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2390 IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 20, NO. 4, OCTOBER 2005

II. NEURAL-NETWORK APPLICATION TO FAULT ANALYSIS

Artificial neural networks (ANNs) can be applied to fault

analysis because they are a programming technique applicable

to problems in which the information appears in a vague, re-

dundant, distorted, or massive form. Also, they are able to learn

using examples.

In the problems of fault classification and fault location, theyare potentially applicable because:

many parameters must be considered, specially in certain

conditions, such as double-circuit lines;

there are methods to simulate examples in a quick and

reliable way;

the conditions of the electric system change. A neural net-

work is able to adapt itself to the new state, immediately,

just putting it under a new training;

the ANN output is very fast, because its working consists

in a series of very simple operations.

Although the programming using ANNs has great advan-

tages, it also presents some disadvantages [16]. Among them,the complexity of the type and the network architecture selection

(number of layers, number of neurons per layer, activation func-

tions, learning algorithms parameters, etc.) can be emphasized.

The fault location problem in transmission lines using ANNs

consists in defining a neural network that allows to obtain the

position at which the fault has occurred, using a small number

of electrical parameters measured in a line terminal. These pa-

rameters are the values of the fault and prefault voltages and

currents in steady state.

Due to the fault type, the voltage and current values are going

to be very different. This fact divides the resolution of the fault

location problem into two steps: the fault classification and thefault location.

Thus, the fault classification consists in obtaining a neural

network that allows to determine the fault type, from the fault

and prefault voltage and current values measured in a line ter-

minal.

The fault location consists in obtaining a neural network that,

using the same values used in the fault classification process,

allows to obtain the fault position.

III. ANN STRUCTURES FOR FAULT CLASSIFICATION IN SINGLE-

AND DOUBLE-CIRCUIT LINES OF TWO TERMINALS

A. ANN Structure

The fault classification method proposed in this paper is based

on an independent analysis of each line phase. The ANN ob-

tains whether the analyzed phase is affected by the fault or not.

The magnitudes considered are the fundamental components of

voltage and current modules in that phase. These fundamental

componentshavebeenobtainedusingtheFFTfilteringtechnique.

The phase voltage and current values measured in a line ter-

minal during the fault, are expressed in per unit value relative

to the prefault situation (V/Vpf) and (I/Ipf). Thus, in the case

of two terminal single lines, the classification network has the

structure shown in Fig. 1.

By analyzing the values of these electrical parameters in faultsituations, represented in a (V/Vpf) versus (I/Ipf) diagram, two

Fig 1. Network structure for fault classification in single lines.

clearly differentiated areas are observed. The phases in fault sit-uation are located in onearea, while the sound phasesare located

in the other one. This allows to define a classification criterion.

Fig. 2 shows the voltage and current per-unit values for

the four fault types (single-phase faults, two-phase faults,

two-phase-to-earth faults and three-phase faults), in different

positions and with different fault resistances. The maximum

value considered of fault resistance has been 75 . These values

have been obtained for the single line La Lomba-Herrera.

In Fig. 2, it can be observed an area corresponding to the

phases affected by the fault (top left-hand area) and another cor-

responding to the sound phases (bottom right-hand area).

In this figure, a zoom has been included for single-phasefaults, two-phase faults, and two-phase-to-earth faults. In these

zooms, it can be seen that overlaps do not exist between zones

offault and no-fault situation. Thus, the current and voltage

values of each phase, relative to the prefault situation, are sup-

plied to the ANN. The ANN will indicate whether the phase is

in fault or in sound situation. This process is repeated for each

one of the three phases. Once the process has finished, it is pos-

sible to know the type of fault that occurred.

In double-circuit lines, the representation of (V/Vpf) versus

(I/Ipf) of different phases during the fault does not present two

areas clearly differentiated in contrast to single-circuit lines.

Overlaps between the areas appear due to the influence of one

circuit on the other. For this reason, another ANN has been sug-

gested for classification. This ANN considers phase currents and

voltages of the two circuits of the line (Fig. 3).

If these three parameters are considered as inputs, then a

three-dimensional (3-D) representation is possible. This repre-

sentation avoids the overlap between the areas that appear in

the 2-D representation. So each point of the diagram represents

clearly a fault or no fault situation.

