Optimization of SVM Classifier by k-NN for the Smart Diagnosis of the Short-Circuit and Impedance Faults in a PV Generator

Wail Rezgui; Kinza-Nadia Mouss; Leïla-Hayet Mouss; Mohamed Djamel Mouss; Yassine Amirat; Mohamed Benbouzid

doi:10.15866/iremos.v7i5.3442

Optimization of SVM Classifier by k-NN for the Smart Diagnosis of the Short-Circuit and Impedance Faults in a PV Generator

Wail Rezgui^(1*), Kinza-Nadia Mouss⁽²⁾, Leïla-Hayet Mouss⁽³⁾, Mohamed Djamel Mouss⁽⁴⁾, Yassine Amirat⁽⁵⁾, Mohamed Benbouzid⁽⁶⁾

^(*) Corresponding author

Authors' affiliations

DOI: https://doi.org/10.15866/iremos.v7i5.3442

Abstract

This paper deals with a new algorithm allowing short-circuit and impedance faults smart diagnosis of PV generators. It is based on the use of the SVM technique for the classification of observations not located in its margin, otherwise the proposed algorithm is used a k-NN method. A PV generator database containing observations distributed over classes is used for testing the new algorithm performance, which shows therefore its contribution and its effectiveness in the diagnosis area.
Copyright © 2014 Praise Worthy Prize - All rights reserved.

Keywords

Photovoltaic Generator; SVM; k-NN; Short-circuit; Impedance; Smart Diagnosis

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References

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