Performance Comparison of VSI Switches Faults Analysis Using STFT and S Transform

Manap, Mustafa; Abdullah, Abdul Rahim; Saharuddin, N. Z.; abidullah, Noor Athira; Ahmad, Naseer; Jopri, Mohd Hatta

doi:10.4028/www.scientific.net/amm.785.210

Cited by 5 publications

(5 citation statements)

References 12 publications

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“…However, this method utilizes Fourier and wavelet transform signal processing techniques, whereas many constraints of these signal processing techniques are explained in [19][20][21]. Furthermore, a diagnosis study using a short-time fourier transform (STFT) and S-transform based on a rules-based classifier is introduced in [22][23][24]. However, high technical knowledge is essential due to  ISSN: 2302-9285 distinguish the rules-based classifier threshold parameter values [25,26].…”

Section: Introductionmentioning

confidence: 99%

Linear discriminate analysis and k-nearest neighbor based diagnostic analytic of harmonic source identification

et al. 2021

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The diagnostic analytic of harmonic source is crucial research due to identify and diagnose the harmonic source in the power system. This paper presents a comparison of machine learning (ML) algorithm known as linear discriminate analysis (LDA) and k-nearest neighbor (KNN) in identifying and diagnosing the harmonic sources. Voltage and current features that estimated from time-frequency representation (TFR) of S-transform analysis are used as the input for ML. Several unique cases of harmonic source location are considered, whereas harmonic voltage (HV) and harmonic current (HC) source type-load are used in the diagnosing process. To identify the best ML, each ML algorithm is executed 10 times due to prevent any overfitting result and the performance criteria are measured consist of the accuracy, precision, geometric mean, specificity, sensitivity, and F measure are calculated.

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Section: Introductionmentioning

confidence: 99%

Linear discriminate analysis and k-nearest neighbor based diagnostic analytic of harmonic source identification

et al. 2021

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show abstract

“…The research literatures on the diagnosis technique utilizing the stochastic methodology and indexing of power quality been clarified in [19,20]. Be that as it may, this strategy uses Fourier and wavelet transform of signal processing methods, while numerous limitations of these methods are clarified in [21,22]. Moreover, a diagnosis analysis utilizing a shorttime Fourier transform (STFT) and S-transform dependent on a rule-based classifier is presented in [23,24].…”

Section: Introductionmentioning

confidence: 99%

Naïve Bayes and linear discriminate analysis based diagnostic analytic of harmonic source identification

Jopri

Ghani

Abdullah

et al. 2020

IJEECS

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The diagnostic analytic type of harmonic source is a vital research due to diagnose and identify type of harmonic source that exist in the power system. This paper presents a comparison of machine learning (ML) algorithm namely as the Naïve Bayes (NB) and linear discriminate analysis (LDA) in identifying and diagnosing the harmonic sources. The MLs inputs are the voltage and current feature sets that estimated from the time-frequency representation (TFR) of S-transform analysis. Four specific cases of harmonic source location are considered in this research, whereas harmonic voltage (HV) and harmonic current (HC) source type-load are used in the diagnosing process. The sufficiency of the proposed methodology is tested and verified on the IEEE 4-bust test feeder, and to prevent overfitting, the K-fold cross-validation technique is implemented for performance evaluation. To identify the best ML, the performance measurement consist of the accuracy, precision, geometric mean, F-measure, sensitivity, and specificity are conducted.

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“…However, this method utilize Fourier and wavelet-transform signal processing techniques, whereas a lot of limitation of this signal processing techniques are explained in [13,14]. Furthermore, a diagnosis study using short-time fourier transform (STFT) and S-transform based on rules-based classifier is introduced in [15][16][17]. Yet, high technical experience is required due to determine the rules-based classifier threshold parameter values [18].…”

Section: Introductionmentioning

confidence: 99%

Support-vector machine and naïve bayes based diagnostic analytic of harmonic source identification

Jopri¹,

Abdullah²,

Too³

et al. 2020

IJEECS

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A harmonic source diagnostic analytic is a vital to identify the location and type of harmonic source in the power system. This paper introduces a comparison of machine learning (ML) algorithm which are support vector machine (SVM) and Naïve Bayes (NB). Voltage and current features are used as the input for ML are extracted from time-frequency representation (TFR) of S-transform. Several unique cases of harmonic source location are considered, whereas harmonic voltage and harmonic current source type-load are used in the diagnosing process. To identify the best ML, the performance measurement of the propose method including accuracy, specificity, sensitivity, and F-measure are calculated. The adequacy of the proposed methodology is tested and verified on IEEE 4-bust test feeder and each ML algorithm is executed for 10 times due to different partitions and to prevent any overfitting result.

show abstract

Performance Comparison of VSI Switches Faults Analysis Using STFT and S Transform

Cited by 5 publications

References 12 publications

Linear discriminate analysis and k-nearest neighbor based diagnostic analytic of harmonic source identification

Linear discriminate analysis and k-nearest neighbor based diagnostic analytic of harmonic source identification

Naïve Bayes and linear discriminate analysis based diagnostic analytic of harmonic source identification

Support-vector machine and naïve bayes based diagnostic analytic of harmonic source identification

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