An Improved Detection and Classification Technique of Harmonic Signals in Power Distribution by Utilizing Spectrogram

Jopri, Mohd Hatta; Abdullah, Abdul Rahim; Manap, Mustafa; Yusoff, M.R.; Sutikno, Tole; Habban, M.F.

doi:10.11591/ijece.v7i1.pp12-20

Cited by 17 publications

(12 citation statements)

References 12 publications

(16 reference statements)

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“…The coefficients of the functions will then be used as the descriptors of local property of the particular signal [32]. Gabor transform uses hanning window just as spectrogram but different in terms of window length (480 samples) [6,33]. Hanning window is selected as window function due to its lower peak side lope which has narrow effect on other frequencies around fundamental value (50 Hz in this study).…”

Section: Power Quality Analysis Methods 31 Gabor Transformmentioning

confidence: 99%

Voltage variations identification using gabor transform and rule-based classification method

Tee¹,

Yusoff²,

Yaakub³

et al. 2020

IJECE

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This paper presents a comparatively contemporary easy to use technique for the identification and classification of voltage variations. The technique was established based on the Gabor Transform and the rule-based classification method. The technique was tested by using mathematical model of Power Quality (PQ) disturbances based on the IEEE Std 519-2009. The PQ disturbances focused were the voltage variations, which included voltage sag, swell and interruption. A total of 80 signals were simulated from the mathematical model in MATLAB and used in this study. The signals were analyzed by using Gabor Transform and the signal pattern, timefrequency representation (TFR) and root-mean-square voltage graph were presented in this paper. The features of the analysis were extracted, and rules were implemented in rule-based classification to identify and classify the voltage variation accordingly. The results showed that this method is easy to be used and has good accuracy in classifying the voltage variation.

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Section: Power Quality Analysis Methods 31 Gabor Transformmentioning

confidence: 99%

Voltage variations identification using gabor transform and rule-based classification method

Tee¹,

Yusoff²,

Yaakub³

et al. 2020

IJECE

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“…whereby the signal component sequence, k, signal component amplitude, A k , fundamental frequency, f 0 , of the signal, t is the time, a box function, (t) of the signal and the number of the signal component, K [26].…”

Section: The Signal Modelmentioning

confidence: 99%

An improved smooth-windowed Wigner-Ville distribution analysis for voltage variation signal

Manap

Abdullah

Nikolovski

et al. 2020

IJECE

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This paper outlines research conducted using bilinear time-frequency distribution (TFD), a smooth-windowed wigner-ville distribution (SWWVD) used to represent time-varying signals in time-frequency representation (TFR). Good time and frequency resolutions offer superiority in SWWVD to analyze voltage variation signals that consist of variations in magnitude. The separable kernel parameters are estimated from the signal in order to get an accurate TFR. The TFR for various kernel parameters is compared by a set of performance measures. The evaluation shows that different kernel settings are required for different signal parameters. Verification of the TFD that operated at optimal kernel parameters is then conducted. SWWVD exhibits a good performance of TFR which gives high peak-to-side lobe ratio (PSLR) and signal-to-cross-terms ratio (SCR) accompanied by low main-lobe width (MLW) and absolute percentage error (APE). This proved that the technique is appropriate for voltage variation signal analysis and it essential for development in an advanced embedded system.

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“…The spectrogram is a mathematical tool used to stimulate an analytical signal from a real-time signal obtained from data collection [12], [22], [23]. It involves a composition between frequency and time resolution [24]. It is one of the time-frequency distributions (TFDs) that describes the signal in time and frequency representations.…”

Section: Time-frequency Distribution (Tfd)mentioning

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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An Improved Detection and Classification Technique of Harmonic Signals in Power Distribution by Utilizing Spectrogram

Cited by 17 publications

References 12 publications

Voltage variations identification using gabor transform and rule-based classification method

Voltage variations identification using gabor transform and rule-based classification method

An improved smooth-windowed Wigner-Ville distribution analysis for voltage variation signal

Spectrogram Based Window Selection for the Detection of Voltage Variation

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