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ANFIS-based modelling for photovoltaic power supply system: A case study

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  • Mellit, Adel
  • Kalogirou, Soteris A.

Abstract

Due to the various seasonal, monthly and daily changes in meteorological data, it is relatively difficult to find a suitable model for Photovoltaic power supply (PVPS) system. This paper deals with the modelling and simulation of a PVPS system using an Adaptive Neuro-Fuzzy Inference Scheme (ANFIS) and the proposition of a new expert configuration PVPS system. For the modelling of the PVPS system, it is required to find suitable models for its different components (ANFIS PV generator, ANFIS battery and ANFIS regulator) that could give satisfactory results under variable climatic conditions in order to test its performance and reliability. A database of measured climate data (global radiation, temperature and humidity) and electrical data (photovoltaic, battery and regulator voltage and current) of a PVPS system installed in Tahifet (south of Algeria) has been recorded for the period from 1992 to 1997. These data have been used for the modelling and simulation of the PVPS system. The results indicated that the reliability and the accuracy of the simulated system are excellent and the correlation coefficient between measured values and those estimated by the ANFIS gave a good prediction accuracy of 98%. Additionally, test results show that the ANFIS performed better than the Artificial Neural Network (ANN), which has also being tried to model the system. In addition, a new configuration of an expert PVPS system is proposed in this work. The predicted electrical data by the ANFIS model can be used for several applications in PV systems.

Suggested Citation

  • Mellit, Adel & Kalogirou, Soteris A., 2011. "ANFIS-based modelling for photovoltaic power supply system: A case study," Renewable Energy, Elsevier, vol. 36(1), pages 250-258.
    • Handle: RePEc:eee:renene:v:36:y:2011:i:1:p:250-258
    DOI: 10.1016/j.renene.2010.06.028
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    References listed on IDEAS

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    10. Mellit, A. & Sağlam, S. & Kalogirou, S.A., 2013. "Artificial neural network-based model for estimating the produced power of a photovoltaic module," Renewable Energy, Elsevier, vol. 60(C), pages 71-78.
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    22. Yang, L. & Entchev, E., 2014. "Performance prediction of a hybrid microgeneration system using Adaptive Neuro-Fuzzy Inference System (ANFIS) technique," Applied Energy, Elsevier, vol. 134(C), pages 197-203.
    23. Fernandez-Jimenez, L. Alfredo & Muñoz-Jimenez, Andrés & Falces, Alberto & Mendoza-Villena, Montserrat & Garcia-Garrido, Eduardo & Lara-Santillan, Pedro M. & Zorzano-Alba, Enrique & Zorzano-Santamaria,, 2012. "Short-term power forecasting system for photovoltaic plants," Renewable Energy, Elsevier, vol. 44(C), pages 311-317.
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