Sistema de deteccion de eventos de calidad de energ&amp;#x00ED;a basado en Maquinas de Vectores de Soporte

Yong, D. De; Bhowmik, S.; Magnago, Fernando

doi:10.1109/argencon.2014.6868480

Cited by 1 publication

(1 citation statement)

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“…K-Nearest Neighbors Algorithm (KNN) as well as Support Vector Machine (SVM) are used as classifiers in this study. KNN is a non-parametric method to assign weight to the contributions of the neighbors, so that the closer neighbors attribute more to the average than the further ones [18], [19].SVM is a supervised learning models that analyses data which build a model that appoints new patterns to one category, making it a non-probabilistic binary linear classifier [5], [20].…”

Section: Introductionmentioning

confidence: 99%

Spectrogram Based Window Selection for the Detection of Voltage Variation

Tee¹,

Yusoff²,

Abdullah³

et al. 2019

IJIE

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This paper presents the application of spectrogram with K-nearest neighbors (KNN) and Support Vector Machine (SVM) for window selection and voltage variation classification. The voltage variation signals such as voltage sag, swell and interruption are simulated in Matlab and analyzed in spectrogram with different windows which are 256, 512 and 1024. The variations analyzed by spectrogram are displayed in time-frequency representation (TFR) and voltage per unit (PU) graphs. The parameters are calculated from the TFR obtained and be used as inputs for KNN and SVM classifiers. The signals obtained are then added with noise (0SNR and 20SNR) and used in classification. The tested data contain voltage variation signals obtained using the mathematical models simulated in Matlab and the signals added with noise. Classification accuracy of each window by each classifier is obtained and compared along with the TFR and voltage PU graphs to select the best window to be used to analyze the best window to be used to analyze the voltage variation signals in spectrogram. The results showed window 1024 is more suitable to be used.

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Section: Introductionmentioning

confidence: 99%