An integrated technique for fault location and section identification in distribution systems

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Contents lists available at ScienceDirect

Electric Power Systems Research

j o ur na l ho mepage: www.elsev ier .com/ locate /epsr

n integrated technique for fault location and section identification inistribution systems

.S. Gazzanaa,∗, G.D. Ferreiraa, A.S. Bretasa, A.L. Bettiolb, A. Carniatob, L.F.N. Passosb,

.H. Ferreirac, J.E.M. Silvad

Department of Electrical Engineering, Federal University of Rio Grande do Sul, Osvaldo Aranha, 103, 90035-190 Porto Alegre, RS, BrazilA Vero Domino Consulting and Research, Florianópolis, SC, BrazilCETRIL Cooperative of Rural Electrification, Ibiúna, SP, BrazilEFLJC Power and Light Company, Siderópolis, SC, Brazil

r t i c l e i n f o

rticle history:eceived 15 November 2013eceived in revised form 24 January 2014ccepted 1 February 2014vailable online xxx

a b s t r a c t

This paper proposes an integrated impedance and transient-based formulation for fault location indistribution systems. The technique uses an extended apparent impedance analysis in order to esti-mate the fault distance, taking into account the unbalanced operation, intermediate loads, laterals, andtime-varying load profile of distribution systems. The fault distance information is used in calculating

eywords:pparent impedanceistribution networkault locationransient analysis

characteristic frequencies associated to each possible fault location. A transient analysis is proposed toevaluate the spectral content of the fault-generated traveling wave, thus identifying its most significantfrequency components. Based on the time–frequency correlation it is possible to eliminate the multipleestimates obtained from the impedance-based method, thus resulting in a single fault location. Aimingto verify the application of the proposed methodology, simulations were carried out in the ATP/EMTPSoftware considering a real distribution feeder with 3472 buses.

© 2014 Elsevier B.V. All rights reserved.

. Introduction

Several techniques have been proposed in the literature in ordero identify the fault location (FL) in electric power distributionystems (EPDS). Some established methods, such as those basedn the apparent impedance analysis are able to accurately iden-ify the fault distance based on one-terminal measurements. The

ain drawback of impedance-based methods is the multiple FLstimates due to the existence of several possible faulty points athe same distance [1]. Depending on some factors including theeeder’s topology, the number of estimates may be on the orderf tens, each one corresponding to a different feeder lateral. Somettempts have been made to mitigate this problem using expertystems or integrating information provided by measurements athe substation and the feeder protection schemes [2–4]. However,uch information is usually insufficient, inaccurate or unavailable5]. Thus, the identification of the exact FL on multi-branched dis-

Please cite this article in press as: D.S. Gazzana, et al., An integrated tecsystems, Electr. Power Syst. Res. (2014), http://dx.doi.org/10.1016/j.ep

ribution feeders is a problem that has not yet been solved.Impedance-based FL techniques are especially attractive

ecause of their low implementation cost, particularly those based

∗ Corresponding author. Tel.: +55 51 3308 4437; fax: +55 51 3308 3293.E-mail addresses: [email protected], [email protected] (D.S. Gazzana).

378-7796/$ – see front matter © 2014 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.epsr.2014.02.002

on one-terminal measurements [6]. Recently FL methods usingthe phase components approach have been developed for applica-tion on unbalanced EPDS [2–4,6–11]. However, individually thesemethods do not fully consider the characteristics of distributionsystems (unbalanced operation, presence of intermediate loads,laterals, and time-varying load profile), which significantly affecttheir performance. Except for [12] and [6], the previously citedmethods do not account for the time-varying load profile. Thisintrinsic characteristic of EPDS has a detrimental effect on the faultlocators’ accuracy, since the load data during the fault period isa required input for any impedance-based method. The approachdescribed in [12] requires measurements at each load point, whichare hardly available in practical EPDS. The technique presentedin [6] uses a single-step compensation of loads’ apparent power,although loads may also present power factor variations along thetime. Also, very few FL formulations have considered the shuntcapacitance of distribution lines in conjunction with its inherentunbalance [6–8]. However, it must be noted that in the cases oflong, lightly loaded overhead lines as well as underground feeders,the capacitive currents may be of considerable magnitude [13].

