of algorithms involves the solution of five equations derived from considering select points of an current-voltage (I-V) characteristic, e.g. the open-circuit and short-circuit coordinates, the maximum power points and the slopes at strategic portions of the characteristic for different level of illumination and temperature. This method is often much faster and simpler in comparison to curve fitting. However, the disadvantage of this approach is that only selected parts of the characteristic are used to determine the cell parameters. The curve fitting methods offer the advantage of taking all the experimental data in consideration. Conversely it has the disadvantage of artificial solutions. The nonlinear fitting procedure is based on the minimisation of a not convex criterion, and using traditional deterministic optimization algorithms leads to local minima solutions. To overcome this problem, the nonlinear least square minimization technique can be computed with global search approaches such Genetic Algorithms (GAs) (Haupt and Haupt, 1998; Sellami et al., 2007; Zagrouba et al., 2010) strategy, increasing the probability of obtaining the best minimum value of the cost function in very reasonable time. In this chapter, we propose a numerical technique based on GAs to identify the electrical parameters of photovoltaic (PV) solar cells, modules and arrays. These parameters are, respectively, the photocurrent (Iph), the saturation current (Is), the series resistance (Rs), the