A new offline method for extracting I-V characteristic curve for photovoltaic modules using artificial neural networks. (October 2018)

• Record Type: Journal Article
• Authors: Khatib, Tamer; Ghareeb, Ahmed; Tamimi, Maan; Jaber, Mahmoud; Jaradat, Saif
• Is Part Of: Solar energy. Volume 173(2018)
• Page Start: 462
• Page End: 469
• Publication Date: 2018-10
• Subjects: Solar cell -- I-V curve -- P-V curve -- Photovoltaic -- ANN; Solar energy -- Periodicals; Solar engines -- Periodicals; 621.47
• DOI: 10.1016/j.solener.2018.07.092
• Languages: English
• ISSNs: 0038-092X

Abstract

Highlights: We proposed a new method for I-V curve prediction of photovoltaic modules. The proposed method is based on two artificial neural networks. The proposed method is general for types of photovoltaic modules. The accuracy of the proposed method is almost 99%. Abstract: This paper presents a new I-V curve prediction method using artificial neural networks. The proposed method is based on two artificial neural networks namely generalized regression artificial neural network and cascaded forward neural network. An experiment is set up so as to extract a dataset that includes records of solar radiation, ambient temperature, current and voltage for different photovoltaic modules. The developed model is a general model for all photovoltaic modules whereas the inputs of the model are solar radiation, ambient temperature and datasheet specifications of photovoltaic module (open circuit voltage and short circuit current). Matlab is used to train, test and validate the proposed model. Moreover, the proposed model is validated experimentally. The results show that the proposed model has a high accuracy in predicting I-V curves with average mean absolute percentage error, mean bias error and root mean square error of 1.09%, 0.0229 A and 0.0336 A respectively. Such a model is very helpful in generating I-V curves for different photovoltaic modules.