B. ANN Input/Output Data

Examples for ANN training and verification in fault condi-

tions have been generated with a FALNEUR simulator [13].

Faults have been defined by:

fault distance: with . That is to say,

faults in 101 different positions.

fault resistance: , with . That is

to say, 76 different fault resistances for each fault position.

The neural networks have been trained selecting cases of this

group following two different criteria.

Training with random data. The network is trained with a

random sample of total generated cases (in our case 8% of

the total cases).

Training with frontier data. The network is trained with

cases corresponding to faults generated in 101 equidis-

tant positions of the line and maximum fault resistance( and %), and with cases corresponding

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GRACIA et al.: BEST ANN STRUCTURES FOR FAULT LOCATION 2391

Fig. 2. Fault voltages versus current values relative to the prefault state for different fault situations.

Fig 3. Network structure for fault classification in double-circuit lines.

to faults generated in the remote terminal of the line and

fault resistances varying between 0 and and

.

It was observed that the results using the first criterion were

good enough. So this criterion has been selected for training the

networks. In addition, it has been verified that the classification

network training can be carried out only with the data corre-

sponding to single-phase faults. This is because the single-phase

fault is the fault type for which the most severe conditions (over-

laps between areas) appear.

C. Application to Real Lines

Once network inputs were selected, the appropriate ANN

type for the fault classification problem was determined, as

well as the best network structure (number of layers, number of

neurons per layer, and activation functions).

In order to determine the characteristics of the optimal ANN

for fault classification, an exhaustive analysis was carried out

with SARENEUR application [15]. Due to the applied strategy(V/Vpf versus I/Ipf), the software tool SARENEUR showed that

the two types of ANN that seem more appropriate for fault clas-

sification are MLP and LVQ networks. The operation of these

ANN types was verified on a group of single- and double-cir-

cuit transmission lines. This way, the structures that fulfill the

conditions settled down by the user for training and verification

were selected after verifying that a great number of applicable

ANN structures exist.

The group of electrical transmission lines analyzed belong to

the Spanish electrical system and their main characteristics are

shown in Table I.

The analysis developed showed that either the MLP network

or the LVQ network are applicable for fault classification. How-

ever, the MLP network presents important advantages in front of

the LVQ one, such as smaller errors and a smaller training time.

This is mainly due to the rounding technique applied in the MLP

network to obtain discrete values, which eliminates most of the

errors.

Therefore, the structure selected is a MLP network that uses

backpropagation training with LevenbergMarquardt optimiza-

tion [17]. This algorithm reduces the training time, presenting

practically a null error and allowing to train with a random

sample of data that does not require previous preparation.

In the transmission lines analyzed, the best network training

times are lower than a minute. In some networks, this time did

not exceed 5 s. In the process of fault classification in singlelines, the optimal networks classified without committing any

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TABLE ICHARACTERISTICS OF THE TRANSMISSION LINES ANALYZED

TABLE IISTRUCTURES SELECTED FOR THE SINGLE LINES ANALYZED

error. In the case of double-circuit lines, the errors in classifica-

tion were below 1%.

1) Single Lines: The main conclusion obtained is that there

are many optimal networks that carry out the fault classifica-tion process in a correct way. Also, those structures that have a

linear activation function in the output layer present better be-

havior. Due to the good performance of MLP networks, simple

networks of two hidden layers and no more than six neurons per

layer were chosen. Even when a more reduced training time is

required, single hidden layer networks can be used.

In the analysis made on two terminals single transmission

lines, it was observed that training times were always lower than

a minute and, in some cases, lower than a second, without com-

mitting any error. These results were obtained in a AMD Athlon

900-MHz computer, with 128-Mb RAM.

The optimal classification network for each single line is

shown in Table II. In the selection of these ANN structures,different parameters have been considered, such as the ANN

size, the training time, and the errors in the results. The nomen-

clature used for the activation functions is the following:

linear function, called purelin ( );

limited sigmoid function between , called tansig

(T);

limited sigmoid function between , called logsig

(L).

The methodology applied for these three lines can be applied

to any line. For those new lines, the most appropriate network

structures is selected easily with SARENEUR.

