Page 1 of 6
European Journal of Business &
Social Sciences
Available at https://ejbss.org/
ISSN: 2235-767X
Volume 07 Issue 04
April 2019
Available online: https://ejbss.org/ P a g e | 2012
A Novel Daubechies (DB6) Wavelet for
Identifying Internal Faults and Inrush
Currents in Power transformer
BALAJI.S, (P.G. Student)
M.Tech. Power System Engineering,
PRIST Deemed Madurai, Tamilnadu
Abstract - In this paper a novel method using to identify
small fault current results from winding turn-to-turn short
circuit in power transformer inrush current. Inrush currents in
power transformers are non-sinusoidal, high magnitude
currents generated due to flux saturation in the core during
energization. This paper describes a decision method for
discrimination between internal faults and inrush currents in
power transformers using the DAUBECHIES (DB6) wavelet
transform based feature extraction technique &
Coif4transform technique. It is shown that the features
extracted by the wavelet transform have a more distinctive
property than those extracted by Coif4transform due to the
good time and frequency localization characteristics of the
wavelet transform. As a result, by quantifying the extracted
features, the decision for distinguishing an internal fault from
an inrush current in different power transformer system can be
accurately made. The experiment simulation studies have
verified that the proposed method is more reliable and simpler,
and is suitable for different power transformer systems.
I.INTRODUCTION
As the power industry of our country rapid
development, the scale of electric power system expends
constantly ,and it is higher to demand for the quality of power
supply .How to guarantee the quality of power supply , and the
safe reliability of electronic power system operation is the
key problem of electric power
industry department .The power transformer is the very
important electrical equipment, whether it operates safely or
not , it directly relate to whether the electric power system
works continuously and stably or not While the power
transformer of high capacity is the expensive component ,
protective devices of good performance and reliable work
must be installed according to capacity and significant degree
of transformer , and all kind of possible faults and abnormal
operating state.[2] The differential protection is the key
technology for the power transformer. The power transformer
differential protection based on microcontroller collects every
phase alternative current processing it, so it has a powerful
processing ability. Its main issues are the distinguishing the
internal faults current from the magnetizing inrush current.
The protection refuses to trip, when the magnetizing inrush
current appears while the internal fault current takes place the
protection trips fast However, the second harmonic
components in the magnetizing inrush currents tend to be
relatively small in modern large power transformers because
of improvements in the core material . Moreover, the second
harmonic component may also be generated during internal
faults in the power transformer. This may occur when the
power transformer is energized with a slight internal fault . In
certain cases, the magnitude of the second harmonic in an
internal fault current can be close to or greater than that
presents in the magnetizing inrush current.
II.MAGNETIZING INRUSH AND INTERNAL
FAULT CURRENT
Under normal operation circumstance, the iron core
of transformer works in the unsaturation state, the relative
magnetic conductivity is tremendous , and the excitation
inductance of winding is also tremendous, so the excitation
current is tiny, which is not more than 2%-10% of rated
current. When the no-load transformer throws in or voltage
recovers after external fault is cleared, because of iron core ,
relative conductivity is nearly 0. Seldom parts of primary
side current, most parts are into excitation inrush. So
excitation inrush current only flow into one side of
transformer, the amplitude is probably 6-8 times of rated
current. And it causes protection equipment error operation.
Internal fault is various fault occurring inside of the
transformer tank, including the short circuit between the
phase winding, single-phase inter-turn short circuit, single
phase ground short circuits, its turn to turn short-circuit
problems accounted for a large ratio.[7] Harmful to internal
faults, because the high temperature electric arc short-circuit
currents will not only damage the winding insulation,
burning core, but will also heat insulating materials and
decomposition of transformer oil , which will produce large
amounts of gas which may cause the transformer tank
explosion.[5]
Page 2 of 6
European Journal of Business &
Social Sciences
Available at https://ejbss.org/
ISSN: 2235-767X
Volume 07 Issue 04
April 2019
Available online: https://ejbss.org/ P a g e | 2013
III.FUNDAMENTALS OF INRUSH CURRENT
It is very well known that a transformer will
experience magnetizing inrush current during energization.
