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A Novel High Impedance Fault Location forDistribution Systems Considering Distributed

GenerationArturo S. Bretas, Member, IEEE, Miguel Moreto, Student Member, IEEE, Rodrigo H.Salim, and LucianoO.Pires

Index Terms— Distributed generation, Fault location, Neuralnetwork applications, Power distribution faults, Power distribu-tion protection.

I. INTRODUCTION

The detection and location of high impedance faults (HIF) inpower distribution systems (PDS) represents a great challengefor the power community because of several non-linearitiesthat can be produced by such faults. HIF are difficult to bedetected by overcurrent relays usually employed in power dis-tribution protection because of the low fault current magnitudegenerated. A HIF can be confused with a normal load increaseor decrease, leading the protection schemes to failure, causingdamage to property and exposing the population to hazards.The most common causes of such faults in distribution systemsare the broken conductors or its contact with poorly groundedobjects, like trees, vehicles and wood fences.

The location process of HIF is also a very difficult task.The apparent positive sequence impedance fault location al-gorithms commonly used in digital relays, can produce signif-icant errors in the location of such faults, especially whendistributed generation (DG) facilities are connected to thefeeder.

This work was supported by CEEE grant #9923983 and CAPES.A. S. Bretas and R. H. Salim are with the Federal University of Rio Grande

do Sul, Brazil.Miguel Moreto is with the Federal University of Santa Catarina, Brazil.L. O. Pires is with the Pontifical Catholic University of Rio Grande do Sul,

Brazil.

Fig. 1. HIF model.

With the purpose of reducing the problems caused by a HIF,several studies have been developed to detect HIFs [1]–[6].Concerning distribution system fault location, the majority ofpublished works deals with low impedance linear faults [7]–[11].

In order to investigate the influence of HIFs in feederswith DG and reduce the restoration time and the hazardousconditions caused by the non-detection of HIFs, it is proposedin this paper a new fault detection and location scheme. Theproposed methodology is based in the use of particular featuresof HIF and Artificial Neural Networks (ANN).

This paper is presented as follows: In section two, it ispresented the HIF fault model used in this study. In the thirdsection the impacts of distributed generation in PDS are shortlydiscussed. In the fourth section, a brief introduction to ANNsis presented. Section five presents the proposed scheme. Thepower distribution feeders used in this work and the resultsobtained are shown in sections six and seven respectively.Section eight presents the conclusions obtained from this work.

II. HIF MODELING

In this work, a HIF model which acts as similar as possibleto real faults was used. The HIF model used takes intoaccount the existence of an electric arc at the fault point. Asstated in [12], [13], the electric arc has a voltage/current non-linear relation and might show an asymmetric behavior of thepositive half cycle with respect to the negative one.

The HIF model used in this study was based on the workof Emanuel et al [13]. In this study, the authors have reportedthe effects of HIFs in the magnitude and phase of the faultcurrent low frequency spectrum (1st to 5th harmonics). As aresult, they presented a HIF model based on an electric circuit.This model is illustrated in Fig. 1.

The HIF is modeled with two absolute DC sources inseries with diodes. During the positive half cycle, the currentflows through VP and during the negative, through VN . Thesevoltage values are maintained constant during the simulations.

1-4244-0288-3/06/$20.00 ©2006 IEEE

2006 IEEE PES Transmission and Distribution Conference and Exposition Latin America, Venezuela

2

The harmonic content generated by the fault are functions ofthe voltage difference VN −VP and the relation XL/R. Theserelations were confirmed by experimental tests.

In Fig. 2, it can be seen the fault current produced by theHIF model together with an harmonic analysis of the signalresulting from an one cycle Discrete Fourier Transform (DFT).

III. DISTRIBUTED GENERATION IMPACTS

The addition of new generation units to the power distribu-tion feeders has great consequences in the overall operationof these systems. As it is a new source of power inside thesystem, the main change introduced by the addition of DG inPDS is the loss of its radial characteristic.

The addition of DG changes the original power flow, makinga modification in the protection systems settings necessary.Thus, the protection equipments that have protection routinesand settings for a specific circuit could work improperly.

In result of these facts, the reliability, the safety and theefficiency of the protection systems are affected by DG.With this, the system capacity in maintain the electric powersupply with minimum interruptions, with quality and withoutputting the system and the consumer in risk gets compromised,making necessary a new approach that considers DG.

