Experimental Investigations on Multi-end Fault Location System based on Current Traveling Waves

8/13/2019 Experimental Investigations on Multi-end Fault Location System based on Current Traveling Waves

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Experimental Investigations on Multi-end FaultLocation System based on Current Traveling Waves

A. Elhaffar, student member IEEE,N. I. Elkalashy, student member IEEE and M. Lehtonen.

Abstract Traveling Waves Recorders (TWR) are used toaccurately find the location of different faults in transmissionnetworks. These recorders are installed at few substation buseswhere current traveling waves can be extracted. The recordedsignals time delay of the initial wave is recoded at each TWR. Inthis paper, the minimum travel time of the traveling wave hasbeen calculated considering Dijkstra algorithm to select thenearest TWR to the faulted line. The Wavelet Transform is usedto find the highest spectral energy of the frequency band of thetraveling wave signals. Thus, the Wavelet Transform enhancesthe traveling wave fault location. The current transformers (CT)are modeled and experimentally verified to represent thetraveling wave interaction with the CT. The secondary wiringfrom the CT secondary winding to TWR has also some effect onthe measured traveling wave signal which motivates practical

issues associated with measuring the arrival times. Correctionfactors are derived to monitor high frequency current travelingwave signals. Validation of fault location is examined byATP/EMTP simulations for typical 400 kV power system faults.

Keywords: Traveling waves, fault location, multi end method,

single phase to ground fault, modal analysis, wavelet transform.

I. INTRODUCTION

ccuratly locating faults on high voltage transmission

systems is very important for utilities to allow quick

maintenance action of the repair crew. Fault location systems

have been traditionally relied on the measurement of power

frequency components. However, traveling wave faultlocation shows an increasing interest to researchers and

utilities due to its accuracy [1]-[7].

As the number of traveling wave recorders (TWR) are

usually less than the number of buses, efficient methods are

needed to find the fault from only the existing recording units

[8]. Recently, traveling waves and the wavelet transform of

the current transient are used to extract initial arrival times of

fault initiated waves reflected from the fault point[9]-[11].

In [8], a method was developed to estimate the fault area

using several recorders scattered throughout the system by

comparing a fault signature record with calculated fault

signatures. This method is used in this presented paper

considering few recording units installed at few monitored

substations in the power system. The fault location is

determined by accurately time-tagging the arrival of the

traveling wave at these monitored substations and comparing

the time difference to the total propagation time of the lines.

The time reference signal can be attained using satellite from

A. Elhaffar, N. I. Elkalashy and M. Lehtonen are with Power System & High Voltage Engineering,

Department of Electrical and Communication Engineering, Helsinki University of Technology (TKK),

P.O.Box 3000, FIN-02015 HUT, Finland, Tel. +358 9 4515484, Fax +358 9 460224, Finland.(email:

[email protected], [email protected], [email protected]).

the Global Positioning System (GPS). Fault distance

calculation is, therefore, carried out using double end method

and pre-selected two TWR signals.

Conventional CTs are used to monitor the traveling wave

transients. In this paper, effects of CT and split-core inductive

couplers filtering current transformers on the recorded signals

and therefore on the traveling wave fault locator performance

are investigated. This investigation is carried out using the

total transfer function of the whole fault locator measuring

system. Then, a practical solution for fault location using few

traveling wave current signals is studied taking into account

CT and associated secondary system model. The signals are

analyzed using wavelet transform. A method of selecting anoptimum mother wavelet and an optimum level according to

signals energy content is presented. The minimum travel time

of the current traveling wave signal traveling to the nearest

TWR has been calculated using Dijkstra algorithm [12].

Simulation results indicate good correlation between the

estimated and actual fault locations for the studied network.

