@prefix dcat: <http://www.w3.org/ns/dcat#> .
@prefix dct: <http://purl.org/dc/terms/> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix vcard: <http://www.w3.org/2006/vcard/ns#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

<https://rec.harvest-normandie.data4citizen.com/dataset/oai-hal-tel-00650911v2> a dcat:Dataset ;
    dct:description """
              The recognition of disturbances affecting MV networks is essential to industrials and distribution system operators. The aim of this thesis work is to design a near real-time automatic system able to detect and identify disturbances from their waveforms. Segmentation methods split the disturbed waveforms into transient and steady-state intervals. They use Kalman filters or anti-harmonic filters to extract the transient intervals. Adaptive thresholding methods increase the detection capacity while a posterior delay compensation methods improve the accuracy of the decomposition. Indicators adapted to the disturbance dynamic are used to characterize its steady-state and transient phases. They are robust to segmentation inaccuracies as well as to steady-state disturbances such as harmonics. Two distinct decision systems are also studied: expert recognition systems and SVM classifiers. During the learning stage, a large simulated event database is used to train both systems. Their performances are evaluated on real events: the type and direction of the measured disturbances are determined with a recognition rate over 98%.
            """ ;
    dct:identifier "NNT: 2011SUPL0008" ;
    dct:issued "2026-05-14T00:10:59.631613"^^xsd:dateTime ;
    dct:language "fr" ;
    dct:modified "2026-05-14T00:10:59.631618"^^xsd:dateTime ;
    dct:publisher <https://rec.harvest-normandie.data4citizen.com/organization/cce9db95-46d9-4dc2-84b6-764215d0a002> ;
    dct:title "Identification and Characterization of Power Quality Disturbances affecting MV Distribution Networks" ;
    dcat:contactPoint [ a vcard:Organization ;
            vcard:fn "CCSD" ] ;
    dcat:distribution <https://rec.harvest-normandie.data4citizen.com/dataset/oai-hal-tel-00650911v2/resource/80721018-c321-4a6a-9f40-515740adb15c> ;
    dcat:keyword "caracterisation",
        "characterization",
        "classifieurs-svm",
        "electrical-networks",
        "expert-system",
        "infoeu-reposemanticsdoctoralthesis",
        "pattern-recognition",
        "power-quality",
        "qualite-de-lelectricite",
        "reconnaissance",
        "reseaux-electriques",
        "segmentation",
        "spiotherengineering-sciences-physicsother",
        "support-vector-machines",
        "systemes-experts",
        "theses" ;
    dcat:landingPage <https://theses.hal.science/tel-00650911> .

<https://rec.harvest-normandie.data4citizen.com/dataset/oai-hal-tel-00650911v2/resource/80721018-c321-4a6a-9f40-515740adb15c> a dcat:Distribution ;
    dct:format "HTML" ;
    dct:issued "2026-05-14T00:10:59.740680"^^xsd:dateTime ;
    dct:modified "2026-05-14T00:10:59.594309"^^xsd:dateTime ;
    dct:title "Identification and Characterization of Power Quality Disturbances affecting MV Distribution Networks" ;
    dcat:accessURL <https://theses.hal.science/tel-00650911> .

<https://rec.harvest-normandie.data4citizen.com/organization/cce9db95-46d9-4dc2-84b6-764215d0a002> a foaf:Agent ;
    foaf:name "test_moissonnage_selune" .

<https://theses.hal.science/tel-00650911> a foaf:Document .