2) Double-Circuit Lines: Carrying out the same process fordouble-circuit lines, the errors produced were always lower than

TABLE IIISTRUCTURES SELECTED FOR THE DOUBLE-CIRCUIT LINES ANALYZED

1% for the analyzed networks. These errors belong to faults pro-

duced with a fault resistance near to the maximum value (75 )

and in positions very far from the reference terminal.

There are not any significant differences in terms of more

suitable ANN topology, whenever they are MLP networks with

a linear output layer. In order not to increase the training time

unnecessarily, it is recommended to use networks with no morethan six neurons in the hidden layers. Under these conditions,

the training time is near a minute. Single hidden layer structures

can be used if speed is a decisive factor.

The structures selected for the fault classification process

in the double-circuit transmission lines analyzed are shown in

Table III.

A total of 23 028 cases were verified for each line. These cases

correspond to faults simulated in 101 positions, with 76 different

fault resistances, and for the three phases.

IV. ANN STRUCTURES FOR FAULT LOCATION IN SINGLE- AND

DOUBLE-CIRCUIT TRANSMISSION LINES

A. ANN Structure

For the fault location problem, fault voltages and currents in

per unit referred to prefault values, (V/Vpf) and (I/Ipf), have also

been considered. Therefore, the simulations made with FAL-

NEUR software for the classification process are also valid for

the location process. Besides as the value of current of the faulty

phase is very high, the logarithm of the fault current has been

used. This application of logarithms is not recommended in the

classification process in order to avoid overlaps between fault

and no fault zones.

For instance, the ANN proposed for fault location, for the

single-phase fault case, has the structure shown in Fig. 4.Due to the structure and characteristics of the problem to

solve, an MLP with backpropagation training algorithm and

LevenbergMarquardt optimization have been considered.

B. ANN Input/Output Data

The same examples generated in the classification step can

be used for training the selected network, although it has been

enough to train with those cases defined by the following pa-

rameters:

Fault distance: with (faults simu-

lated in 26 positions)

Fault resistance; with (faultssimulated with 19 different fault resistances).

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TABLE IVRESULTS OF THE VERIFICATION OF THE SELECTED FAULT-LOCATION NETWORKS

Fig 4. Example of ANN structure for fault location in case of single-phasefault (phase R).

In order to consider the errors associated with the measure-

ment equipment in fault situation, the location network is also

trained with cases affected by errors (up to 3% in the faulty

phases and up to 1% in the sound phases). The total training

cases are 988. These cases belong to simulations of the typefault determined in the classification process, located in 26 dif-

ferent positions, with 19 different fault resistances, and with and

without errors associated with the measurement equipment.

The verification hasbeen carried out using all of the generated

cases, with and without errors. The total number of cases used

in the verification is 15 352:

Fault distances: , with (faults

simulated in 101 positions).

Fault resistances: , with (faults

simulated with 76 different fault resistances).

C. Application to Real Lines

In the transmission lines shown in Table I, the networks with

better behavior have been selected. These networks have been

trained and verified following the criterion described previously,

obtaining the results shown in Table IV.

These networks have been taken from a greater group previ-

ously selected by SARENEUR. Due to the good behavior of the

MLP structure, there are many possible networks to use. As an

example, a group of 38 networks, selected by SARENEUR, for

the case of a single-phase fault in the single line La Lomba-

Compostilla are shown in Table V. The ANN considered the

best has been taken from this group and shown in Table IV (net-work LLP 6-6-3-2).

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TABLE VTRAINING PARAMETERS FOR THE SINGLE-PHASE FAULT LOCATION IN

THE LINE LA LOMBA-COMPOSTILLA

TABLE VI

ACTUAL FAULT PARAMETERS IN THE TRANSMISSION LINE

The analysis developed shows that to obtain an only best

ANN structure for all of the lines is not possible. Nevertheless,

a series of common characteristic to the best networks has been

observed:

two hidden layers with up 89 neurons in the first layer

and up 46 in the second one;

preferably LLP, LTP, TLP, or TTP topologies;

nonlinear activation function in the input layer;

linear activation function in the output layer.

Under these conditions, the best networks are able to train

in times from a few seconds to 3 min, depending on the fault

characteristics.