II. CURRENT TRANSFORMER MODELING

In this section, the CT modeling is presented. The

modeling is carried out with the aid of experimental results

measured at Helsinki University of Technology (TKK). The

CT used in this measurement was a hair-pin type 110 kV,

200/5 CT with three secondary windings as shown in Fig. 1.The CT modeling is divided into two parts; the low and high

frequency models. The low frequency model parameters of the

CT are calculated from open and short circuit tests of the CT

at power frequency. The inductance of secondary windings

dominates the impedance at a low frequency and the stray

capacitances have negligible effects. Open circuit tests were

only measured from the secondary side while the short circuit

tests were measured from both primary and secondary sides.

Towards high frequency transfer function modeling of the

CT, an impulse current signal was injected in the primary

winding and the output secondary current was measured using

a low inductance shunt resistor (0.4905 ). The signals aredigitally recorded using a digitizer. The digital recorded

signals are transformed using Fourier Transform to obtain the

frequency spectra from which the desired transfer functions

are calculated. The shunt capacitances representing the

capacitances of the transformer windings can no longer be

ignored. These parasitic secondary capacitances have a great

influence on the output current. Open and short circuit

impulse tests are also carried out for the abovementioned CT

to find high frequency model parameters [13]. Fig. 2 shows

secondary winding 1 spectrum during the open circuit test.

A


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Figure 1 CT High Frequency equivalent circuit.

103

104

105

1060

2

4

6

8

x 104 S1 Winding Impedance

Zs

1[Ohms]

Frequnecy [ Hz]

103

104

105

106

-100

0

100

S1 Winding Impedance Angle

Phi[deg]

Frequnecy [ Hz] Fig. 2 Open circuit test from secondary winding 1.

The frequency response measurements were carried out

using standard 1.2/50 s low impulse voltage signals for opencircuit tests and non-standard 2.2/6 s current signals for shortcircuit test. The first resonance frequency is found for each

secondary winding from its corresponding spectrum. Then,

the secondary winding shunt capacitances Cs1, Cs2and Cs3can

be calculated. Consequently, from these tests, a frequency

dependent correction factors can be obtained based on the

calculated parameters of the CT as in:

n

n

n

nn

Zc

Zs

Zm

ZsCFs ++= 1 (1)

whereZcnis the capacitance of secondary winding and n is the

secondary winding number 1, 2 and 3.

Split-core current transformers are connected directly to thesecondary of the relaying CTs for isolating high frequency

signals. They are also tested and their transfer function

calculated using the system identification tool box of

MATLAB[14].Obviously the Box-Jenkins model gives good

results as shown in Fig. 3. From impulse current

measurements, the overall transfer function was plotted

against the frequency. Therefore, the resonance frequency

values for the CT, wiring and coupler was located and

recorded. The overall transfer function bode plot is shown in

Fig. 4. Considering the interaction between the ATP/EMTP

simulated network and the transient analysis control system

(TACS) function, the transfer function of the measuring

system of the CT, secondary wiring and split-core inductive

couplers are inserted in the ATP/EMTP transmission line 110

kV model in addition to the secondary wiring cable from the

CT to the TWR as shown in Fig. 5 [15], [16].

0 1 2 3 4 5 6 7 8

x 10-6

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2 x 10

-3

y1

Coupler Measured and Simulated Models Output

Measured Output Current

Box-Jenkins Model Fit: 71.22%

PEM Fit: 51.7%

Fig. 3 Inductive coupler model comparison

10

-5

10

0

10

510

-8

10-6

10-4

Amplitude

Overall Transfer function of CT and coupler

10-5

100

105

-400

-200

0

200

Phase(degrees)

Frequency (rad/s) Fig. 4 Overall transfer function of both CT and inductive coupler

110kV 110kV

Wave-imp

B1

Wave-imp

G(s)

G(s)

G(s)

Transfer function: CT and Line Couplers

Fig. 5 Simulated 110 kV transmission Line with CT Transfer Function and

secondary Wiring


3/6

III. SIGNAL PROCESSING

A. Wavelet Analysis

The main problems that came across by using current

traveling waves are that they have no direction and are noisy.

