Comparison of two PV array models for the simulation of PV systems using five different algorithms for the parameters identification

RENEWABLE ENERGY

卷:99

Article

Kichou, Sofiane¹  Silvestre, Santiago¹  Guglielminotti, Letizia¹  Mora-Lopez, Llanos²  Munoz-Ceron, Emilio³ 

[1] UPC BarcelonaTech Barcelona, Dept Elect Engn, MNT Grp, C Jordi Girona 1-3,Modul C4 Campus Nord UPC, Barcelona 08034, Spain

[2] Univ Malaga, Dpto Lenguajes & Ciencias Computac, Campus Teatinos Sn, E-29071 Malaga, Spain

[3] Univ Jaen, IDEA Res Grp, Campus Las Lagunillas, Jaen 23071, Spain

关键词: PV modeling;    Simulation;    Parameter extraction;    Metaheuristic algorithms;   

DOI  :  10.1016/j.renene.2016.07.002

来源: Elsevier

【 摘 要 】

Simulation is of primal importance in the prediction of the produced power and automatic fault detection in PV grid-connected systems (PVGCS). The accuracy of simulation results depends on the models used for main components of the PV system, especially for the PV module. The present paper compares two PV array models, the five-parameter model (5PM) and the Sandia Array Performance Model (SAPM). Five different algorithms are used for estimating the unknown parameters of both PV models in order to see how they affect the accuracy of simulations in reproducing the outdoor behavior of three PVGCS. The arrays of the PVGCS are of three different PV module technologies: Crystalline silicon (c-Si), amorphous silicon (a-Si:H) and micromorph silicon (a-Si:H/mu c-Si:H). The accuracy of PV module models based on the five algorithms is evaluated by means of the Route Mean Square Error (RMSE) and the Normalized Mean Absolute Error (NMAE), calculated for different weather conditions (clear sky, semi-cloudy and cloudy days). For both models considered in this study, the best accuracy is obtained from simulations using the estimated values of unknown parameters delivered by the ABC algorithm. Where, the maximum error values of RMSE and NMAE stay below 6.61% and 2.66% respectively. (C) 2016 Elsevier Ltd. All rights reserved.

【 授权许可】

Free