The techniques based on time–frequency analysis of fault-

hnique for fault location and section identification in distributionsr.2014.02.002

generated transient signals are considered the state-of-the-art insolving the FL problem on multi-branched radial networks. Thewavelet transform has been used [14–16] to analyze the high fre-quency components from traveling waves in order to deduce the

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L. To improve the wavelet analysis mother wavelets were inferredrom the fault-originated transient in [17]. Ref. [18] presented

technique for correlation between transmitted and reflectedaveforms using transmission lines models in representing dis-

ribution feeders. The application of artificial neural networks haslso been proposed to locate faults in EPDS, based on the analy-is of fundamental [19] and high frequency [16] components ofault-originated waveforms.

Most of the techniques discussed so far are focused on networksith small configurations, and therefore do not take into account

he topological characteristics of real EPDS. In this context, thisaper presents a hybrid FL approach to be applied on large andighly branched distribution feeders. The technique integrates thepparent impedance and transient analyses to identify the fault dis-ance and the faulted section using one-terminal measurements.he apparent impedance analysis is proposed to estimate the faultistance, using pre-fault voltage and current values to mitigatehe inaccuracies introduced due to the erroneous estimation ofhe system loading. An iterative algorithm to compensate the lineapacitive current component is also provided, thus making theethod suitable for application on underground or long, lightly

oaded rural feeders, in addition to overhead systems. The proposedethodology uses the fault distance information provided by the

mpedance-based technique to estimate theoretical frequenciesssociated with all of the possible propagation paths of the fault-enerated traveling wave. The transient analysis is then applied inrder to identify the most significant frequency components of theignal spectrum, which are correlated to theoretical frequencies.he correlation analysis is performed in both frequency domainnd time domain, thus resulting in a more reliable inference of thectual faulted section.

The paper is structured as follows. Section 2 presents techniquessed for fault detection and classification. Section 3 describes theethodology for fault distance estimation, including the appar-

nt impedance formulation, computation of equivalent systemsnd compensation of load uncertainty. Section 4 presents the tran-ient analysis approach for faulted section identification. Section 5resents the results obtained from several test scenarios simulatedsing ATP/EMTP software [20] considering a real distribution feederith 3475 buses. The discussions and conclusions are presented in

ection 6.

. Fault detection and classification

A power system disturbance is characterized by the distor-ion in voltage and current waveforms resulting in a change inngle and magnitude. Thus, the determination of the fault incep-ion instant can be performed by the comparison between currentnd past samples. Park’s transformation [21] is used in this paperor this purpose. Fault classification was performed by analyzinghe magnitude and angular relations of pre-fault and fault currentsepresented by symmetrical components [22].

. Fault distance estimation based on apparent impedancenalysis

The proposed method for fault distance estimation uses fun-amental frequency components of the voltages and currentseasured at the local terminal. A modified Fourier filter [23] is

sed to remove the DC component and estimate phasors repre-


enting the systems’ pre-fault and fault states. Pre-fault values aresed to both compensate load uncertainty and compute equiva-

ent systems for each possible path from the local terminal to theault point. Fault-period signals are analyzed in order to estimate

Fig. 1. Flowchart of the apparent impedance analysis.

the fault distance for each equivalent system. The methodology isillustrated in Fig. 1.

In describing the techniques represented in Fig. 1 the followingnotations are adopted in this section:

VPFS = [VPFSa VPFSb VPFSc]T pre-fault voltage at the source node (V);IPFS = [IPFSa IPFSb IPFSc]T pre-fault current at the source node (A);VFS = [VFSa VFSb VFSc]T fault voltage at the source node (V);IFS = [IFSa IFSb IFSc]T fault current at the source node (A);VF = [VFa VFb VFc]T voltage at the fault point (V);IF = [IFa IFb IFc]T fault current at the fault point (A);RFk fault resistance of phase k (�);Z third-order line series impedance matrix

(�/m);Y third-order line shunt admittance matrix

(S/m);Ix = [Ixa Ixb Ixc]T current at the lumped line impedance

upstream from the fault point (A);ID = [IDa IDb IDc]T current downstream from the fault point (A);Zeqn equivalent node impedance matrix (�);ZLeqn equivalent node load impedance matrix (�);� line section length (m);x fault distance (m).