Recently, it was shown that the wavelet transform of the

modal components of the fault initiated traveling waves is

used to estimate the location of the fault [10].

The wavelet transforms of the modal current componentsare obtained yielding the corresponding wavelet coefficients

in selected levels. The potential benefits of applying digital

wavelet transform (DWT) for analyzing traveling waves

transients have been well-recognized because of its inherent

time and frequency localization characteristics [17]. DWT

offers an alternative to windowed (Short-Time) Fourier

(STFT) analysis where a uniform window is used for all

frequencies. This problem has been solved in DWT by

employing short windows at high frequencies and long

windows at low frequencies. The windowing is performed in

practice by scaling and translating a Mother Wavelet (t).

The continuous wavelet transform (CWT) is initiallyshown in equation (8).

+

= dttx

a

bt

abaCWT )().(

1),(

(2)

The mother wavelet )(t is a band pass filter which should

satisfy some conditions: It should be short and satisfies the

relation:

+

= 0)( dtt (3)

a1 is a coefficient used in order to have the same "energy"

in each analyzing wavelet.

In practice discrete wavelet transform DWT is used

instead of CWT. The two parameters aand bare discretized.

For a signal x(t), its DWT with respect to a discrete mother

wavelet (t)can be represented by

[ ] [ ] =

k a

bnkx

anmxDWT )(

1,, (4)

The b gives the time position of the wavelet, while the

parameter acontrols its frequency.

DWT of a sampled signal is calculated by choosing a= a0m,

and b= nb0a0m. Toward better efficiency of computations, a0

and b0 are set to 2 and 1 respectively resulting in a binarydilation of 2

mand dyadic translation of 2

mn[18]. The dyadic

DWT analysis is the most popular implementation of DWT,

which is used in most applications. The discrete basis function

of the dyadic DWT is given by:

020

0

( , )

m m

m

x a nn m a

a

=

(5)

The wavelet coefficients that represent the fault traveling

wave signal are:

0 0 1 2[ ........ 1]TWC c d d d dj= (6)

wherejrepresents the total number of resolution levels.

For carrying out wavelet analysis, first, a suitable mother

wavelet must be chosen, which plays a significant role in a

fault-location scheme. In this work, an optimum mother

wavelet is selected based on the highest energy content and

based on its ability to reconstruct the original signal with

minimum errors [19],[20].

B. Optimum Wavelet FunctionDifferent wavelets can be used to decompose the fault

transient signal and extract the feature vector. A comparison

between three different groups of orthogonal wavelets was

carried out. These wavelets used in the comparison were: db2,

db4, db6, db8, db10, db20, db30, db40, sym4, sym8, sym10,

coi1..5, bior2.4, bior3.3, and bior3.7. The criterion for

selecting the proper wavelet, to be used in feature extraction,

is based on its ability to reconstruct the original signal with

minimum errors. The norm of the error is used as a

discriminator. For selecting the proper details level, the energy

content at different resolution levels is calculated and the

highest one is selected. Fig. 6 shows the details energy at sixlevels for a fault at 112.2 km from OL substation for a pre-

selected mother wavelet.

The problem now arises from which level of the sub-band

components should be used in DWT analysis procedure. The

main reason is at fault location, there is one level at which the

calculation of the fault distance is more accurate than other

levels. If the used scaling function and the wavelets form an

orthonormal basis, the Parseval's theorem relates the energy of

the fault traveling wave signal to the energy in each of the

expansion components and their wavelet coefficients.

Therefore, the energy of the fault traveling wave signal will be

partitioned at different resolution levels according to the

transmission line transients [21].

22 2

0

( ) ( ) ( )jk j k

x t dt c k d k

= = =

= + (7)where, djis thejlevel wavelet coefficient and cjis thej level

scaling coefficient.

