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IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 21, NO. 3, JULY 2006
Failure Risk Prediction Using Artificial Neural
Networks for Lightning Surge Protection of
Underground MV Cables
Ángel L. Orille-Fernández, Member, IEEE, Nabil Khalil, and Santiago Bogarra Rodríguez
Abstract—Lightning surge is actually being considered as one
of the most dangerous events in power distribution systems. Basically, it hits the overhead distribution line then propagates to the
other network components, such as underground cables and transformers. Due to lightning strokes, insulation failure of such components could occur. The failure risk can be determined on the
basis of network configuration, its parameters, and surge arresters
data. The determination of this index can greatly help in optimizing
the network surge protection. The implementation of an artificial
neural network (ANN) for prediction of the failure risk for underground medium-voltage cables connected to overhead distribution
lines is introduced. The main advantage of ANN actually is the time
and effort savings due to the random nature of the problem and extended calculation process. The calculation of the failure risk using
ANN is applied to a group of industrial surge arresters. The results
of the ANN test coincide with the analytical ones.
Index Terms—Artificial neural networks (ANNs), Electromagnetic Transients Program (EMTP)/Alternative Transients
Program (ATP), lightning surges, risk analysis, surge protection
arresters.
I. INTRODUCTION
O
UTAGES of overhead distribution lines and the underground cables connected directly to them due to lightning
are caused by either direct or nearby strokes. Several studies on
lightning-induced voltage on distribution systems [1] show that
this voltage can be elevated within the cable at its ends. The
aalysis of surge effects in distribution networks has enabled us
to assert that lightning surges are generally the most dangerous
kind of surges [2].
The surge arrester rating selection depends on the type of the
cable to be protected and its insulation level, its length, and its
far-end termination (i.e., open circuit or loaded). The degree that
a certain cable can withstand lightning strokes is called failure
risk [3].
The lightning overvoltages generated on a distribution line
are randomly variable. These values of overvoltages depend directly on other random values, such as the impact point of the
lightning stroke and the lightning stroke characteristics which
Manuscript received April 8, 2005. Paper no. TPWRD-00206-2005.
A. L. Orille-Fernández and S. Bogarra Rodríguez are with the Department
of Electrical Engineering, Polytechnic University of Catalonia, Barcelona
E-08028, Spain (e-mail: orille@ee.upc.edu; bogarra@ee.upc.edu).
N. Khalil is with the Department of Power Engineering and Electrical Machines, Faculty of Engineering, University of Helwan, Cairo, Egypt (e-mail:
dr_nabil_khalil@link.net).
Digital Object Identifier 10.1109/TPWRD.2006.874643
can be randomly varied from the points of view of amplitude,
duration, and steepness.
In order to calculate the failure risk, all of the previously mentioned random factors must be taken into consideration. This
will lead to a complex work for the engineer to make a decision
about the surge arrester ratings, the cable insulation level, or the
maximum length of a cable to be protected or, in other words,
stay within a certain range of failure risk. These problems led to
the use of artificial intelligence in order to save time and effort
for decision making. In previous works [4], [5], fuzzy logic was
used efficiently to determine the optimum distance for lightning
arrester location within a certain cable run.
In this study, another type of artificial intelligence (i.e.,
ANN) will be introduced to surge protection studies. The basic
target is to predict the failure risk to a range of under ground
medium-voltage cables rated from 3 to 30 kV, considering the
cables’ different insulation levels. The determination of this
factor will be calculated depending on the data of the lightning
arresters located at the line-cable junction. Also, the value of
lightning surge current will be randomly variable and it can be
directed or induced to the line. The ANN can be used directly
to evaluate the failure risk for a certain network or indirectly to
decide which type of lightning arrester, maximum cable length,
or cable insulation level should be used to meet a certain failure
risk.
In order to go through this, the Electromagnetic Transients
Program/Alternative Transients Program (EMTP/ATP) will be
used to simulate a certain sample power system to obtain a database to train and test the ANN. The lightning surges current
values, their probabilities of existing and failure risk are determined according to Monte Carlo method. Sample mediumvoltage surge arrester data are taken from the GE Tranquell
product catalog.
