A regression algorithm for the smart prognosis of a reversed polarity fault in a photovoltaic generator

@article{Rezgui2014ARA,
  title={A regression algorithm for the smart prognosis of a reversed polarity fault in a photovoltaic generator},
  author={Wail Rezgui and Nadia Kinza Mouss and Leila-Hayet Mouss and Mohamed Djamel Mouss and M. E. Benbouzid},
  journal={2014 First International Conference on Green Energy ICGE 2014},
  year={2014},
  pages={134-138}
}
This paper deals with a smart algorithm allowing reversed polarity fault diagnosis and prognosis in PV generators. The proposed prognosis (prediction) approach is based on the hybridization of a support vector regression (SVR) technique optimized by a k-NN regression tool (K-NNR) for undetermined outputs. To test the proposed algorithm performance, a PV generator database containing sample data is used for simulation purposes. 

Citations

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Faults effects analysis in a photovoltaic array based on current-voltage and power-voltage characteristics

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Faults modeling of the impedance and reversed polarity types within the PV generator operation

  • 3rd International Symposium on Environmental Friendly Energies and Applications (EFEA)
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Modeling the PV generator behavior submit to the open-circuit and the short-circuit faults

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