Inrush current occurs in a transformer whenever the residual
flux does not match the instantaneous value of the steady- state flux which would normally be required for the
particular point on the voltage waveform at which the circuit
is closed. For the explanation of the mechanism causing
inrush current in a transformer’s primary winding when
connected to an AC voltage source, we consider , where λ
and v are the instantaneous flux linkage in a transformer core
and voltage drop across the primary winding, respectively.
We see from , the rate of change of instantaneous flux in a
transformer core is proportional to the instantaneous voltage
drop in the primary winding or on the other hand, the flux
waveform is the integral of the voltage waveform.[11]In
continuously operating transformer, these two waveforms
are shifted by 90°. But a significant difference exists
between continuous-mode operation and energization of a
transformer. During continuous operation, the flux level is at
its negative peak when voltage is at its zero point, but during
energization the flux has to start at zero. So, for a rising
voltage just started from zero, the magnetic flux will reach
approximately twice its normal peak as it integrates the area
under the voltage waveform’s first half-cycle. This amount
of flux, because of the nonlinear characteristic of the
magnetization curve, causes saturation of the transformer.
During saturation, disproportionate amounts of mmf are
needed to generate magnetic flux. This means the winding
current, which creates the mmf to cause flux in the core,
will disproportionately rise to a value easily exceeding twice
its normal peak.[9] The generation of inrush current in a
transformer. Exceeding flux from the knee point, results in
large magnetizing current that in some circumstances can be
ten times of the rated current in a transformer.
VI.MODEL SIMULATION
Fig-1 Simulation Model
To obtain the required current signals for
investigation of the merit of the proposed algorithm, a part of a
power system consisting of a power transformer and relevant
CTs with transmission lines on the both sides of the
transformer are modeled using MATLAB SIMULINK
software that is shown in Fig. . So the required current signals
for a digital differential protection can be provided. The
proposed power system consists of a 500-MVA and 400/230-
kV transformer and the distributed model for the transmission
lines is used. A long 400-kV line in parallel with the
transformer distorts the faulty current waveform. Different
cases of inrush current and fault current are simulated.
Different cases of inrush current are simulated by varying
those major parameters that influencing the characteristics of
this current. These parameters are the residual core flux of
single phase transformers , the voltage angle of switching
phase a, switching in the case of close or open secondary, the
high or low power supply connected to transformer and knee
of the core magnetic characteristic. Different cases of fault
currents are also simulated where the major factors affecting
the characteristics of the current are considered. These factors
include the type of fault and load condition. The results of
application of the proposed algorithm for different conditions
of inrush current have been summarized in 6.3.1. This section
shows the values of X and Y of different phases during shorter
than a quarter a cycle following waveform distortion due to
different inrush current. The results of application of the
proposed algorithm for internal fault conditions .Simulations
have been carried out for different faults in no-load and on- load of power system.