3.1. Load variation compensation

Although the system load impedances are assumed to be avail-able for the fault location they can hardly represent exact values,since there is a degree of uncertainty in the loading estimation.Thus, the first step is to compensate the load uncertainties in orderto reduce the error in the fault distance estimation [24]. Knowingthe pre-fault voltages and currents, the apparent impedance seenfrom the local terminal can be computed as

ZPFS k = VPFS k

IPFS k, (1)

for k = a, b, c.By using the back-forward sweep power-flow described in [13],

the estimated current at the local node (I′PFSk) is determined. Thus,the estimated pre-fault impedance (Z′

PFk) is computed, as

Z ′PFSk = VPFSk

I′PFSk

. (2)

The load variation factors associated to the active (�Rk) andreactive (�Xk) components of load impedances are given by:

�Rk = (�{ZPFk} − �{Z ′PFk})

�{Z ′PFk} (3)

�Xk = (I{ZPFk} − I{Z ′PFk})

I{Z ′PFk} , (4)

where � {·} and I {·} denote the real and imaginary parts of acomplex number, respectively. Compensation of load variation is


performed by multiplying the real and imaginary parts of loadmatrices by (1 + �Rk) and (1 + �Xk), respectively. The describedprocedure is repeated, by running the power-flow and applying

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qs. (2)–(4) until the impedances given by Eqs. (1) and (2) match.onvergence is attained when the following condition is verified:

axk

(|�Rk|) ≤ ε and maxk

(|�Xk|) ≤ ε, (5)

here ε is a predefined tolerance.

.2. Equivalent systems’ computation

The computation of equivalent systems is performed in order toake the apparent impedance method suitable for application in

istribution feeders with an arbitrary number of lateral branchesnd loads. The technique consists in for each possible path fromhe local node to a fault point, to obtain equivalent impedancesor lines and loads outside the analyzed path. By considering theystem’s pre-fault conditions, the equivalent impedance of phase ks calculated for each feeder node n, according to:

eq nk = Vnk

I(m−n)k, (6)

here Vnk is the pre-fault node voltage and I(m−n)k is the currentowing from the upstream node m to node n. If node n is a connec-ion point for a lateral or load then the equivalent load impedanceor each phase k (ZL

eq nk) is also calculated:

Leq nk = Vnk

(I(m−n)k −∑

p∈˝nI(n−p)k)

, (7)

here I(n−p)k is the current flowing from the node n to node p, andn is the set of nodes downstream from n and outside the analyzed

ath.

.3. Equations for fault distance estimation

The mathematical formulation of the apparent impedance equa-ions is derived from the exact line segment model [13]. This modelas chosen because it is suitable for representing untransposed

verhead and underground (highly capacitive) distribution lines. Its also more accurate in modeling long, lightly loaded (rural) distri-ution systems, where capacitive currents may be of considerableagnitude. Fig. 2 shows the on-line diagram of a line of length �

epresented by the exact model. For a fault occurring at a distance from the source terminal, the resulting voltage at the fault pointVF) can be expressed as Eq. (8):

F = VFS − x · Z · Ix. (8)

Consider the generalized ground-fault model as shown inig. 3(a). If fault impedances are considered as pure resistancesepresented by RFa, RFb, RFc and RFg the voltage at the fault pointor each faulted phase k can be expressed as


Fk = RFk · IFk + RFg · IFg, (9)

here IFg = IFa + IFb + IFc. Eq. (9) can be generalized for any type ofault involving one or more phases and ground by making the

Fig. 3. Ground faults (a) and phase faults (b) models.

currents on the unfaulted (healthy) phases equal to zero. Replac-ing Eq. (8) in Eq. (9), it is possible to write Eq. (10) for each faultedphase k:

RFk · IFk + RFg · IFg = VFSk − x · Mk, (10)

where Mk is the kth row of the matrix M = Z·Ix.By separating Eq. (10) into its real and imaginary parts, it can be

found that

RFk · IrFk + RFg · Ir

Fg = VrFSk − x · Mr

k (11)

RFk · IiFk + RFg · Ii

Fg = ViFSk − x · Mi

k (12)

where the superscripts r and i denote the real and imaginary partsof the variables, respectively. By isolating RFk in Eq. (11), replacingin Eq. (12) and rearranging its terms it is possible to obtain Eq. (13):

x · (Mrk · Ii

Fk − Mik · Ir

Fk) + ViFSk · Ir

Fk − VrFSk · Ii

Fk − RFg · I{IFg · I∗Fk} = 0

(13)

Eq. (13) can be written for each faulted phase k. The sum of theresulting equations is a single generalized equation, which can beused to estimate the fault distance x for all types of ground faults.Considering that I{IFg · I∗Fg} = 0, the final form of Eq. (13) is given byEq. (14):

x =∑

k∈�I{VFSk· I∗

Fk}∑

k∈�I{Mk · I∗Fk

} (14)

where � is the set of faulted phases, given by any combination ofphases a, b and c.

For phase-to-phase faults, the model shown in Fig. 2(b) is con-sidered, and similar steps to those used to obtain Eq. (14) arefollowed. However, IFg = 0 in Eq. (9), and Eq. (10) will be a singleequation given by:

(15)VFSj − x · Mj = VFSk − x · Mk + RF · IFk,where j and k are thefaulted phases. The resulting generalized fault distance equationfor phase faults is expressed by Eq. (16):

x =I{(VFS k − VFSj

) · I∗Fk

}I{(Mk − Mj) · I∗

Fk} . (16)

Eqs. (14) and (16) give the fault distance x as a function of


both the fault current (IFk) and the current flowing through theline impedance upstream from the fault (Ixk). Since these vari-ables are unknown from the local terminal measurements, theyare estimated by an iterative procedure. Basically, each section

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speed.From Tables 1 and 2 it can be noted that since P1 and P2 are

at the same distance from the measuring terminal, paths 1-P1and 1-P2 have similar theoretical frequencies. Also, faults in both

Table 1Theoretical frequencies for a fault at point P1 of Fig. 4(a).

Path xp (km) np fcp (kHz)

1-P1 2.3 4 32.61-3 3 2 50

Table 2Theoretical frequencies for a fault at point P2 of Fig. 4(b).

Fig. 4. Propagation paths of the trave

f the equivalent system determined by procedure presented inection 3.2 is analyzed, starting from the substation. The iterativerocedure to solve Eq. (14) and (16) is described below.

By starting with x = �/2 as a first estimation of the fault distance,he unknown current Ix can be calculated considering compen-ation of the capacitive current flowing through the line shuntdmittance. As it can be noted from Fig. 2, the current Ix is giveny:

x = IFS − 0.5 · x · Y · VFS. (17)

The fault current IF is the difference between the current Ix

upstream from the fault point) and the sum of the load currentownstream the fault point (ID) and the capacitive current flow-

ng through the shunt admittance lumped at the fault point. It isxpressed as

F = Ix − (0.5 · x · Y · VF + ID) (18)

here

D = YD · VF , (19)

nd YD is the equivalent admittance downstream from the faultoint. By the series–parallel association of impedances and admit-ances shown in Fig. 2, it is possible to write:

D = {(� − x) · Z + [0.5 · (� − x) · Y + Z−1eq ]

−1}−1

+ 0.5 · (� − x) · Y.

(20)

Once current IF is obtained, the fault distance can be evaluatedsing Eq. (14) for a ground fault or Eq. (16) for phase-to-phase fault.rom the new estimate of x, a new current Ix is obtained using Eq.17) and the procedure repeated by applying Eqs. (18)–(20) untilonvergence is attained. In this paper convergence is considered byesting the following condition:

xiter−1 − xiter | ≤ ı, (21)

here iter is an iteration counter, and ı is a predefined tolerance. Ifhe fault distance x converges to a value greater than the length ofhe line section being analyzed, then the steps outlined above areepeated, but now considering the next line section of the analyzedquivalent system.