IV. ARTIFICIAL NEURAL NETWORKS

The ANNs were developed based on data processing char-acteristics at the human brain. The greatest advantage ofusing this technique is due to their ability to learn fromexamples and generalize the result for inputs not seen in thetraining phase. An ANN consists in a set of processing unitsconnected to each other. Each processing unit, also calledartificial neurons, implements a mathematical expression andis based on the biological neuron behavior. Artificial neuronsare the fundamental units of an ANN. The output expressionof an artificial neuron is given by (1).

y = φ

(m∑

i=1

xi · wi + b

)(1)

Where xi are the value of the input i, wi is the weight valueassociated with the input i, b is a bias value, m is the totalnumber of inputs and φ is the activation function, usually anon-linear function.

0 0.01 0.02 0.03 0.04 0.05 0.06−40−20

0204060

Time [s]

Faul

t cur

rent

[A

]

0 60 120 180 240 300 360 420 480 5400

5000

10000

Frequency [Hz]

Mag

nitu

de

Fig. 2. Fault current and its harmonic content.

Fig. 3. Multi-layer neural network.

Fig. 3 shows an example of a feed forward ANN. Theartificial neurons are organized in layers. The layers locatedbetween the output and the input layers are the hidden layers.

The training of an ANN is an iterative process which con-sists, at its first step, in applying known inputs to the networkand calculating its output. The next step is a comparison ofthe calculated output with the expected one for the appliedinput, resulting in an error signal. This signal is used to adjustthe synaptic weights (wi) in order to approximate the value ofANN’s output to the desired one. This process of adjustment isrepeated until a minimum error value is reached. The learningmethod described is called supervised learning [14].

V. THE PROPOSED METHODOLOGY

The use of harmonics to detect high impedance faults wasstudied by several authors. In reference [15] the second, thirdand fifth current/voltages harmonics were used to to detect thepresence of HIF in a PDS using ANNs. Also, several otherstudies using only third harmonic components were publishedin [2], [16], [17]. In [17] common harmonic sources presentin a PDS were compared with the harmonics generated bya HIF. In this last work, it was shown that the harmonicsphase angle have unique aspects due to the occurrence ofHIFs, specially the angle difference between the complexnumbers that represent the third harmonic current and thefundamental voltage. Its value is obtained by the measureof the time between the zero crossing of the fundamentalfrequency reference and the next zero crossing in the samedirection of the harmonic. In [2], the same authors proposeda protective relay to detect HIFs, using this characteristic.

In this paper, it is proposed the use of the angle differencestated by [17] in conjunction with the third harmonic currentmagnitude for HIF location in PDS. As can be seen in Fig.2, the third harmonic is the most significant one (excludingthe fundamental) and it is an important source of informationabout the HIF. The fundamental voltage and current are used aswell, allowing the methodology to also locate low impedanceand linear faults.

The proposed scheme utilizes current and voltage dataobtained only from local Potential Transformers (PT’s) andCurrent Transformers (CT’s). A block diagram of the method-ology is shown in Fig. 4.

A. Feature extractor

The feature extractor, which is executed online, processesthe data to obtain the input patterns that will be used in thenext processing block. These input patterns obtained fromthe feature extractor are the fundamental voltage and current

3

Fig. 4. The proposed scheme block diagram.

phasors of the three phases and the three phase 3rd harmoniccurrent phasors. Also, the feature extractor calculates thesymmetrical components of the 2nd, 3rd and 5th harmonicscurrent signal which are used in the fault detection process.

A one cycle Fourier Filter adjusted for each harmoniccomponent is used to estimate the phasors. The equation ofthe Fourier filter can be seen by (2) [18].

Y k =2

N·

N∑n=1

y(n) ·

[cos

(2πnk

N

)+ j · sin

(2πnk

N

)](2)

where:

Y k Phasor estimation of the k harmonic;y Input signal;N Number of samples per cycle;n Number of the sample;k Harmonic order (k = 1, 2, 3, ...);

Sometimes, decaying DC offset can be observed in thecurrent signal due to the fault occurrence. The decaying DCoffset degrades the Fourier filter response, leading wrongestimates by fault location processes. This problem is solvedusing the phasor estimator proposed by [19], where threeconsecutive phasors are calculated in order to estimate thetime constant of the decaying DC offset and eliminating itsinfluence.

B. Fault detection and identification

The fault detection and identification routine is based onthe symmetrical components of the 1st, 2nd, 3rd and 5th

harmonics of the current signal, measured at the feeder’ssubstation terminals. These components feed an ANN whoseoutputs indicate the fault presence and the fault type.