With the energy in the expansion domain is partitioned in

time by kand in scale by j. This means that the energy of the

distorted signal can be partitioned in terms of the expansion

coefficients [21]. The above method was implemented in a

MATLAB program using the current details of the fault line

signal as the absolute sum value of the current signal details

squared computed in a discrete form, as in:2

1

( ) ( )

N

j

k

DE k d k

=

= (8)whereDE(k)is the details energy detector in discrete samples.

j is considered as the scale number, and k is the index of

wavelet details coefficients. Fig. 6 indicates that high-

frequency current was more powerful in the fault signal at the

fist level, and the signal energy was distributed over the whole

analyzed resolution levels with different magnitude [19].


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1 2 3 4 5 60

1

2

3

4

5

6x 10

6

Levels

DetailsEnergy

Fig. 6. OL current traveling wave wavelets transform details energy

0 20 40 60 80 100 120 140 160-2

-1.5

-1

-0.5

0

0.5

1

Fault Distance [km]

Error

%

Percentage Error

Fig. 7 Error analysis of single end method

IV. FAULT LOCATION PROBLEM FORMULATION

Traveling wave methods for transmission lines fault

location have been reported since a long time. Subsequentdevelopments had employed high-speed digital recording

technology to capture the traveling wave transients created by

faults. It is well known that when a fault occurs in overhead

transmission lines systems, the abrupt changes in voltage and

current at the point of the fault generate high frequency

electromagnetic travelling waves which propagate along the

transmission line in both directions away from the fault point.

If the times of arrival of the travelling waves in the two ends

of the transmission line can be measured precisely, the fault

location then can be determined by comparing the difference

between these two arrival times of the first initial traveling

wave signal.By modal transform, a three-phase system can be

represented by an earth mode and two aerial modes. Each

mode has a particular velocity and characteristic impedance.

In this paper, the aerial mode 1 signal is used in the fault

distance estimation. The modal components are obtained by:

),(),(

),(),(

txITtxI

txUTtxU

mi

mv

=

=

(9)

(10)

where Tv and Ti are voltage and current transformation

matrices, U and I are phase voltage and current components,

Um and Im are modal voltage and current components

respectively. for transposed lines to transform the transient

current signals Ia, Iband Icinto their modal components using

Clarks transformation as follows [22],[23]:

1

2

3

1 1 11

2 1 13

0 3 3

a

b

c

I I

I I

II

=

(11)

where I1 is the ground mode current, I2 and I3are known asaerial mode current components for transposed lines.

In single-ended fault location method, the signals reflected

from the fault points as well as from the remote end busbars

imposes more difficulties in finding the arrival times of the

recorded signals especially for faults at the second half of a

transmission line. The time difference between the ground and

Ariel mode wavelet coefficients (dt0) is compared with that of

the time difference produced by a fault located at the middle

of the line (dtm) [11]. If dt0dtm, then the fault will be locatedin the first half of the line and the fault distance x will be:

2

dtv

x

= (12)where x is the distance to the fault, td=(t2 t1)

which is the

time difference between two consecutive peaks of the

optimum wavelet transform detail coefficients of the recorded

currents at the terminal bus and v is the wave propagation

velocity of the aerial mode.

For a fault between the midpoint and remote end bus, some

reflections from remote end will arrived at the sending station

before the first reflection from the fault point. The time

difference between the ground and arial mode wavelet

coefficients is greater than that of the time difference

produced by a fault located at the middle of the line (dt0> dtm).Then the fault will be located in the second half of the line and

the fault distance x will be calculated using:

2 1x - (t -t )2

vL= (13)

where L is the line length and t2-t1 is the time difference

between two consecutive peaks of wavelet coefficients of the

aerial mode current signal. These signals are processed using

MATLAB [18]. Current signal was acquired at a sampling

rate of 1.25 MHz and so frequencies up to 625 kHz were

considered and the faults are simulated on the OL-KA line

using ATP/EMTP program.

