The Discrimination of Inrush Current from Internal Fault of Power Transformer based on EMD

doi:10.4236/epe.2013.54B270

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Energy and Power Engineering, 2013, 5, 1425-1428

doi:10.4236/epe.2013.54B270 Published Online July 2013 (http://www.scirp.org/journal/epe)

The Discrimination of Inrush Current from Internal

Fault of Power Transformer based on EMD

Fanyuan Zeng, Qianjin Liu, Chao Shi

School of Electric Power, South China University of Technology,Guangzhou, China

Email: [email protected]

Received March, 2013

ABSTRACT

The method is based on that the waveform of the inrush distorts seriously, while the fault current nearly keeps sinusoid.

The complicated signal can be decomposed into a finite intrinsic mode functions (IMF) by the EMD, then define and

compute the projection area on X-axis of each IMF—, the specific gravity of SIMF—

Sci

, and the maximum of

—max

. We can get a new scheme of transformer-protection based on comparing the difference between inrush and

fault current. Theoretical analysis show that the method can precisely discriminate inrush and fault current, fault clear-

ance time is about 20ms. Moreover, it is convenient to achieve and hardly be affect by not-periodic component.

Keywords: Inrush Current; Transformer Protection; HHT; EMD; IMF

1. Introduction

At present, the domestic transformer primary protection

in power system configuration mainly uses second har-

monic restraint principle and longitudinal differential

protection based on current discontinuous corner braking

principle. The long-term operating experience shows that

the differential protection can not accurately distinguish

the difference between the transformer internal faults and

external faults, so the main contradiction is still focused

on the identification of magnetizing inrush and internal

fault.

2. Empirical Mode Decomposition

Empirical Mode Decomposition (EMD) can effectively

identify magnetizing inrush and internal fault. EMD is

suitable for the analysis of non-linear, non-stationary

signal sequence with a high signal-to-noise ratio. The

center of this technology is empirical mode decomposi-

tion, which can decompose complex signals into a finite

number of intrinsic mode functions (IMF).The decom-

posed IMF component contains the local features signal

of the different time scales of the original signal. The

empirical mode decomposition method can make the

non- stationary data become smooth and get the Hilbert

transform spectrogram and the frequency of physical

significance. Compared with the short-time Fourier

transform and wavelet decomposition methods, this

method is intuitive, direct, posterior and adaptive.

Basic principles of empirical mode decomposition:

In order to obtain th e intrinsic mode fun ction s , the d ata

signal must be decomposed with EMD. So it is necessary

to introduce the definition of the basic concepts of EMD

decomposition process: IMF. This is the basis of the

master of EMD method.

The intrinsic mode function must satisfy the following

two conditions:

1) In the entire time range, the number of lo cal extreme

points and zero-crossing points of the function must be

equal to, or up to a diffe rence of one;

2) At any time, the average of the local maximum of

the envelope (upper envelope) and the local minimum of

the envelope (the envelope line) must be zero.

The first condition obviously has the similar require-

ments with traditional narrowband stationary Gaussian

signal. For the second condition, it is a new concept

which is the classic global requirements modifies local-

ized requirements, so that the instantaneous frequency is

no longer subject to the asymmetric waveform with un-

necessary fluctuations. In fact, this condition should be

“the local mean of the data is zero”. However, for non-

stationary data calculating the local mean involves the

concept of the local time scale, which is difficult to de-

fine. Therefore, in the second condition, the envelope

flocal maxima and local minimum values of the average

envelope are instead of zero, which make the wave form

of a signal locally symmetric. Huang et al study shows

that, under normal circumstances, to use this instead of

the physical significance of the instantaneous frequency

is in line with the system studied. Intrinsic mode func-

F. Y. ZENG ET AL.

1426

tions show the inherent vibration mode of data. Because

of the intrinsic mode functions defined by the ze-

ro-crossing point of each vibration cycle, there is only

one vibration mode with no other complex riding waves.

