A novel adaptive scheme of discrimination between internal faults and inrush currents of transformer using mathematical morphology

Ma, Jing; Xu, Yan; Wang, Zengping; Haofang, Liu

doi:10.1109/pes.2006.1709011

Cited by 15 publications

(2 citation statements)

References 11 publications

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“…Determining their exact effect in the frequency domain is not trivial, as they are nonlinear operators, but one can easily observe a low-pass effect from Figure 3. In recent years, the mathematical morphology was applied to many areas such as power quality monitoring, 21 fault diagnosis on power transformers, 22 and broken rotor bar detection in induction motors. 23…”

Section: Feature Extraction Using Mathematical Morphology Mathematicamentioning

confidence: 99%

Fuzzy integral-based multi-sensor fusion for arc detection in the pantograph-catenary system

Aydın

Çelebi

Barmada

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part F:

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The pantograph-catenary subsystem is a fundamental component of a railway train since it provides the traction electrical power. A bad operating condition or, even worse, a failure can disrupt the railway traffic creating economic damages and, in some cases, serious accidents. Therefore, the correct operation of such subsystems should be ensured in order to have an economically efficient, reliable and safe transportation system. In this study, a new arc detection method was proposed and is based on features from the current and voltage signals collected by the pantograph. A tool named mathematical morphology is applied to voltage and current signals to emphasize the effect of the arc, before applying the fast Fourier transform to obtain the power spectrum. Afterwards, three support vector machine-based classifiers are trained separately to detect the arcs, and a fuzzy integral technique is used to synthesize the results obtained by the individual classifiers, therefore implementing a classifier fusion technique. The experimental results show that the proposed approach is effective for the detection of arcs, and the fusion of classifier has a higher detection accuracy than any individual classifier.

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Section: Feature Extraction Using Mathematical Morphology Mathematicamentioning

confidence: 99%

Fuzzy integral-based multi-sensor fusion for arc detection in the pantograph-catenary system

Aydın

Çelebi

Barmada

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part F:

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show abstract

“…Therefore there should be proper differentiation between inrush currents and fault currents. Based on the transient characteristics of the short circuit and inrush currents, the difference between them can be easily determined by using a technique known as morphological gradient technique [1]. As the inrush current and the short circuit currents are the random signals, the samples of these signals can be taken out and processed by using correlation and digital signal processor and then it is then compared with the standard signals [2].…”

Section: Introductionmentioning

confidence: 99%

Wavelet Transform Implementation to Differentiate Inrush Current and Fault Current in Power Transformer

Venkateshnaik*,

Rokdae M.

2020

IJRTE

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An electrical system is al complex network, to enhance power generation, transmission and distribution transformers play an important role. Power transformer is the heart of the electrical system. Even though electrical power is generated at lower rate in the coastal areas, it can be transmitted easily over hundreds of kilometer with the help of power transformer. So there must proper protection should given to power transformers. But in order to give the proper protection there should be proper discrimination between internal faults and inrush current. If there is no proper differentiation between faults and inrush current phenomena than the protection circuit will not trip when inrush current comes into the picture. But the protection circuit will trip at faster rate than the actual one when the normal faults or internal faults come into the picture. So there should be proper discrimination between inrush current and internal fault current so that the protection circuit will work properly. There are so many techniques available to get differentiation between inrush current and fault current. Fast Fourier transform is one of the method to differentiate between these two currents. Here we are using wavelet transform technique to get the difference between these two currents. The properties of the inrush current obtained from this technique are accurate and distinct from those obtained from the fast Fourier transform. The experimental set up is carried out in matlab simulink and results were discussed as shown below in results.

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The study of power transformer differential protection’s operation in the internal fault conditions

et al. 2021

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Special attention is paid to high-speed relay protections’ operation in transient modes due to a number of major failure events that have occurred over the past 10 years in the power system of the Russian Federation. Operation of power transformer’s differential protection in case of internal short circuit is studied in this research. False blocking of protection is possible in such mode due to saturation of current transformers. A value of blocking time may exceed the maximum permissible short-circuit disconnection time under conditions of maintaining the dynamic stability of the power system. Primary and secondary currents in transient modes are obtained by simulation of short circuits. Windings of the modeled current transformers are connected in a star to a null wire. RMS values are calculated using a mathematical model of the Fourier filter. The current transformers were checked according to the methods declared in PNST 283-2018 and GOST R 58669-2019. The analysis carried out in this work allows to estimate possibility of long-term blocking of the differential protection of a power transformer in case of internal short circuit, especially in case of significant value of time constants.

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A novel adaptive scheme of discrimination between internal faults and inrush currents of transformer using mathematical morphology

Cited by 15 publications

References 11 publications

Fuzzy integral-based multi-sensor fusion for arc detection in the pantograph-catenary system

Fuzzy integral-based multi-sensor fusion for arc detection in the pantograph-catenary system

Wavelet Transform Implementation to Differentiate Inrush Current and Fault Current in Power Transformer

The study of power transformer differential protection’s operation in the internal fault conditions

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