Efficient predictive models for characterization of photovoltaic module performance. (April 2020)

• Record Type: Journal Article
• Authors: Al-Subhi, Ahmad; El-Amin, Ibrahim; Mosaad, Mohamed I.
• Is Part Of: Sustainable energy technologies and assessments. Volume 38(2020)
• Publication Date: 2020-04
• Subjects: PV -- MPP -- Mathematical models -- EUREQA -- I-V curve; Renewable energy sources -- Periodicals; Energy development -- Technological innovations -- Periodicals; Electric power production -- Periodicals; Energy storage -- Periodicals; 333.79
• DOI: 10.1016/j.seta.2020.100672
• Languages: English
• ISSNs: 2213-1388

Abstract

Abstract: This paper presents simple, efficient and general mathematical models for predicting the I-V characteristics of any Photovoltaic (PV) module at any irradiance and temperature values without any need to estimate the parameters of the PV circuit model. The prediction of the I-V characteristics in this paper is mainly dependent on building general mathematical models for ten key points selected from the I-V characteristics. To generalize these models and make them applicable for any PV module, twenty different PV modules covering a wide range of PV modules with different temperature and irradiance values are selected for training data generation. All simulations are performed in MATLAB/SIMULINK. The training process and models development are built using EUREQA software. The performance of the developed models is tested by comparison with simulation data, experimental data, and different recent research articles. The assessments have shown efficient prediction capability of all models to predict the I-V curves for any PV module, at any weather condition, without evaluating the PV circuit model parameters. The models have also exceeded various algorithms used in the literature in terms of accuracy of I-V characteristics estimation with less execution time.