Spectrogram Based Window Selection For The Detection Of Voltage Variation

Abdullah, Abdul Rahim and Tee, Wei Hown and Nik Anwar, Nik Syahrim and Yusoff, Mohd Rahimi and Jopri, Mohd Hatta and Musa, Haslinda (2019) Spectrogram Based Window Selection For The Detection Of Voltage Variation. International Journal of Integrated Engineering, 11 (3). pp. 240-247. ISSN 2229-838X

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Detection, Noise, Spectrogram, Voltage variation
Divisions:	Faculty of Electrical Engineering
Depositing User:	Sabariah Ismail
Date Deposited:	29 Jul 2020 12:57
Last Modified:	29 Jul 2020 12:57
URI:	http://eprints.utem.edu.my/id/eprint/24170
Statistic Details:	View Download Statistic

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