The ANN’s input vector is given by the magnitude ofthe symmetrical components of the 1st, 2nd, 3rd and 5th

harmonics calculated from the three phase current signals. Thisvector, called Y, is shown in (3).

Y =[|I

(0,1,2)1h | |I

(0,1,2)2h | |I

(0,1,2)3h | |I

(0,1,2)5h |

](3)

Where the superscript (0, 1, 2) denotes the zero, positive andnegative sequence components, respectively and the subscripts1h, 2h, 3h and 5h denotes the harmonics order.

The fault incidence instant is determined through successivefault type identification executed at every time step using anone cycle sampling window. The fault is detected when thesame type of fault is identified for 10 consecutive samples

Fig. 5. Fault detection process flow chart.

Fig. 6. Fault location block diagram.

(considering a 11520Hz sampling rate frequency). A flowchartof the entire fault detection process is given in Fig. 5.

C. Fault location

The proposed fault location routine is based in two ANNs.One trained for phase faults and other for ground faults.According to the fault type, the input vector is created basedonly in the faulted phases. In case of phase-to-phase faults,the input vector is created considering the subtraction of thetwo phases involved. For ground faults, only one of the faultedphases is utilized by the algorithm. The fault location blockcan be seen in the Fig. 6. According to the fault type and thefault incidence instant, both previously determined, the correctANN is selected and the proper input vector is calculated.

The input vector X is given by (4):

X =[|V 1h| |I1h| |I3h| Δθ1h Δθ3h

](4)

Where:|V 1h| Magnitude of the fundamental voltage phasor;|I1h| magnitude of the fundamental current phasor;|I3h| magnitude of the 3rd harmonic current;Δθ1h angle difference between the complex numbers V 1h

and I1h;Δθ3h angle difference between the complex numbers V 1h

and I3h.These features are normalized to be applied to the ANN

which single output returns a value ranging from 0 to 1 ( thebeginning and the end of the distribution feeder).

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TABLE I

POWER SYSTEM DATA.

Bus Length Branch Impedance Pj Qj

i j [km] Rij[Ω] Xij[Ω] [kW] [kVAr]0 1 4.18 1.15 1.14 2646 8821 2 1.26 0.35 0.34 522 1742 3 1.26 0.35 0.34 4896 16323 4 2.19 0.60 0.60 936 3124 5 1.03 0.28 0.28 0 05 6 1.93 0.53 0.53 1806 6026 7 1.58 0.44 0.43 0 07 8 1.58 0.44 0.43 1503 5018 9 1.55 0.43 0.42 189 639 10 1.55 0.43 0.42 0 0

10 11 4.65 1.28 1.27 657 21911 12 2.17 0.60 0.59 336 11212 13 0.89 0.24 0.24 125 4213 14 1.80 0.50 0.49 225 85

VI. POWER DISTRIBUTION SYSTEM OF STUDY

Two power distribution systems were considered in thiswork. The first one is a modified version of the systempresented in [20]. In its original form, the system was a radialdistribution network with several laterals and no DG. In orderto avoid the difficulties involved in locating faults on laterals,it was performed a substitution of the laterals for an equivalentload. This system is illustrated in Fig. 7.

The second system tested had the same parameters than thefirst one, except that a DG facility was inserted. This systemis illustrated in Fig. 8.

The distribution lines were modeled as RL circuits withoutmutual coupling and the loads as constant impedance. Table Ipresents the system’s data. At its maximum load, the systemconsumes approximately 10MW. The system was simulated inATP/EMTP software [21].

VII. IMPLEMENTATION AND RESULTS

A. Implementation

The ANNs training set was formed by four distinct faulttypes: Phase-to-ground (Ag), double phase-to-ground (BCg),double-phase (BC) and three phase. For each simulated casethe faults were applied in 67 different locations in each feeder.Of these 67 positions, 51 were used in the ANN trainingprocess while the other 16 were used as test cases.

The following parameters were changed for the fault detec-tion process:

• Ten fault types: A-g, B-g, C-g, AB-g, BC-g, AC-g, AB,BC, AC, ABC-g;

• No fault case;• Six fault models: HIF, Rf = 0, 10, 20, 50 and 100 Ω;• 5 different fault positions in each system: Nodes 1, 15,

30, 50 and 67.

Fig. 7. Studied distribution system unifilar diagram.

Fig. 8. Studied distribution system unifilar diagram with GD.