. Fault location based on transient analysis

A fault occurring in a distribution feeder generates a travelingave which propagates through the system. It will be reflected

everal times in the fault point and in the other terminals (dis-ontinuities) until the steady state. This fault-generated transient


as characteristic high-frequency spectral components accordingo its specific propagation paths. These frequencies can be identi-ed by applying a time–frequency transformation to the voltage orurrent waveforms recorded at the local terminal of a feeder [17].

ave due to a fault in P1 (a) and P2 (b).

In the proposed approach the essential idea is to establish a cor-relation between the characteristic frequencies associated with afault-generated traveling wave and theoretical frequencies, calcu-lated for each possible propagation path. The correlation resultsare used to infer a probability for the actual location of a fault,thus eliminating the multiple estimates obtained from impedance-based analysis.

The basic concepts related to the propagation paths of fault-originated traveling waves are briefly described considering thesimple distribution feeder shown in Fig. 4.

Let us suppose that a fault has occurred at the estimated distanceof 2.3 km from the measuring point in the system shown in Fig. 4.The two possible locations are points P1 and P2. For a fault in P1two possible paths for the traveling wave propagation are possible,indicated as 1-P1 and 1-3 as can be seen in Fig. 4(a). If the faultoccurs in P2 the wave propagates through paths 1-P2 and 1-2, asshown in Fig. 4(b).

Assuming the traveling wave propagation speed is known, thecharacteristic frequency fcp associated to each path p can be evalu-ated as [17]:

fcp = vnp · xp

(22)

where v is the wave propagation velocity (km/s); xp is the faultdistance (km) and np is the reflection coefficient of path p, whichcan be equal to 2 or 4 depending of the polarity of the reflectioncoefficient in the extremities of the analyzed lateral. At the faultpoint np can be considered close to −1 because the fault resistanceis lower than the line impedance. At the measurement point thecoefficient is equal to +1, because the short-circuit impedance ofthe system is higher than the network impedance. Based on thesedefinitions, np is equal to 2 for the paths 1-2 and 1-3 in Fig. 4, andfor paths 1-P1 and 1-P2 it is equal to 4. Tables 1 and 2 show thetheoretical frequencies associated with the faults located at pointsP1 and P2, calculated using Eq. (22) and considering v as the light


Path xp (km) np fcp (kHz)

1-P2 2.3 4 32.61-2 5 2 30

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frequency component closest to the theoretical frequency fcp, and

Fig. 5. Google Earth view of the real test feeder.

ocations will result in transients recorded at the local terminalhose spectrum will present the highest energy content for fre-

uency components around 32.6 kHz. Consequently, the frequencyomponents associated with paths 1-3 and 1-2 are of more impor-ance when inferring the actual fault location. Assuming a fault at1, the frequency component associated with path 1-3 (50 kHz)hould have higher energy content than the component associatedith path 1-2 (30 kHz). It must be noted that depending on the

ault resistance, the 30 kHz component can have energy magnitudelose to zero, since bus 2 is behind the fault point. Otherwise, for aault at point P2 the frequency related to the path 1-2 (30 kHz) willresent higher energy than that related to path 1-3 (50 kHz). Again,he later may not appear in the transient spectrum, since bus 3 isehind point P2.

As an alternative to increase the reliability of the technique, thenalysis of the transient fault signal is performed in both frequencynd time domain in this paper. The following sections provide aetailed description of the proposed approach to solve the multipleL estimates problem.

.1. Modal transformation and signal filtering

Modal decomposition of the phase voltage signals is accom-lished by using Clarke’s matrix. It is noteworthy that this matrix

s ideally used for symmetrical lines or fully transposed systems.


ased on several simulations for different fault types and resis-ances, it was found that the propagation modes 0, 1 and 2 haveetter applicability depending on the type of fault. Thus, in this

Fig. 6. Average and maximum errors on the fault

PRESSms Research xxx (2014) xxx–xxx 5

paper the propagation modes are applied according to fault typesas follows:

• Mode 0: phase–ground and 2 phase–ground faults.• Mode 1: phase–phase faults.• Mode 2: 3 phase–ground and phase–phase faults.

