Maximum Likelihood Parameters Estimation Of Single-Diode Photovoltaic Module/Array: A Comparative Study At STC

Abstract

Maximum likelihood is known as optimal method adapted for parameters estimation with process and measurement noise. Experimental data generally contain measurement noise. In this paper, review on modelling photovoltaic (PV) module/array is established first; next, the combination of the optimization method of maximum likelihood estimator (MLE) and Newton Raphson resolution for identifying the five unknown parameters of single diode photovoltaic Module/Array at different types of test conditions is proposed. This predicted method is compared with generalized least square estimator known also as optimization method. It is also compared with popular predictive methods Villalva's and Lambert solution. The proposed method is applied for polycrystalline silicon photovoltaic MSX60 solar module at standard test condition (STC). Two types of comparison are made: first, the dynamic variations of all five parameters values are carried out by graphs and compared in tables with the values found with the other methods (mentioned above); the parameters have converged after up to 150 iterations at STC and the accuracy of estimated parameters is sensitive to the initial parameters of trust region. Secondly, the (I-V) curves are superposed, justifying the accuracy of the proposed method. The comparative errors graphs are also carried out. The results proved the effectiveness of the maximum likelihood estimator, by accuracy parameters of the PV module/array.