In single end method, usually it is difficult to find the exactfault location if the fault inception angle or the fault resistance

changes. The auto-correlation of the details for each level

gives good fault distance estimation [24]. The fault location

percentage error using autocorrelation of the wavelet details

has shown good results as shown in Fig 7. Due to the

difficulties in finding the second peak signal, single end

traveling wave fault location method has high errors when

applied to meshed networks. The reason is the multipath

reflections from different discontinuities. Utilities prefer

multi-end fault location with few recording units.


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V. MULTI END MEHOD

Fig. 8 illustrates the single line diagram of a 400 kV

transmission network simulated using ATP/EMTP [15], in

which the processing is created by preprocessor program

ATPDraw [16]. A small part of 400kV FinGrid network has

been simulated and a fault scenario similar to that disturbances

occurs on 29.6.2002 13:12:54 (GMT) between OL-KA

substations at a distance 112.2km from OL substation. The

TWR recorders that recorded the fault signals are YL, AJ, ES,OL recorders. The minimum path for the traveling wave has

been calculated from Bus OL though OL-KA line to BUS AJ.

The fault is then calculated using two end method using the

OL and AJ recordings. Traveling wave velocities can be

determined based on the type and configuration of lines using

line/cable constant (LCC) program of ATP/EMTP[15].

The power lines studied are part of the 400 kV Finnish

EHV transmission system. The relevant lines and location of

the current transducers are shown in Fig. 8. This system has

been modeled using ATP/EMTP with measurements at three

substations: OL, AJ and ES.

If the times of arrival of the traveling waves in the chosentwo TWR units can be measured precisely, the fault location

then can be determined by comparing the difference between

these two arrival times. However, two main aspects which

affect the accuracy directly and significantly need to be

considered. One is the data synchronized sampling and the

other is arrival time detection. The former can be obtained

easily by using the Global Positioning System (GPS) which

can provide time synchronization up to maximum 1 s

accuracy over a wide area; the latter can be fulfilled by using

wavelet analysis which has already been successfully applied

in various fields in electrical engineering [19].

A MatLab program has been developed to estimate the

fault location from the first peak arrivals at three TWR units.

The minimum path for the traveling wave between the closest

two TWR units to the fault has been calculated using Dijkstra

Algorithm [12]. The maximum value of the first recorded

signals are selected as the two TWR candidates for fault

location Then, the fault distance is calculated by the double-

end method using the chosen TWR signals. For example: If a

single earth fault occurs at OL-KA line, and the best two

TWR candidates are OL and AJ recorders, the fault location

can be calculated as follows:

[OL-AJ] OL AJ

OL

line lengths + (T1 -T1 ).FL =

2

v

(14)

VI. RESULTS

The proposed approach has been tested under different system

circuit configurations, under different system and fault

conditions. The minimum path for the traveling wave has been

calculated using Dijkstra Algorithm. The fault is then

calculated using double end method using the best two fault

traveling wave signals as shown in Fig 9.

AJ

UL

OLKA

ES

RA

TM

TWR

TWR

TWR

HYYL

TWR

HU

KR

Fig. 8. Test transmission system single line diagram.

0 0.5 1 1.5 2

x 10-3

-300

-200

-100

0

100

200

300

Time [%mu s]

OptimumD

etailsCurrentLevel[A]

AJ Recoder Signal

OL Recoder Signal

Fig. 9. The optimum details level of two TWR at AJ and OL buses.

TABLEI SIMULATION RESULTS FOR ASINGLE-PHASE EARTH FAULTS

Actual Location from OLFaulted

Line % km

Estimated Location

from OL

Error %

OL-KA 20 % 32.6 32.6540 -0.1656

OL-KA 50 % 81.5 81.6586 -0.1946

OL-KA 80 % 130.4 130.4298 -0.0229

UL-ES 20 % 97.2 96.9032 0.3053

UL-ES 50 % 172.5 172.6376 -0.1096

UL-ES 80 % 50.2 49.5104 -0.2783

This method is sensitive to the travelling wave propagation

velocity which can be calculated using the LCC program.