3. Decomposition Process of EMD Method

The EMD decompositio n method is based on the follo w-

ing as assumptions:

1) Data must include at least two extreme values, a

maximum value and minimum value; 2) Local time

domain characteristics of the data is uniquely deter

mined by the time scalebetweentheextreme points; 3) If

data has an inflection point instead of extreme point, the

decomposition results can be obtained by differentiating

the data once or more times and integrating the

extremum. The essence of this approach ge ts the intrinsic

fluctuations mode by the characteristic time scale of the

data, and then break down the data. This decomposition

process can be vividly called “selecting” process.

Decomposition process: Find out all the maxima of the

original data sequence ()

t and cubic saline interpolat-

tion function fitting form of th e original data on envelope;

Similarly, find out all the minimum point, and all of the

minimum point formed by cubic spline interpolation

function fitting the data under the envelope, the upper

envelope and lower envelope means recorded as 1

The original data sequence ()

t by subtracting the av-

erage envelope obtain a new data sequence .

()hxtm

(2-1)

4. Hilbert Spectra and Marginal Spectrum

Huang et al proposed a new method for analysis of a

signal systematically on the basis of the IMF and EMD,

which called Hilbert—Huang Transform (HHT). It in-

cludes EMD and Hilbert spectral analysis methods cor-

responding to EMD.

First arbitrary signal s (t) is broken down into a finite

number of IMF with EMD.

 

tctr





t

(3-1)

Then each IMF component is analyzed by Hilbert

transform, and finally get the homeopathic frequency

signal

 



Re Re

sta tea te











tdt



(3-2)

Here omitted the residual function , Re represent

the real part. We call the right side of the above formula

for the Hilbert spectrum, referred to as the Hilbert spec-

trum.



 



,Re

tdt

Ht ate





 (3-3)

Omitting the residual function since it is a con-

stant, or a monotonic function. Although







rt can be

seen as a part of a long period wave, taking into account

the long period of uncertainty, and signal the information

contained in the high-frequency component, so we

should do the omission processing. In the expansion of

each component, the amplitude and phase is time vari-

able, and the same signal s (t) of the Fourier transform

expansions is



tae







 (3-4)

This clearly shows that: Generalizing the Fourier ex-

pansion can not only increase the frequency of the signal,

but also represents a variable frequency. New ways break

the shackles of the Fourier transform.

 

Htd







t

(3-5)

5. Transformer Protection Based on EMD

5.1. Fundamental

Under condition of no-load closing of a transformer, due

to core saturation nonlinear magnetic properties, it may

produce the magnetizing inrush comparable short-circuit

current. In Figure 4-1(a), the line OABP is basic mag-

netizing curve of the transformer .The line OABP is re-

placed by a two-stage approximate magnetization of pol-

yline OC and CP, and when closing power supply volt-

age is set : sin( )

uU ta





, without considering the

transformer leakage reactance.

While transformer is no-load closing (t = 0), we can

get the magnetizing inrush approximate expression as

(4-1) show.

cos cos()

oc m

cp m

Uata

iUata

































c



 











(4-1)

In Equation (4-1),



is core flux of the transformer;



is remanence of transformer; m



is amplitude of the

steady-state flux，rated conditions for rated flux ampli-

tude .

For fault current waveform keep sinusoidal character-

istics. In this paper, the basic idea of the identification

principle is: Read a section of the differential waveform

data f, IMF can be attained by EMD for f, where we can

look for all the leading IMF. If the number of leading

IMF is 1,it is fault current; if the number is more than

one, it is magnetizing inrush.

F. Y. ZENG ET AL. 1427

(a) (b)

Figure 4.1. Top wave bump characteristics of the trans-

former.

4.2. Specific Methods and Protection Criteria

4.2.1. Sear ch IMF

The dominant IMF is broken down into component of the

IMF with a large amplitude .In order to search for the

dominant IMF easily, this article defines IMF component

i on the horizontal axis of the projected area as

follows:

cci



ci i

Sctdt (4-2)

discretizing formula (4-2):



ci i

Sck



t

(4-3)

According to formula (4-3), the way to get the domi-

nant IMF is: Calculate the various components i

c of

IMF，then , if the ci of i

have a difference with in 20%, of IMF is the

dominant IMF.



max max, 1....