The fundamental frequency usually is the spectral componentof the transient signal with greatest energy magnitude. Thus, itmust be removed in order to identify and evaluate the spectralcomponents related to the traveling wave propagation paths inan acceptable energy scale. In this paper a high-pass filter typeButterworth, order 10 with cutoff frequency of 300 Hz was adopted.

4.2. Frequency domain correlation

For a transient analysis in the frequency domain the Fourier,Wavelet and Gabor transforms can be used [25]. The essential ideais to identify the frequency components with most representa-tive spectral energy magnitudes. Based on several simulations itwas found that all mentioned methods identify similar frequencycomponents associated with the paths of the traveling wave prop-agation. Thus, by using the discrete Fourier transform the mostrelevant spectral components of the fault signal are obtained. Fromthe fault distance provided by apparent impedance, Np propagationpaths can be determined. For each path the total length of the linesegment is identified, which is characterized by the first discon-tinuity (change of cable, load, transformer). This length is definedas the length of the section. Also, by using Eq. (22) Np theoreticalfrequencies are evaluated.

The faulted section is inferred on the frequency domain basedon the comparison of the theoretical frequencies and the most rep-resentative frequencies of the signal spectrum. Thus, the frequencycomponent associated with the path that presents the high mag-nitude of the fault distance and the less magnitude of the sectionlength will indicate the actual faulted section. Then the coefficientof correlation on the frequency domain Rf

p is defined as

Rfp = Y(xcp) +

Np−1∑

e = 1

e /= p

Y(�ce) (23)

where Y(xcp) is magnitude of normalized Fourier transform of the


Y(�ce) is the magnitude of normalized Fourier transform of the fre-quency component closest to the theoretical frequency associatedto the end of the line section of other possible path e.

distance estimate according to fault types.

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estima

4

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Fig. 7. Average errors on the fault distance

.3. Time domain correlation

In the time domain approach the cross-correlation is performedetween the fault signal and Np sinusoidal signals of frequencyiven by ωp = 2 · · fcp, where fcp is the theoretical frequency asso-iated with path p (Eq. (22)). The cross-correlation between ainusoidal signal zp and the fault signal y can be determined as26]:

tyzp

(t1, t1 + ) = limN→∞

1N

N∑

k=1

zpk(t1) yk(t1 + ) (24)

here N is the number of samples; k is the sample point at thenstant of time t and is the time step. After a normalization process,he correlation coefficients Rt

yzp indicate the correlation degreeetween signal y and zp, where a Rt

yzp value close to 1 indicates high degree of correlation.

Finally, the actual faulted section is inferred as a probabilityiven by the normalized average of coefficients (23) and (24).

. Tests and results

The performance of the proposed FL method was evaluatedonsidering a real 13.7 kV distribution feeder located in southernrazil (Ibiúna – SC). The feeder has 3475 buses, a total line lengthf 147 km and an average load demand of 4.38 MVA. The system’sine equipment includes 431 distribution transformers, 2 capaci-or banks (150 kVAr and 600 kVAr) and a three-phase step-voltageegulator. The test system was simulated using BPA’s ATP-EMTP


oftware [20], with a sampling frequency of 400 kHz. The proposedL formulation was developed in MATLAB [27].

Simulations were performed considering 180 test scenarios,omprising the following fault conditions:

Fig. 8. Average and maximum errors on the fault d

te according to fault types and resistances.

• Fault types: Ag, Bg, Cg, ABg, BCg, ACg, AB, BC, CA and ABC.• Fault resistances: 0, 15 and 30 �.• Fault distances: 6595.4 m (F1), 6804.1 m (F2), 11 067.1 m (F3),

11 377.4 m (F4), 14 243.4 m (F5) and 16 661.4 m (F6).

Fig. 5 shows a Google Earth view of the test feeder, where thelocations of simulated faults are indicated as F1–F6 and the mea-suring point as MP.

