Wavelet network-based classification of transients using dominant frequency signature

Chatterjee, Saibal; Chakravorti, S.; Roy, C.K.; Dey, Debangshu

doi:10.1016/j.epsr.2006.12.003

Cited by 15 publications

(6 citation statements)

References 28 publications

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“…The resulting coefficients form a finely meshed grid of scales with small time steps, which can be useful for detailed analysis of transient signals. To reduce the resulting feature space of coefficients, calculation of a dominant frequency signature has been proposed based on the maximum coefficient value at each instant [6].…”

Section: B Wavelet Transformsmentioning

confidence: 99%

Specification of New Test Waveforms for Static Electricity Meters

Ritzmann¹,

Wright²

2021

Preprint

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Section: B Wavelet Transformsmentioning

confidence: 99%

Specification of New Test Waveforms for Static Electricity Meters

Ritzmann¹,

Wright²

2021

Preprint

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“…These disturbances can be monitored as in [25] and classified on the basis of time-variant statistical characteristics of the voltage and current waveforms as in [26][27][28] and they could be sinusoidal or non-sinusoidal as in [14]. For only non-stationary PQ events, dominating frequency components have been used as features for recognition of events in [29]. Since PQ disturbances can lead to poor power quality which may have negative impact on the economic operation of the electric power system, therefore evaluating the electric power quality becomes very essential for both utilities and consumers especially when moving towards smart grid.…”

Section: Power Qualitymentioning

confidence: 99%

Classification of power quality events – A review

Saini

Kapoor

2012

International Journal of Electrical Power & Energy Systems

189

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“…al [19] have used wavelet networking for power quality detection of power system disturbance. Saibal [20] used wavelet networking for classification of transient in distribution system. Ramos et.…”

Section: Data Mining Applicationsmentioning

confidence: 99%

Data Mining Technology and its Applications to Power Systems

Mishra¹,

Saxena²

2015

IJCA

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With increasingly environmental constraints the modern Power and Energy Systems are experiencing huge transformations in many ways. The quantum of data in power systems is growing rapidly due to large database used by power systems engineers for various operations. From the power generation plants the electrical energy is transmitted and distributed to end users. Frequent failure of various equipments and the systems has made it impossible to maintain the continuity of supply. Sometimes these failures are beyond the control of the power system operator. The operation and planning of power systems provide a large amount of data and it is difficult to extract the useful information from this large database that is continuously used by operators. Data mining is a process of extracting interesting and previously unknown knowledge from a set of data. The data mining techniques help power systems planner/operator to have smooth system planning, operation and are useful for extracting useful information from the existing data banks. The paper describes the data mining technology and its applications that would be useful in power systems.

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Wavelet network-based classification of transients using dominant frequency signature

Cited by 15 publications

References 28 publications

Specification of New Test Waveforms for Static Electricity Meters

Specification of New Test Waveforms for Static Electricity Meters

Classification of power quality events – A review

Data Mining Technology and its Applications to Power Systems

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