II. FAILURE RISK
The failure risk of a network component due to lightning
stroke presents the probability that the lightning surge exceeds
the withstand voltage [6]. The statistical distribution of lightning overvoltages at the network nodes depends on independent
random variables, such as the peak value of the return stroke current, the slope of the wavefront, the impact point of the lightning
stroke, etc.; especially the maximum intensity (peak value) of
the return stroke current [2] (Fig. 1).
While we do not have a known function in order to obtain
the statistical distribution of lightning overvoltages, we have the
0885-8977/$20.00 © 2006 IEEE
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ORILLE et al.: FAILURE RISK PREDICTION USING ARTIFICIAL NEURAL NETWORKS
1279
Fig. 2. Failure risk of a network component.
Fig. 3.
Simulated sample power system.
Fig. 1. Statistical distribution of the peak value of the return stroke.
EMTP/ATP which allows us to generate the statistical distribution of lightning overvoltages in the network nodes.
In order to determine the values of lightning overvoltages at
the network nodes, we need to use a procedure offering random
values of independent variables, of which the statistical distributions are known. The procedure considered most convenient
is the Monte Carlo method [7]. It is assumed that lightning overvoltage distribution is the Gaussian density function
%
(1)
where
is the probability density of overvoltage occurrence,
is
% the overvoltage for which the probability density
of occurrence is 50%, and is the standard deviation.
It is assumed that the probability of disruptive discharge of insulation is given by a Gaussian cumulative probability function
%
(2)
is the probability of disruptive discharge;
where
% is
the voltage under which the insulation has a 50% probability to
flashover or to withstand; and is the standard deviation.
The failure risk of a network component (Fig. 2) is calculated
by taking the distribution of applied overvoltages together with
the distribution of its withstand voltages and is expressed as [3]
Fig. 4. Surge arrester model.
consists of a distribution line connected to an underground
medium-voltage cable. A surge arrester is located at the line
cable junction. The surge arrester is connected to ground via a
in series with an inductance
. The resistance
resistance
simulates the grounding wire and ground resistance, while the
inductance is the grounding wire inductance. The distribution
line is considered to be infinitely long. The cable is considered
to be open circuited at the load end.
The lightning surge is simulated using a dual exponential current source that injects current in the distribution line 15 m away
from the line cable junction. The lightning arrester is simulated
using the simplified model [8] of the IEEE model [9]. Fig. 4
shows the electric equivalent circuit of a surge arrester. In this
model, the lightning arrester is modeled using two nonlinear resistances A0 and A1 and two inductances L0 and L1. L0 and L1
are calculated according to the following equations [8] on the
base of 10-kA current surge:
(4)
(5)
(3)
where is the failure risk;
overvoltage occurrence; and
tive discharge.
is the probability density of
is the probability of disrup-
III. POWER SYSTEM MODELING
In order to train the ANN, a sample power system will be
simulated to generate the necessary input– output training
patterns. The power system sample shown in Fig. 3 will
be simulated using the Electromagnetic Transients Program
(EMTP)/Alternative Transients Program (ATP). The system
where
is the residual voltage at 10-kA fast front current
s) or (
s),
is the residual voltage at 10-kA
curve (
s), and Vn is the arrester rated
fast front current curve (
voltage.
A resistance with a value of 1 M is located across the
terminal of the surge arrester.
The current source simulates the direct lightning surge current or the induced current in the line caused by nearby surge.
The distribution line and cable characteristic impedances and
propagation speeds are 350 , 300 m/ s, 50 and 150 m/ s,
respectively.
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IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 21, NO. 3, JULY 2006
IV. ANN DESIGN, TRAINING, AND TESTING
Fig. 5. Application of the electrogeometric model.
The electrogeometric model is used to predict the probability
of the lightning to hit the distribution line. The induced magnetic field caused by the lightning surge depends directly on the
distance between the object and the lightning surge itself. Fig. 5
explains this phenomenon [10].
The distance between an object in this study is the distribution line or the earth, and the end of the main discharge can be
calculated as follows:
(6)
is the impact distance in meters, and is the lightning
where
surge current.
The direct impact of the lightning surge to the distribution
line will generate a current wave. This wave will split into to
equal parts, each one travels in a certain direction. Otherwise, if
the surge hit the ground, it will induce a certain voltage on the
line which, in turn, will induce a current wave which has a value
depending on the line parameters.
