Contents I. Introduction
The behaviour of photovoltaic module in current-voltage and voltage-power has been described by complex and nonlinear analytical equations. The solar module model is complex, with nonlinear function varying with temperature and solar irradiation and difficult to identify its parameters by the conventional methods [1]. Several mathematical models and many studies are focused on the modelling of poly-crystalline [2] solar cell and developing several electric models with different level of complexity. Therefore, it is vital to produce more accurate models that can better reveal the actual behaviour of solar cell and photovoltaic module. Since the photovoltaic module model has double non-linearity in the current-voltage and in the intrinsic parameters, multi-variable and multi-modal problem with many local optima, the analytical methods [3] [4] such as Quasi-Newton method (N-R) [5] can't extract the parameters accurately and require certain conditions such as continuity, convexity and differentiability for being applicable, and due of the nonlinearity of current-voltage makes the analytical optimization techniques unable to effectively solve the parameter identification problem. Therefore, because of their great potentials in solving modern global optimization for nonlinear and complex system, the use of the meta-heuristic stochastic optimization algorithms has received considerable attention recently such as Nelder-Mead and Modified Particle Swarm Optimization (NM-MPSO) [6], pattern search (PS) [7], Genetic Algorithm (GA) [8] and Simulated Annealing algorithm (SA) [9] have been used to extract and identify the parameter estimation issue. However, most of cited algorithms trapped at local points and have large values of errors, and more capable algorithms are still need to extract the optimal parameters for solar cell and photovoltaic modules.