From Table IV, the average errors oscillate between 0.015%

and 0.4% in the fault location process and between 0.017% and

0.46% in the fault resistance calculation. The maximum error

varies between 0.09% and 3.77% in the fault-location process

and between 0.16% and 3.21% in the fault resistance calcula-

tion. Comparing these results with those obtained with tradi-

tional methods, we can see that these errors are really low. Be-sides, we should to keep in mind that the input data already con-

tain errors (3% in the faulty phases and 1% in the sound phases).

V. RESULTS

The global system operation was verified once the selected

ANN was trained and verified. In order to check it, several faults

provided by the Spanish utility IBERDROLA S.A. were ana-

lyzed. Thus, for the single transmission line La Lomba-Her-

rera, the faults indicated in Table VI were analyzed.

Faults were correctly classified with the selected structures

(Table II) in all of the cases. For the fault-location process, the

selected location networks (Table IV) gave the results shown inTable VII.

TABLE VIIPARAMETERS CALCULATED IN THE TRANSMISSION LINE LA

LOMBA-HERRERA

TABLE VIIIACTUAL FAULT PARAMETERS IN THE TRANSMISSION LINE

VILLARINO-VILLALCAMPO

TABLE IXPARAMETERS CALCULATED IN THE TRANSMISSION-LINE

VILLARINO-VILLALCAMPO

Also, faults whose characteristics are shown in Table VIII

were analyzed in the VillarinoVillalcampo double-circuit

transmission line.

In the case of the double-circuit line, faults were also classi-

fied and located correctly (Table IX).

VI. CONCLUSION

An ANN-based application for fault location in electricaltransmission lines has been presented in this paper. In the selec-

tion of the best ANN structures, a tool developed specifically

for this aim, called SARENEUR, has been used. This tool

allows to select the ANN that fulfills certain conditions or to

verify the operation of a specific network.

Because many structures that offer satisfactory results exist,

an only ANN optimal structure cannot be determined, as much

in fault classification as in fault location. Nevertheless, the best

networks present some common characteristics:

In the fault classification step, the networks should have linear

activation function in the output layer. Besides, in order not to

increase the training time, the networks should not have more

than two hidden layers and not more than six neurons per layer.In these conditions, the networks training time for single- and

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double-circuit lines is less than a minute. The classification er-

rors are null in single lines and smaller than 1% in double-circuit

lines. Single hidden layer structures can be used if speed is a de-

cisive factor.

In the fault-location step, the selected networks are character-

ized by two hidden layers (eight to nine neurons in thefirst layer

and four to six in the second), no-linear activation function inthe input layer and lineal activation function in the output layer,

with LLP, LTP, TLP, or TTP activation functions. Under these

conditions, the ANN trains in very small times and is always

lower than 3 min. The mean errors in the fault location oscillate

between 0.015% and 0.4%. In the fault resistance determination,

the mean errors change between 0.017% and 0.46%. If smaller

training times are required, a single hidden layer can be used.

The times have been obtained in a AMD Athlon 900-MHz

computer with 128-Mb RAM.

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J. Gracia was born in Zaragoza, Spain, in 1967. He received the electrical en-gineering degree from the University of Zaragoza, Zaragoza, in 1991. He iscurrently pursuing the Ph.D. degree at the University of the Basque Country,Bilbao, Spain.

Currently, he is with the Government of the Autonomous Community ofAragon, Zaragoza, Spain.

A. J. Mazon (M03) received the electrical engineering and Ph.D. degrees fromthe University of the Basque Country, Bilbao, Spain, in 1990 and 1994, respec-tively.

Currently, he is a Full Associate Professor in the Electrical Engineering

Department, University of the Basque Country. In 1992, he was with LabeinResearch Laboratories, Zamudio, Spain. His research activities include electricpower systems, transients simulation, fault analysis, and transmission-linethermal rating.

I. Zamora (M03) received the electrical engineering and Ph.D. degrees fromthe University of the Basque Country, Bilbao, Spain, in 1989 and 1993, respec-

tively.Currently, she is a Full Associate Professor in the Electrical Engineering De-

partment at the University of the Basque Country. Her research activities in-clude electric power systems, transients simulation, fault analysis, and trans-mission-line thermal rating.

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