@article {3071,
	title = {Electrical faults detection for the intelligent diagnosis of a photovoltaic generator, March, ISSN/ISBN 1582-4594/1335-3632.},
	journal = {JEE Journal of Electrical Engineering,},
	volume = {Vol. 14},
	number = {Issue. 1},
	year = {2014},
	pages = {pp. 77-84},
	abstract = {the work presented inthis paper isdedicated to improvingthe methods ofdetection and diagnosisoffaults affectingproduction systems,particularlyphotovoltaic systems.We proposeda newintelligent algorithmfor the detectionand diagnosis ofPVinstallations, capable of detecting and resonateto define thetype of defectsthat canaffectthis typeofsystem. This new algorithmis based onthe notion ofpattern recognition,for that it isable to preparethe representation spaceandthe decision spaceon the one hand,and on the otherhand, theclassificationof all newobservationscollected duringthe functioning of the system. This algorithmmainly based onthe method ofk-nearest neighbor and two toolsof artificial intelligenceto improve thismethod andincreasing the rate ofits classification, which arefuzzy logic tooptimizethe location of thecenters of gravity ofclassesandalsothe new observations,and the neural network thatcan classify thecase of dischargesambiguityandreleasesdistancewhich presentsthe limitations of the methodof thek-nearest neighbor. Wetested the performanceof our algorithm ona databaseofa photovoltaic system at theresearch unit ofGHARDAIAAlgeria.},
	url = {https://pdfs.semanticscholar.org/8d54/cc8ebdfc720b5d95cbb6d4238667874f8799.pdf},
	author = {Rezgui Wail and Mouss Leila Hayet and Kadri Ouahab}
}
