Short-circuit switches fault analysis of voltage source inverter using spectrogram

Abdullah, Abdul Rahim; Ahmad, Naseer; Bahari, Norhazilina; Manap, Mustafa; Jidin, Auzani; Jopri, Mohd Hatta

doi:10.1109/icems.2013.6713294

Cited by 22 publications

(20 citation statements)

References 9 publications

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“…Total harmonic distortion, THD, is used to measure of how much harmonic content in a waveform [26], [27], [8]. The total harmonic distortion of a waveform is defined mathematically as equation 2.…”

Section: Instantaneous Total Harmonic Distortionmentioning

confidence: 99%

“…Several methodologies were proposed over the years for detecting and identifying the harmonic source location and contributions [6]. However, the conventional techniques that are currently used for power quality monitoring are based on visual of voltage and current waveforms [7], [8]. Signal processing techniques have been widely used for automatic power quality disturbances detection and classification [9].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Diagnostic Analytics of Harmonic Source Signature Recognition by Using Periodogram

Jopri

Abdullah

Sutikno

et al. 2018

IJECE

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This paper presents a diagnostic analytics of harmonic source signature recognition of rectifier and inverter-based load in the distribution system with single-point measurement at the point of common coupling by utilizing Periodogram. Signature recognition pattern is used to distinguish the harmonic sources accurately by obtaining the distribution of harmonic and interharmonic components and the harmonic contribution changes. This is achieved by using the significant signature recognition of harmonic producing load obtained from analysing the harmonic contribution changes. Based on voltage and current signature analysis, the distribution of harmonic components can be divided into three zones. To distinguish between the harmonic producing loads, the harmonic components are observed at these zones to get the signature recognition pattern. The result demonstrate that periodogram technique accurately diagnose and distinguish the type of harmonic sources in the distribution system.

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Section: Instantaneous Total Harmonic Distortionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Diagnostic Analytics of Harmonic Source Signature Recognition by Using Periodogram

Jopri

Abdullah

Sutikno

et al. 2018

IJECE

Self Cite

View full text Add to dashboard Cite

show abstract

“…By windowing the signal at first and after that taking the Fourier transforms the time-localization can be obtained correctly. Furthermore, this will lead to the rise of short time Fourier transform, (STFT) or windowed Fourier transform [9]. The continuous-time STFT of a signal, x(t), is expressed as…”

Section: Linear Time-frequency Distributionsmentioning

confidence: 99%

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

Jopri¹,

Abdullah²,

Manap³

et al. 2017

IJECE

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This paper introduces an improved detection and classification technique of harmonic signals in power distribution using time-frequency distribution (TFD) analysis which is spectrogram. The spectrogram is an appropriate approach to signify signals in jointly time-frequency domain and known as time frequency representation (TFR). The spectral information of signals can be observed and estimated plainly from TFR due to identify the characteristics of the signals. Based on rule-based classifier and the threshold settings that referred to IEEE Standard 1159 2009, the detection and classification of harmonic signals for 100 unique signals consist of various characteristic of harmonics are carried out successfully. The accuracy of proposed method is examined by using MAPE and the result show that the technique provides high accuracy. In addition, spectrogram also gives 100 percent correct classification of harmonic signals. It is proven that the proposed method is accurate, fast and cost efficient for detecting and classifying harmonic signals in distribution system.

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“…The most widely used in signal processing is spectral analysis using Fourier Transform but due to the non-stationary nature of PQ, this technique is not effective in detecting the disturbances [10], [11]. There are some other time-frequency distribution (TFD) techniques such as Spectrogram, Wavelet transform, Gabor Transform, S-Transform and so on [12]- [16] . In this study, spectrogram with several windows will be applied for analyzing voltage variation signals by distinguishing the signals obtained in time and frequency domain.…”

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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Short-circuit switches fault analysis of voltage source inverter using spectrogram

Cited by 22 publications

References 9 publications

A Diagnostic Analytics of Harmonic Source Signature Recognition by Using Periodogram

A Diagnostic Analytics of Harmonic Source Signature Recognition by Using Periodogram

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

Spectrogram Based Window Selection for the Detection of Voltage Variation

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