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

Author

Rezgui, Wail ; Mouss, Nadia Kinza ; Mouss, Leila-Hayet ; Mouss, Mohamed Djamel ; Benbouzid, Mohamed

Author_Institution

LAP-Lab., Univ. of Batna, Batna, Algeria

fYear

2014

fDate

25-27 March 2014

Firstpage

134

Lastpage

138

Abstract

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.

Keywords

fault diagnosis; photovoltaic power systems; power system analysis computing; regression analysis; support vector machines; K-NNR; PV generators; SVR technique; k-NN regression tool; prediction approach; reversed polarity fault diagnosis; smart algorithm; smart prognosis; support vector regression technique; undetermined outputs; Circuit faults; Generators; Photovoltaic systems; Prediction algorithms; Prognostics and health management; Support vector machines; Photovoltaic generator; SVR; diagnosis; k-NNR; prognosis; reversed polarity fault;

fLanguage

English

Publisher

ieee

Conference_Titel

Green Energy, 2014 International Conference on

Conference_Location

Sfax

Print_ISBN

978-1-4799-3601-4

Type

conf

DOI

10.1109/ICGE.2014.6835411

Filename

6835411