A Regression Algorithm for the Smart Prognosis of a Reversed Polarity Fault in a Photovoltaic Generator
Résumé
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.
Domaines
Energie électrique
Origine : Fichiers produits par l'(les) auteur(s)
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