Power quality signals detection using S-transform

Huda, Naili; Abdullah, Abdul Rahim; Jopri, Mohd Hatta

doi:10.1109/peoco.2013.6564609

Cited by 31 publications

(21 citation statements)

References 9 publications

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“…Results of ST which is TFR of a time series is from the combination of frequency-dependent resolution that concurrently localized the real and imaginary signals. From the Gaussian modulated cosinusoidal, time-frequency spectrum is represented from intuitive notions of cosinusoidal frequencies [12]. In Fig.…”

Section: B S-transformmentioning

confidence: 99%

“…There are several linear TFDs, i.e. short time Fourier transform (STFT), wavelet transform (WT) and S-transform (ST) [12].…”

Section: Introductionmentioning

confidence: 99%

“…Except for multiresolution strategy, ST formula is similar to STFT in its way of preserving the phase information. The window width varies with the frequency measured [12], thus, higher frequency resolution by low frequency component and good time resolution by high frequency component can be achieved through the window function [38].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Lead Acid Battery Analysis using S-Transform

Basir¹,

Abdullah²,

Selamat³

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

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This paper proposes a new signal processing technique using time-frequency distribution (TFD), namely S-transform (ST) for battery parameters estimation. Compared to other TFDs such as short time Fourier transform (STFT) and wavelet transform (WT), ST technique offers more promising results in a low frequency application analysis, especially battery. The results of the ST are the parameters of instantaneous means square voltage (V RMS (t)), instantaneous direct current voltage (V DC (t)) and instantaneous alternating current voltage (V AC (t)) extracted from the time-frequency representation (TFR). Simulation through MATLAB has been conducted using equivalent circuit model (ECM), using 12 V lead acid (LA) battery with capacities from 1.0 Ah to 10.0 Ah. For the battery model, charging/discharging signal has been used to estimate the battery parameters from the ST technique to determine battery characteristics. From the signal characteristics of battery capacity versus V AC (t) obtained, new equation is proposed based on the curve fitting tool. The advantage of this technique embraces a better capability in estimating battery parameters at low frequency component, resulting in better frequency and time resolutions compared to previous TFDs.

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Section: B S-transformmentioning

confidence: 99%

“…There are several linear TFDs, i.e. short time Fourier transform (STFT), wavelet transform (WT) and S-transform (ST) [12].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Lead Acid Battery Analysis using S-Transform

Basir¹,

Abdullah²,

Selamat³

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

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“…The power quality (PQ) term originates from the idea that the AC voltage supplied to the domestic and industrial equipment must be unpolluted sinusoidal and its frequency and magnitude as must be in the range of IEEE and IEC standards. Nowadays, one of the main issue of PQ is harmonic pollution, this is due to the use of different power electronics equipment and nonlinear loads [1], [2]. A harmonic pollution monitoring in the distribution system is vital for the study of the root causes, harmonic level and mitigation [3]- [5].…”

Section: Introductionmentioning

confidence: 99%

A Critical Review of Time-frequency Distribution Analysis for Detection and Classification of Harmonic Signal in Power Distribution System

Jopri¹,

Abdullah²,

Sutikno³

et al. 2018

IJECE

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<p>This paper presents a critical review of time-frequency distributions (TFDs) analysis for detection and classification of harmonic signal. 100 unique harmonic signals comprise of numerous characteristic are detected and classified by using spectrogram, Gabor transform and S-transform. The rulebased classifier and the threshold settings of the analysis are according to the IEEE Standard 1159 2009. The best TFD for harmonic signals detection and classification is selected through performance analysis with regards to the accuracy, computational complexity and memory size that been used during the analysis.</p>

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“…Some examples of PQD are sag, swell, interruptions, harmonic and flicker [2]. PQD may cause failure or disoperation of end-use equipment [3]. There are also hybrid PQD which consists of two PQD present simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Power Quality Disturbances Classification using Discrete Wavelet Transform and Support Vector Machine

Janahiraman¹,

Harun²

2018

IJET

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Power utility providers and power industry service providers face a significant challenge in identifying the type of Power Quality Disturbances (PQD) automatically. This paper discusses a method to classify PQD using signal decomposition, statistical analysis and machine learning. Firstly, Discrete Wavelet Transform (DWT) is applied on the generated PQD signals to decompose the signal to obtain its representation in time and frequency domain. Secondly, first and second order statistical parameters are computed on the selected sub-band of DWT. These parameters are used as features vector for the machine learning based classifier. Our database consists of 2400 generated signals of PQD, which were divided into train and test set. Another set of noise corrupted signal database was generated to evaluate the capability of the system. SVM using quadratic kernel was selected as the classifier of the Power Quality Disturbances feature vector. Comparisons were also made with other types of classifiers and other types of mother wavelet filter functions. The results show that the combination of DWT and SVM managed to classify Power Quality Disturbances with high accuracy and has a strong resistance towards noise.

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Power quality signals detection using S-transform

Cited by 31 publications

References 9 publications

Lead Acid Battery Analysis using S-Transform

Lead Acid Battery Analysis using S-Transform

A Critical Review of Time-frequency Distribution Analysis for Detection and Classification of Harmonic Signal in Power Distribution System

Power Quality Disturbances Classification using Discrete Wavelet Transform and Support Vector Machine

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