The following parameters were changed for the fault loca-tion process:

• Four fault types: A-g, BC-g, BC, ABC-g;• Six fault models: HIF, Rf = 0, 10, 2050 and 100 Ω;• 67 different fault positions in each system;• Two load values: 100% (nominal) e 50% (half load).The ANNs used were feedforward networks with one hid-

den layer. The fault type identification ANN had 12 inputsneurons, 15 hidden neurons and 10 output neurons (12-15-10). One of the outputs is active for each fault type. Whenthere is no fault, all the outputs remain inactive. The ANNsused for the fault location had a neuron topology of 5-7-1.For all the ANNs used, the activation function at the hiddenlayers was a logarithmic sigmoid with maximum value equalto 1. A pure linear function was used at the output neurons.

The training algorithm used was the supervised Levemberg-Marquardt backpropagation implemented in the Matlab’s ANNtoolbox [22]. The mean square error was used as a perfor-mance function with a goal value of 5.0e-5.

Also, the PDS fault location algorithm proposed by Lee etal [11] was implemented in Matlab’s environment [22].

For the analysis of the results, the error profile was calcu-lated by (5):

error[%] =

∣∣∣∣xdesired[km] − xobtained[km]

feeder lenght [km]· 100

∣∣∣∣ (5)

B. Results

After finishing the training stage, the proposed scheme wastested by applying test cases in each system. The test set usedwas formed by 768 cases, according to the Section VII-A.The fault detection scheme detected correctly all the faultspresented in the test set. It suffered no influence from faultresistance, load level, fault type, location or the DG’s presence.

With the fault correctly detected and its type identified, thefault location process could start. Some of the results obtainedby the fault location routine and by Lee et al [11] algorithmare presented in Fig. 9, 10, 11 and 12. These figures shows thepercentage fault location error for each fault simulated in the67 feeder’s nodes with 100% loading. In this way, a profile ofthe errors over the feeder’s length can be visualized.

The Lee method resulted in higher errors in comparison withthe proposed scheme when the fault is beyond the DG con-nection. The effects caused in the fault location performanceby the variation of certain parameters are shown below.

1) Influence of the Distributed Generation: As expected,the DG has highly influenced the results obtained by bothmethodologies showed in this paper. The ANNs of the pro-posed scheme were retrained in order to take into account the

5

HIF RF=0 ohm Rf=20 ohm Rf=100 ohm

Err

or

[%]

Err

or

[%]

Fault distance [km]

Fault distance [km]

Ground faults - ANN method

Phase faults - ANN method

0

0.5

1

1.5

2

2.5

3

3.5

4

0 2 3 4 5 7 8 9 10 11 13 14 15 16 17 19 20 21 22 24 25 26 28

0

1

2

3

4

5

6

7

0 2 3 4 5 7 8 9 10 11 13 14 15 16 17 19 20 21 22 24 25 26 28

Fig. 9. Proposed method performance without DG.

remote generator. With the new ANNs, the errors were below5% for HIF. For the same tests, the Lee method produced wellabove 5% errors for faults located after the GD location.

2) Fault Resistance Influence in Fault Location: Accordingto the figures one can notice an increase in the errors ofthe proposed routine as the fault resistance increases. Thisbehavior is also significant in the Lee method. When appliedin HIFs cases, the Lee method produce errors greater than40% due to its formulation which considers only linear faults.

3) Load Level Influence in Fault Location: The reduction ofthe load level resulted in a better fault location estimative forlinear faults obtained by Lee method. Concerning HIFs, theload level reduction caused an error increase. The proposedmethodology resulted in more accurate estimates as the loadlevel decreases. The explanation for this result is that whenthe load is reduced, the feeders voltage profile becomes higherthan its normal value. This causes an increase in the harmoniclevels produced by the fault model, which allows the inputpatterns to become more representative for HIFs.

Rf=0 Rf=50 Rf=100

0

1

2

3

4

0 2 3 4 5 7 8 9 10 11 13 14 15 16 17 19 20 21 22 24 25 26 28

0

1

2

3

4

5

6

0 2 3 4 5 7 8 9 10 11 13 14 15 16 17 19 20 21 22 24 25 26 28

Fault distance [km]

Fault distance [km]

Err

or

[%]

Err

or

[%]

Ground faults - LEE method

Phase faults - LEE method

Fig. 10. LEE method performance without DG.