Using a ground resistivity of 2300 .m and the data of table Ifor a horizontal transmission line configuration, the

propagation speed was found to be 29177.4 km/s. At least,

two recordings are needed for an accurate fault location in a

meshed network. Simulation studies show that for earth faults,

errors are symmetric along the transmission line except that

when fault inception angle is too small or fault location is tooclose to the line terminals.

VII. CONCLUSIONS

The experimental results reveal that CTs can be used for

monitoring high frequency current signals over a useful range

suitable for traveling wave based fault locators.

This paper presents a fault location procedure for multi end

transmission line network using current transient signals from.

For single end method, the wavelet transform of the current


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transient is used to extract arrival times of fault traveling

waves reflected from the fault point. Simulation results

indicate good correlation between the estimated and actual

fault locations for the studied transmission line. The maximum

of the squared value of auto-correlation function of optimum

level detail of the wavelet coefficients enhances the fault

location accuracy. A detailed investigation of the capabilities

of the DWT to identify, detect and localize signal disturbances

expressing power system transients and fault conditions. Aproposed DWT implementation algorithm using an optimum

mother wavelet has been introduced, examined, and validated.

The investigation results show that the DWT can detect the

band of traveling wave signals and localize them in time.

For multi end method, the minimum path for the traveling

wave has been calculated; it will be automatically done by

Dijkstra Algorithm. The fault is then calculated using two end

method using the appropriate fault recordings from the nearest

TWR recorders.

VIII. REFERENCES

[1] T. W. Stringfeild, D.J. Mahart and R.F.Stevens, Fault Location

Methods for Overhead Lines, AIEE Transactions, pp. 157-160, August

1957.

[2] F. S. Caralho and S. Carneiro,Detection of Fault Induced Transients in

E.H.V. Transmission Lines for the Development of a Fault Locator,

International Conference on Power Systems Transients- IPST 2003.

[3] Y. G. Paithankar and M. T. Sant, A new algorithm for relaying and

fault location based on auto-correlation of travelling waves ,

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Pages 179-185.

[4] Rajendra S, McLaren PG. Traveling-wave techniques applied to

protection of teed circuits: Principle of traveling wave techniques ,

IEEE Trans PAS, 1985, 104, Page(s): 3544-3550.

[5] Rajendra S, McLaren PG. Traveling wave techniques applied to the

protection of teed circuits: Multi phase/multi circuit system. IEEE Trans

PAS 1985; 104, Page(s): 3551-3557.[6] Christopoulos C, Thomas D, Wright A. Scheme, based on travelling-

waves, for the protection of major transmission lines. IEEE Proc C 1988,

pp.63-73.

[7] Aurangzeb, M.; Crossley, P.A.; Gale, P.; Fault location using the high

frequency travelling waves measured at a single location on a

transmission line , Developments in Power System Protection, 2001,

Seventh International Conference on (IEE), 9-12 April 2001

Page(s): 403 406.

[8] Z. Galijasevic and A. Abur,'Fault Area Estimation via Intelligent

Processing of Fault-Induced Transients', IEEE TRANSACTIONS ON

POWER SYSTEMS, VOL. 18, NO. 4, NOVEMBER 2003, pp. 1241-

1247.

[9] Robertson, D. C.; Camps, O. I.; Mayer, J. S. & Gish, W. B.: Wavelets

and electromagnetic power system transients, IEEE Transactions on

Power Delivery, Volume: 11 Issue: 2 , April 1996, Page(s): 1050 -1058.[10] A. Abur and F. H. Magnago, Fault location using wavelets , IEEE

Trans Power Delivery 1998; 13, Page(s): 1475-1480.

[11] A. Abur and F. H. Magnago, Use of time delays between modal

components in wavelet based fault location , International Journal of

Electrical Power & Energy Systems, Volume 22, Issue 6, August 2000,

Pages: 397-403.