SSi

max

SS c

4.2.2. Pr o t ec tion Criteria

According to this identification principle, we define the

proportion of component coefficient

cci

is:

ci n

ck r





 (4-4)

Assume the largest proportion of coefficients of IMF

is max

 

max 1

max

nci

ci n

ck r







 (4-5)

As it can be seen in formula (4-5), the value of max

changes between 0~1. When the differential current is

fault current, since it contains only one dominant IMF,

the value of max

is very large, almost above 0.9. When

the differential current is inrush current, due to the pres-

ence of two or more similar to the proportion coefficient

leading the IMF, the value of max

is 0.5. Additionally,

When the magnitude of the differential current data win-

dow is not greater than the maximum unbalanced cur-

rent，the value of max

is 0.

Therefore, we can obtain protection criterion as (4-6)

shows:

max

K (4-6)

Define

= 0.8, respectively calculate three-phase

differential current max

4.3. Experimental Schematic and Waveform

4.3.1. Transformer Inrush Current Expe riment

Because of the limited laboratory equipment it is impos-

sible to truly imitate inside the short-circuit experiments

of transformer. Because the short circuit current is too

large, and laboratory equipment can not stand the maxi-

mum short-circuit current, otherwise the short-circuit

experiments will burn lines. Therefore, the present study

apply inductive load to simulate the short circuit fault

inside transformer.

4.3.2. Experiment Resulting Waveform and

Processing

Experimental transformer no-load inrush current and

load interterm short-circuit both cases, the two sets of

waveforms, and a group of normal waveform. Since this

article transformer experiment is the presence of har-

monic components in the laboratory, during the test, so

the experimental results from the waveform is not sinu-

soidal waveform, but presents the trend of a square wave.

Transformer inrush current experimental waveform

graph is showed in Figure 4.2. Normal waveform is

showed in Figure 4.3. Inrush current waveform is

showed in Figure 4.4. Short circuit inside waveform is

showed in Figure 4.5.

Figure 4.2. Transformer inrush current experimental

waveform graph.

Figure 4.3. Normal waveform.

F. Y. ZENG ET AL.

1428

Figure 4.4. Inrush current waveform.

Figure 4.5. Short circuit inside waveform.

5. Conclusions

The processing method, fundamentally speaking, is

based on the analysis of the three-phase current funda-

mental and higher harmonics, and then determines the

algorithm of the higher harmonic content, but makes the

method different from the conventional signal due to the

role of the EMD methods of analysis. It is more con-

venient from the principles and experimental methods to

distinguish normal airdrop and fault conditions, but th ere

are also some shortcomings. The main reason is that the

EMD algorithm is still not perfect in the border problem

and envelope fitting: Firstly, there is not a suitable enve-

lope exploded function resulting in fluctuations of max

;

secondly, there is no particularly good boundary proc-

essing method.

REFERENCES

[1] T. Y. Li, Y. Zhao and N. Li, “A New Method for Power

Quality Detection Based on HHT,” Proceedings of the

Csee, Vol. 25, No. 17, 2005, pp. 52-56

[2] Z. Ke, J. Jie and T. Q. Zhang, “Distinguishing Magnetiz-

ing Inrush Based on Characteristic of Equivalent Instan-

taneous Inductance,” Power System Protection and Con-

trol, 2010, pp. 12-16

[3] P. Liu, O. P. Malik, D. S. Chen, G. S. Hope and Y. Guo,

“Improved Operation of Differential Protection of Power

Transformers for Intemal Faults,” IEEE Transactions on

Power Delivery, Vo1. 7,1992.

[4] S. H. Jiao, W. S. Liu, J. F. Liu, Z. H. Zhang and Q. X.

Yang, “A New Principle of Discrimination between

Inrrush Current and Fault Current of Transformer Based

on Wavelet,” Proceedings of the CSEE, Jul. 1999, pp.

1-5.

[5] J. A. Sykes and L. F. Morrison, “A Proposed Method of

Harmonic Restraint Differential Protection of Transform-

ers by Digital Computer,” IEEE Transactions on Power

Apparatus and Systems, Vol. PAS-91, pp. 1266-1273.

doi:10.1109/TPAS.1972.293485

[6] F. M. Cao and P. P. Su, “The Application of Wavelet

transform in Transformer’s Differential Protection,”

Electric Power, Vol. 31, No. 11, 1998, pp. 21-24.