S-transform-based intelligent system for classification of power quality disturbance signals

Lee, I.W.C.; Dash, P.K.

doi:10.1109/tie.2003.814991

Cited by 197 publications

(82 citation statements)

References 8 publications

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“…The S-transform of a time series functon h(t) can be obtained by multiplying the continuous wavelet transform with a phase factor. It is a time frequency spectral localization method, similar to Short Time Fourier transform (STFT), but with a Gaussian window whose width scales inversely and height linearly with the frequency [18][19]. The expression for the Stransform is given as,…”

Section: Concept Of S-transformmentioning

confidence: 99%

“…S-transform produces the time-frequency representation of a time domain signal. It uniquely combines a frequency-dependent resolution that simultaneously localizes the real and imaginary spectra [18][19]. S-transform overcomes the above mentioned drawbacks of FFT and DWT and is well suited for transient analysis of signals under noisy environment.…”

Section: Introductionmentioning

confidence: 99%

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S-Transform Based Time-Frequency Analysis of Leakage Current Signals of Transmission Line Insulators under Polluted Conditions

Natarajan¹,

Narayanan²

2015

Journal of Electrical Engineering and Technology

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-Flashover of power transmission line insulators due to contamination is a major threat to the reliable operation of power system. This paper deals with the analysis of leakage current characteristics of polymeric insulator using S-Transform technique in order to develop a better diagnostic tool to identify the surface condition of outdoor polymeric insulators. In this work, experiments were carried out on 11 kV silicone rubber insulator under AC voltage at different pollution levels. Moving average technique was adopted to find the trend followed by LC peak at different relative humidity conditions. S-Transform was used to find the relationship between energy and frequency content of the leakage current signal with respect to increase in pollution level over a period of time. From the S-Transform time-frequency contour analysis, point of transition to severe arcing due to increase in pollution and its thershold limit were evaluated. Reported results show that the surface condition of insulators could be easily identified from the S-Transform time-frequency analysis of leakage current signals.

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Section: Concept Of S-transformmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

S-Transform Based Time-Frequency Analysis of Leakage Current Signals of Transmission Line Insulators under Polluted Conditions

Natarajan¹,

Narayanan²

2015

Journal of Electrical Engineering and Technology

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“…The S-transform method has been applied to a wide variety of disciplines including physics, engineering, and medical imaging [e.g., Lee and Dash, 2003;Portnyagin et al, 1999;Zhu et al, 2003].…”

Section: Spectral Analysis and The S-transformmentioning

confidence: 99%

Automated identification of lava flow structures using local Fourier spectrum of digital elevation data

2007

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[1] The rheological parameters of a lava flow can be inferred by studying structural features on the solidified lava surface. We have developed a technique using the S-transform (a localized Fourier transform) to analyze and interpret digital elevation data. We examined structural features of a silicic lava flow, the Medicine Lake dacite flow in California. The S-transform accurately identified and located both repeating and single-instance structures. Several classes of structures were identified in the data, including the following: localized short wavelength features (1.3-3.6 m in wavelength) identified as large blocks and repeating medium wavelength features (5-8, 10-16, 18-26, 30-36, and 56-67 m in wavelength) identified as multiple generations of crustal folding. Fold wavelengths determined by the S-transform and a model of compressional folding associated with near surface viscosity gradients were used to evaluate the rheology of the Medicine Lake dacite flow. The similarity of the amount of strain during folding events and the wavelength ratio of successive generations of folding suggest that the flow rheology remained relatively constant during folding of the flow surface. Repeating long wavelength features (67-70-and 105-140-m wavelengths) were identified as the spacing of crease structures. Crease structures were recognized by the absence of short wavelength features at the location of their smooth flanks and by a central spike corresponding to the central trough. The limitations and applicability of this approach lie in the accuracy and resolution of the digital elevation data used.

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“…This technique uses the S-transform, which is an extension of the wavelet transform (WT). An approach for PQ disturbances detection and classification under non-stationary conditions based on S-transform and neural networks has also been presented in [33]. In [34], the authors have developed an S-transform-based probabilistic neural network (PNN) classifier for recognition of PQ disturbances.…”

Section: Introductionmentioning

confidence: 99%

Disturbances Classification Based on a Model Order Selection Method for Power Quality Monitoring

Oubrahim

Choqueuse

Benbouzid

2017

IEEE Trans. Ind. Electron.

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Abstract-In this paper, a new technique for power quality disturbances classification is proposed. It focuses on voltage sags and swells that are first pre-classified into four classes that depend on the number of non-zero symmetrical components and can contain different types of sag and swell. Using the estimated symmetrical component values, we can afterward classify the corresponding sag or swell signature. In this study, we show that the pre-classification can be reformulated as a pure model order selection problem. To solve this problem, we propose two preclassifiers based on Information Theoretical Criteria. The former yields the highest statistical performances, while the latter has a lower computation complexity. The performances of the proposed classification algorithms are evaluated using Monte Carlo simulations on synthetic signals and using real power system data obtained from the DOE/EPRI National Database of Power System Events. The achieved simulations and experimental results clearly illustrate the effectiveness of the proposed algorithms for voltage sag and swell classification.

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S-transform-based intelligent system for classification of power quality disturbance signals

Cited by 197 publications

References 8 publications

S-Transform Based Time-Frequency Analysis of Leakage Current Signals of Transmission Line Insulators under Polluted Conditions

S-Transform Based Time-Frequency Analysis of Leakage Current Signals of Transmission Line Insulators under Polluted Conditions

Automated identification of lava flow structures using local Fourier spectrum of digital elevation data

Disturbances Classification Based on a Model Order Selection Method for Power Quality Monitoring

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