Ground faults - ANN method

HIF RF=0 ohm Rf=20 ohm Rf=100 ohm

Err

or

[%]

Fault distance [km]

0

1

2

3

4

5

6

7

0 2 3 4 5 7 8 9 10 11 13 14 15 16 17 19 20 21 22 24 25 26 28

Phase faults - ANN method

0

1

2

3

4

5

0 2 3 4 5 7 8 9 10 11 13 14 15 16 17 19 20 21 22 24 25 26 28

Err

or

[%]

Fault distance [km]

Fig. 11. Proposed method performance with DG.

4) Fault Type Influence in Fault Location: The fault typehad no influence on the results for the earth faults tested. Thisis because all the earth faults are analyzed as single phase ones.For phase faults, only a small error reduction is observed forthe ANN’s methods. The Lee’s method resulted in an smallincrease on the percentage errors of such faults.

5) Fault Distance Influence in Fault Location: The faultdistance has influence on the errors obtained by the proposedscheme. This influence is related with the feeder’s topology.The greatest errors occurs in the fault points near to the moresignificant loads in the system.

VIII. CONCLUSION

In this paper a novel fault detection and location method-ology for power distribution feeders with DG is proposed.The proposed scheme is capable to lead precise fault detectionand location estimations for both linear and non-linear (HIF)faults. The scheme is based on ANNs and in local measure-ments obtained from the substation. The proposed scheme has

0

20

40

60

80

100

120

140

0 2 3 4 5 7 8 9 10 11 13 14 15 16 17 19 20 21 22 24 25 26 28

Rf=0 Rf=10 Rf=20

0

5

10

15

20

25

30

0 2 3 4 5 7 8 9 10 11 13 14 15 16 17 19 20 21 22 24 25 26 28

Ground faults - LEE method

Phase faults - LEE method

Err

or

[%]

Err

or

[%]

Fault distance [km]

Fault distance [km]

Fig. 12. LEE method performance with DG.

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correctly detected and classified all the simulated faults, evenwith the presence of DG. The average errors encountered bythe proposed fault location procedure were below 5% of thefeeder’s length in almost all simulated cases. In comparisson tothe proposed technique, the Lee method showed better resultsfor linear faults (average error bellow 1.1% of the feeder), butit leads to errors greater than 40% for HIFs. When applied toa system with DG, the Lee’s method also presented greatererrors mainly after the DG’s connection point.

The main result of this work is that the chosen inputpatterns, calculated from local measurements, can characterizecorrectly the fault distance, taking into account DG and HIF.

The proposed methodology showed to be worthy of con-tinuing research. The modeling of harmonic sources, differentfault incidence angles, feeder topologies considering lateralsand fault characteristics are subjects of ongoing work.

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Arturo Suman Bretas (M’98) was born in Bauru,Sao Paulo, Brazil, on July 5, 1972. He received theB.S. and M.S. degrees in electrical engineering fromthe University of Sao Paulo, Brazil, in 1995 and1998, and the Ph.D. degree in electrical engineeringfrom Virginia Tech, Blacksburg, VA, in 2001. Hehas also awarded a Sabbatical Fellowship in 2004at the Institut National Polytechnique de Grenoble(France). Currently, he is an Associate Professor atthe Electrical Engineering Department at the FederalUniversity of Rio Grande do Sul, Porto Alegre,

Brazil. His research interests include power system protection, control, andrestoration.

Miguel Moreto Miguel Moreto was born in NovaPrata, Rio Grande dos Sul, Brazil, on December18, 1979. He received the B.S. and M. S. degreesin electrical engineering from Federal Universityof Rio Grande do Sul, Porto Alegre, Brazil, in2003 and 2005. Currently he is pursuing his Doctordegree with the Power Systems Research Group ofthe Federal University of Santa Catarina, locatedat Florianopolis, Brazil. His main research areasare power systems fault diagnosis, power systemprotection and automatic event analysis.

Rodrigo Hartstein Salim was born in Porto Alegre,Rio Grande do Sul, Brazil, on September 15, 1983.He is currently working in his B.S. degree in elec-trical engineering in the Federal University of RioGrande do Sul. His research interests include powersystem protection and control.

Luciano de Oliveira Pires was born in Santa Maria,Rio Grande do Sul, Brazil, on November 14, 1979.He received the B.S. degree in electrical engineeringfrom the Federal University of Santa Maria. He wasa professor of the Technical School Alto Jacuı, inIbiruba and nowadays is the responsible engineer ofCOOPERNORTE.