[12] E. W.Dijkstra, A note on two problems in connection with graphs,

Numer. Math., vol. 1, pp. 269-271, 1959

[13] D. A. Douglass, Current transformer accuracy with asymmetric and

high frequency fault current, IEEE Trans-PAS, Vol. 100, No. 3, March

1981.

[14] L.Ljung, System Identification Toolbox users Guide for use with

Matlab.

[15] Alternative Transient Program, RuleBook, 1987.

[16] Prikler, L. and Hoildalen, H. ATPDraw users' manual, SINTEF Energy

Research AS, Norway, TR F5680, ISBN 82-594-2344-8, August 2002

[17] Daubechies I. Ten lectures on wavelets , SIAM; 1992.

[18] M. Misiti, Y. Misiti, G. Oppenheim and J. Poggi, Wavelet toolbox user's

guide. Version 2. , The math works.

[19] A. M. Gaouda, M. M. A. Salama, M. R. Sultan, and A. Y. Chikhani,

'Application of Multiresolution Signal Decomposition for Monitoring

Short-Duration Variations in Distribution Systems', IEEE Transactions

on Power Delivery, Vol. 15, No. 2, April 2000,pp. 478-485.

[20]N. Elkalashy et al, 'Impact of High Resistance Arcing FaultCharacteristics on Behavior of Dwt-Based Detection in MV Networks',

Int'l conf. on Electrical and Control Technologies 3-4 May 2007,

Kaunas, Lithuania.

[21] C. Sidney Burrus, Ramesh A. Gopinath and Haitao Guo.Introduction to

Wavelets and Wavelet Transform, Prentice Hall, New Jersey (1997).

[22] L. M. Wedepohl, Application of matrix methods to the solution of

travelling wave phenomena in polyphase systems, Proc. IEE, vol. 110,

no. 12, pp. 2200-2212, Dec. 1963.

[23] E. Clarke, Circuit Analysis of AC Power Systems, Symmetrical and

Related Components, Wiley, NewYork, 1943.

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Transform in Travelling Wave Protection" International Journal of

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IX. BIOGRAPHIESAbdelsalam Elhaffar (S03) He received the

B.Sc. (1989) and M.Sc. degrees in electrical

engineering (1999) from Garyounis University,

Libya. His worked as a protection engineer,

General Electricity Company of Libya, and as a

assistant lecturer at Engineering faculty, Gar-

Younis university. Currently, he is working

towards the Ph.D. degree at Power Systems and

High Voltage Engineering, Helsinki University of

Technology (TKK), Finland. His research interests

are: transmission line fault location, power system protection studies. E-mail:

[email protected])

Nagy I. Elkalashy (S06) was born in Quesna,

Egypt on August 4, 1974. He received the B.Sc.(with first class honors) and M.Sc degrees from the

Electrical Engineering Department, Faculty of

Engineering, Shebin El-Kom, Menoufiya University

in 1997 and 2002, respectively. Currently, he is

working towards the Ph.D. degree at Power Systems

and High Voltage Engineering, Helsinki University

of Technology (TKK), Finland under joint

supervision with Menoufiya University. His research

interests are high impedance fault detection, power system transient studies

including AI, EMTP simulation, and switchgear. (E-mail:

[email protected]).

Matti Lehtonen (1959) was with VTT

Energy, Espoo, Finland from 1987 to 2003, and

since 1999 has been a professor at the HelsinkiUniversity of Technology, where he is now head

of Power Systems and High Voltage Engineering.

He received both his Masters and Licentiate

degrees in Electrical Engineering from Helsinki

University of Technology in 1984 and 1989

respectively and the Doctor of Technology

degree from Tampere University of Technology

in 1992. His main activities include power system

planning, earth fault problems, harmonic related

issues and applications of information technology in distribution systems and

distribution energy management. (Helsinki University of Technology, Power

Systems and High Voltage Engineering, e-mail: [email protected])

Experimental Investigations on Multi-end Fault Location System based on Current Traveling Waves

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