Power Quality Analysis Using S-Transform

Dash, Priyanka; Panigrahi, B. K.; Panda, Ganapati

doi:10.1109/mper.2002.4312414

Cited by 103 publications

(119 citation statements)

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“…Discrete S-transform (DST) in magnitude-time or magnitude-frequency plot is obtained from (3). The maximum value obtained from the DST by searching through all the rows and the columns are used to determine the signature of a given signal [11]. Since power system faults are non stationary, Stransform can be effectively applied for identification purpose.…”

Section: Discrete S-transformmentioning

confidence: 99%

Inverse S-Transform Based Decision Tree for Power System Faults Identification

2011

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In this paper a decision tree based identification of power system faults has been proposed. The key input values to the decision tree are the performance indices calculated from the maximum values of unfiltered inverse Stockwell transform (MUNISTKeywords: Decision tree, faults, identification, power system, signal processing IntroductionComplexity of the power system has become manifold due to an increase in the size and power levels of the present power system. Consequently, many studies like transient stability, power quality and instability etc. have taken a center stage in the power system analysis. The faulty operation of the power system components and absence of non ideal power system design leads to transients [1], [2]. These problems have large operational effect on the power system though the time interval of the transients is negligible. The transient stability analysis can be used for analyzing such events having time periods between seconds and few minutes. With the incorporation of a large number of sensitive and critical loads into the system as well as the inclusion of deregulation and competition in the power market, utilities are now more concerned in identifying, measuring and monitoring the transient events. Also, necessary corrective actions for their reduction and elimination have now become essential.Various types of relays have been developed to isolate the healthy circuit to be affected by the faults. However, action of the relay is same for all types of faults. This is possible if information about the type of fault is available to the system operator. However, to reduce the effect of transients exact precautionary actions are of massive importance. The information about the fault is still in need, and the utilities are also concerned about the reason behind the

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Section: Discrete S-transformmentioning

confidence: 99%

Inverse S-Transform Based Decision Tree for Power System Faults Identification

2011

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“…S-transform has been also introduced recently in (Dash et al, 2003) as a new PQ signal analysis and feature extraction tool. In (Chilukuri et al, 2004), the features of seven simulated signals have been extracted by calculating the minima and maxima of the S-transform absolute matrix.…”

Section: Features Extractionmentioning

confidence: 99%

Power quality event classification: an overview and key issues

Saxena¹,

Verma²,

Singh³

2010

Int. J. Eng. Sci. Tech

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Power quality (PQ) issue has attained considerable attention in the last decade due to large penetration of power electronics based loads and/or microprocessor based controlled loads. On one hand these devices introduce power quality problem and on other hand these mal-operate due to the induced power quality problems. PQ disturbances/events cover a broad frequency range with significantly different magnitude variations and can be non-stationary, thus, accurate techniques are required to identify and classify these events/disturbances. This paper presents a comprehensive overview of different techniques used for PQ events' classifications. Various artificial intelligent techniques which are used in PQ event classification are also discussed. Major Key issues and challenges in classifying PQ events are critically examined and outlined.

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“…During this progressive resolution energy concentration is major important factor for signal analysis [10]. Today in many of real time applications power spectral densities of signal are major requirement for signal analysis [11]. In such cases if the signal is contaminated by abrupt signal, ST suffers to provide time frequency analysis at faster rate due to lesser higher frequency localization [12] [13].…”

Section: Introductionmentioning

confidence: 99%

Time Frequency analysis of Non-Stationary signals by Differential frequency window S –Transform

Krishna¹,

Srinivas²,

Gopal³

et al. 2018

IJET

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The S transform is an extension of Short Time Fourier Transform and Wavelet transform, has a time frequency resolution which is far from ideal. A differential frequency window is proposed in this paper to enhance the time frequency energy localization. When a non stationary signal consists of abrupt amplitude variation equal to peak of Gaussian function at initial intervals of chosen guassian window, then some part of the signal amplitude will be nullified during transform projection. The major function of differential frequency window is to track all abrupt amplitude-frequency variations which exploits in non -stationary signals. A mathematical method namely Newton Raphson method is adopted for this trace. The proposed scheme is tested for ECG data in presence of noise environment and results shows that proposed algorithm produces better enhanced energy localization in comparison to the standard S -Transform, STFT, and CWT. Furthermore the above algorithm is implemented on FPGA for real time applications.

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Power Quality Analysis Using S-Transform

Cited by 103 publications

References 0 publications

Inverse S-Transform Based Decision Tree for Power System Faults Identification

Inverse S-Transform Based Decision Tree for Power System Faults Identification

Power quality event classification: an overview and key issues

Time Frequency analysis of Non-Stationary signals by Differential frequency window S –Transform

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