Results are presented in this section first considering the perfor-mance evaluation of the impedance-based technique. In this case,the percentage errors in fault distance estimates were calculatedby Eq. (25):

e [%] = |xest − xsim|�tot

· 100 (25)

where xest is the fault distance estimate, xsim is the simulated faultdistance and �tot is the total line length, equal to 147 km for thesystem studied.

Fig. 6. shows the performance of the apparent impedance tech-nique in terms of the average and maximum values of percenterrors in the fault distance estimate according to the fault types.Maximum errors were 0.08% (117.7 m) for faults Ag, Bg and ABgwhich also showed the highest average error values (44.1 m). Thefault type does not significantly affect the fault location estimation,although the method presented slightly smaller errors for faultsbetween phases in relation to the ground faults.

The average errors on the fault distance estimation, consideringfault types and resistances are shown in Fig. 7. It can be noted thatthe average error steadily increases for all fault types with the faultresistance value. The maximum average error was 0.06% for faults


Ag, Bg, and ABg with fault resistance of 30 �. It represents an errorof 88.2 m, which can be considered acceptable in light of the highvalue of fault resistance. The increase in error with fault resistanceis a well-known problem in FL research [5], explained mainly by the

istance estimate according to fault distances.

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Fig. 9. Performance of the transient analysis according to fault types.

Fig. 10. Performance of the transient analysis

Fig. 11. Performance of the transient an

Fig. 12. Number of fault location estim


erroneous estimation of the load current for high fault resistances,which directly affect the fault current estimation.

The effect of fault distance on the proposed impedance-basedmethod is shown in Fig. 8. From these results it is possible to observethat both average and maximum error values steadily increase withthe fault distance. This is due to the fact that as the analyzed linesection moves away from the substation, more inaccuracies areintroduced in estimating the capacitive and load currents of theupstream nodes. Consequently, it results in erroneous estimationsof the fault current. It can be seen that the error associated with thefarthest fault location (F6) presented the smallest value among thecases. As shown in Fig. 5, the point F6 is located on a long branchof the feeder which has few laterals along the path to the measur-


ing point. Thus fewer inaccuracies are propagated in the systemequivalents’ computation process.

according to fault types and resistances.

alysis according to fault distances.

ates according to fault distances.

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8 r Systems Research xxx (2014) xxx–xxx

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In the remainder of this section results of the transient analysisre presented in terms of the percentage number of cases in whichhe highest probability was assigned to the actual fault location.ig. 9 shows that the method presented its best performance innferring the actual location of faults Ag. In this case, the highestrobability was correctly assigned to 61.1% of the simulated cases.

t can be noted that the method’s performance has rather undefinedehavior in relation to fault type. The lowest percent of cases inhich the faulted section was correctly identified was associatedith the BCg fault. In this case, the actual location was inferred in

of the 18 cases associated with that fault type.The effect of fault types and resistances on the transient anal-

sis is shown in Fig. 10. Again, the method presents undefinederformance behavior in relation to fault types and resistances.lthough the number of cases in which the correct fault loca-

ion is identified increases 6% when fault resistance changes from � to 15 �, the results suggest that the fault type and resistanceo not significantly affect the performance of transient analysis.n the other hand, from Fig. 11 it can be noted that the faulted

ection inference becomes more difficult as the fault distancencreases. This is predictable behavior, since the number of esti-

ates tends to increase as the fault moves away from the localerminal. Fig. 12 shows the maximum and average number of esti-

ates resulting from the impedance analysis in relation to the faultistance.

By analyzing Figs. 11 and 12, it can be observed that the method’serformance is not dependent only on the number of FL estimates.ig. 12 shows that the number of estimates decreases from F3 to F5,hile the respective number of correct assignments for the actual

L decreases. This behavior is attributed to the feeder topology. Inig. 5 it can be noted that point F4 is located in a region with a higheroncentration of branches in relation to F3. Although the number ofstimates associated with F3 is nearly the same of F4 (Fig. 12), a faultt F4 will result in a higher number of similar frequencies measuredt the local terminal. This increased number of frequencies is duehe traveling wave reflections at the endpoints and tap points ofhe branches, thus resulting in more wave propagation paths to benalyzed.