VII.SIMULATION RESULT
Fault Current
0 500 1000 1500 2000 2500 3000 3500 4000 4500
-6
-4
-2
0
2
4
6
8
10
12
Fig-2 Simulation Fault current
0.0005 0.0005 0.0764 0.0765 0.0766 0.0767
0.0768 0.0770 0.0773 0.0779 0.0784
0.0789 0.0795 0.0801 0.0808 0.0817 0.0829
0.0843 0.0864 0.0897 0.0968 0.1060 0.1152
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Page 3 of 6
European Journal of Business &
Social Sciences
Available at https://ejbss.org/
ISSN: 2235-767X
Volume 07 Issue 04
April 2019
Available online: https://ejbss.org/ P a g e | 2014
0.1243 0.1320 0.1356 0.1393 0.1445 0.1504
0.1613 0.1816 0.2156 0.2518 0.2878 0.3145
0.3390 0.3612 0.3907 0.4095 0.4206 0.4304
0.4370 0.4412 0.4412 0.4412 0.4412 0.4412
0.4412 0.4412 0.4412 0.4412 0.4412 0.4412
0.4412
Inrush_Current
0 1000 2000 3000 4000 5000 6000
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Fig-3 Simulation Inrush current
-0.0000 -0.0000 -0.0000 -0.0000
-0.0000 -0.0001 -0.0001 -0.0001
0.0001 -0.0001 -0.0001 -0.0001
-0.0001 -0.0000 -0.0000 0.0001 0.0001 0.0002
0.0004 0.0005 0.0006 0.0008 0.0010 0.0012
0.0013 0.0014 0.0016 0.0017 0.0020 0.0021
0.0023 0.0024 0.0025 0.0027 0.0029 0.0029
0.0029 0.0029 0.0030 0.0030 0.0030 0.0030
0.0030 0.0030 0.0030 0.0030 0.0030 0.0030
0.0031 0.0031 0.0031 0.0032 0.0032 0.0032
Normal_Current
0 1000 2000 3000 4000 5000 6000 7000 8000
-3
-2
-1
0
1
2
3
x 10-4
Fig-4 Simulation normal current
-5.2066 -5.2066 -5.2004 -5.1909
-5.1840 -5.1769 -5.1727 -5.1686
-5.1608 -5.1529 -5.1453 -5.1376
-5.1247 -5.1110 -5.1003 -5.0927
-5.0847 -5.0766 -5.0661 -5.0477
-5.0282 -5.0184 -5.0081 -4.9977
-4.9698 -4.9245 -4.8757 -4.8236
-4.7427 -4.6759 -4.6568 -4.6377
-4.6178 -4.5976 -4.5775 -4.5662
-4.5544 -4.5425 -4.5264 -4.5044
-4.4818 -4.4642 -4.4466 -4.4290
-4.4107 -4.3921 -4.3736 -4.3283
-4.2815 -4.2339 -4.2058 -4.1773
4.1481 -4.1269
VIII.WAVELET TRANSFORM ANALYSES
The wavelet analysis and wavelet transforms have
emerged recently as a powerful tool for signal processing in
different applications, in particular now for power system
applications. The transient characteristics of wavelets can be
employed to carry out accurate and effective analysis of
signals with complex frequency-time structure. Moreover, the
wavelet analysis accommodates non-uniform bandwidths,
such that the bandwidth is higher at higher frequencies,
making it possible to implement the wavelet analysis through
different levels of decimation in a filter bank [3]. The
applications of wavelet analysis in power systems include
analysis and detection of electromagnetic transients, power
quality assessment, data compression, and fault detection [17].
The wavelet analysis has been recently used in current
differential pilot relay, where current diagnosis is based on
comparing the the first level approximation with a predefined
threshold value [17]. Another application of wavelet analysis,
which is based on the distribution of energy over different
linearly divided frequencies was used to identify various types
of currents flowing through a power transformer [19]. The
data used. in simulating these applications is generated from
softwares like EMTP and not collected from real power
transformers [18]–[19]. There are different wavelet families,
and they are classified according to the characteristics of the
generated basis functions. Wavelet families are classified as
orthogonal, biorthogonal and nonorthogonal [20]. Daubieches,
Coiflet, Symlet and Meyer are examples of orthogonal wavelet
families, while B-Spline is an example of biorthogonal
wavelet families. Morlet, Gaussian and Mexican Hat are
examples of the nonorthogonal wavelet families [16], [19].
Appropriate selection of the mother wavelet for signal
representation can maximize the advantages of this technique.
Moreover, thewavelet analysis will be simplified in terms of
the required number of levels of analysis [3], [10]–[12]. One
of the new methods for optimal wavelet analysis selection is
the minimum description length (MDL) data criteria.
IX.DAUBECHIES (DB6) FUNCTION
Daubechies(4) function is continuous, orthogonal
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