Despite being the farthest location from the measuring terminal,oint F6 is located in a region with few lateral branches. Hence

fault at this point requires the analysis of a smaller number ofropagation paths in comparison to F3, F4 and F5. Thus, it facilitates

nference of the correct frequency component associated with theault location.

Table 3 summarizes the results obtained from the transientnalysis considering all simulated cases. For each case results arexpressed in terms of the probability assigned to the correct faultocation (upper value), while the lower value (in parenthesis) cor-esponds to the highest probability value assigned to an erroneousault location estimate. Cases in bold correspond to simulationsn which the highest probability was assigned to the actual faultocation.

By analyzing Table 3, it can be noted in cases of erroneous faultocation inference the probabilities assigned to actual (upper) andrroneous (lower) estimates generally present very similar val-es. For these cases, probabilities assigned to the erroneous andctual location differ 0.67% on average. On the contrary, cases oforrectly inferred location show upper and lower probability val-es more distant from each other in Table 3. The average differenceetween the correctly assigned probabilities and the highest valuesf probabilities assigned to erroneous estimates are 3.1%. Thus, theroposed method allows inferring the actual fault location even if

Please cite this article in press as: D.S. Gazzana, et al., An integrated technique for fault location and section identification in distributionsystems, Electr. Power Syst. Res. (2014), http://dx.doi.org/10.1016/j.epsr.2014.02.002

he highest probability is assigned to an erroneous estimate, sincehe actual fault location tends to present a similar probability value.n a worst-case scenario this approach provides a priority rank ofeeder’s locations to be investigated for a utilities’ crew. Ta

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The transient analysis was able to correctly infer the actual faultocation in 51.7% of the cases shown in Table 3. These results areromising in view the complex topological structure of the testeeder, which has resulted in a large number of estimates obtainedrom the apparent impedance analysis. It must be pointed that theverage of 9 different fault location estimates was obtained fromhe simulated fault scenarios. As shown in Fig. 12, the maximumumber of 17 estimates has resulted in case F3.

. Conclusion

This paper presented a hybrid method to locate faults in dis-ribution systems using one-terminal measurements. An apparentmpedance-based algorithm which considers system unbalance,ine shunt admittances, intermediate laterals and load variation

as developed in order to estimate the fault distance. Resultshowed that the average error in the fault distance estimation is lesshan 0.02% over all simulated cases. Maximum errors were 0.08%or the fault resistance value of 30 �.

In order to account for the multiple estimates resulting fromhe impedance-based algorithm, thereby identifying the faultedection, the analysis of the transient fault signal was proposed.he method is based on the evaluation of the fault-originatedransient’s aiming to identify its spectral component with highestnergy content. It is correlated to theoretical frequencies, cal-ulated for different propagation paths obtained from the faultistance information provided by the impedance-based technique.

more reliable fault location inference was possible by using aime–domain correlation analysis. Test results shown that it is pos-ible to obtain an effective inference of actual location, even inarge and highly branched distribution feeders. The transient tech-ique was able to correctly infer the actual fault location in 51.7% ofhe simulated cases. These results are promising in view the com-lex topological structure of the test feeder, since the average of 9ifferent fault location estimates was evaluated.

Most of the techniques based on time–frequency analysis foundn the literature are focused on networks with small configurations,hus it is difficult to evaluate their performance on real applica-ions. The difficulties that arise when applying a transient analysiso locate faults on practical EPDS were highlighted in this paper. Thepproach has its accuracy reduced depending on the fault location,specially when faults occur on feeder’s areas with high concen-ration of branches. It must be pointed that the transient analysisepends on receiving faithfully reproduced signals from the volt-ge transducers. The effects of measuring errors and the frequencyesponse of potential transformers were not evaluated in the testserformed. Future works will consider these factors, as well as

mprovements on the time–frequency correlation by using infor-ation provided by the apparent impedance analysis, such as fault

ype and resistance.


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dx.doi.org/10.1016/j.epsr.2014.02.002

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An integrated technique for fault location and section identification in distribution systems