The induction atmospheric overvoltages are generated from
lightning surges impact near power lines. This overvoltage is
a function of the maximum and duration of the surge current,
the principal velocity of the primary discharge, the power line
height, and the distance between the lightning surge impact and
the power line.
The induced overvoltage can be calculated according to
Rusck [10]
(7)
where
lightning surge current in kiloamperes;
induced overvoltage on the line in kilovolts;
height of the power line in meters;
distance between the line and the power line in meters;
propagation velocity of the surge in the media under
study in kilometers per second;
speed of light in space in kilometers/s.
The ANN used in this work is a feedforward neural network
that has eight inputs and five outputs. The ANN inputs are the
normalized values of the following:
1) the network nominal voltage (from 3-to-30-kV line to
line);
2) network maximum permissible voltage (120% of the rated
voltage);
3) cable maximum impulse withstand voltage;
4) surge arrester rated voltage (from 3-to-36-kV line to
ground);
5) surge arrester rated current (5, 10, 20, 40 kA);
6) surge arrester ground wire inductance (1.25 H/m and
tower height from 8 to 14 m);
7) ground resistance plus grounding cable resistance one between 1 to 10 ;
8) protected cable length (from 15 to 150 m).
All of these values are normalized by their maximum values.
The ANN output is the risk factor to be predicted. In order
increase the accuracy of the ANN output, the ANN will have
five outputs. The risk factor will be codified in the following
manner: If the risk factor is R and O1, O2, O3, O4, and O5 are
the outputs then
, the
1) if
, and
;
, the
2) if
, and
;
, the
3) if
, and
;
, the
4) if
, and
;
, the
5) if
, and
.
The segmoidal activation function used in the different neurons
of the ANN has the following formula:
(8)
where is the neuron activation function input; is the activation function output, and is the slope factor.
Note that each neuron has a minimum output of
and a
because the symmetrical segmoidal function is
maximum of
used. An inverse for the above five cases is applied to translate
the ANN output to failure risk. However, the output of the ANN
or . It is found that the segmoidal
is real numbers, not only
, 0.33 for the
functions of the first hidden layer are
second hidden layer, and 0.4 for the output layer in order to
decrease the output error.
The ANN training patterns are generated by simulating the
power system mentioned in Fig. 3, applying random values of
s and a peak value from
the lightning stroke current of
100 A to 400 kA with different values of surge arrester characteristic voltages according to Table I [11].
The network voltages, the corresponding rating values of the
lighting arresters, and the corresponding minimum and maximum insulation levels according to the Electric Network of
Spain are shown in Table II [12].
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ORILLE et al.: FAILURE RISK PREDICTION USING ARTIFICIAL NEURAL NETWORKS
1281
TABLE I
GE TRANQUELL HEAVY DUTY ARRESTERS
Fig. 7. Simulation and test results for a failure risk factor between 0.4 and 0.6.
TABLE II
SURGE ARRESTERS-RATED VALUE, MINIMUM AND MAXIMUM IMPULSE
WITHSTAND VOLTAGES AS A FUNCTION OF RATED NETWORK VOLTAGE
Fig. 8. Simulation and test results for a failure risk factor between 0.4 and 0.5.
Fig. 6.
Simulation and test results for a failure risk factor greater than 0.6.
The designed ANN has eight inputs, ten neurons in each if
the two hidden layers, and five output neurons. The testing of
the ANN is performed with other groups of data differing from
the training ones. The test results can be summarized as follows.
1) Fig. 6 shows the value of the risk of the failure index calculated from simulation results and ANN outputs in cases
where the risk of failure is greater than 0.6. These cases
correspond to a network voltage of 3 kV and lightning arrester of 6 kV with 20 and 40 kA.
2) Fig. 7 shows the same comparison where the failure risk is
between 0.4 and 0.6. This situation corresponds to cable
voltage that is equal to or more than 6 kV and extreme conditions of cable length of more than 130 m, surge arrester
maximum voltage rating, and highest current capacity.
Fig. 9. Simulation and test results for a failure risk factor between 0.2 and 0.4.
3) Fig. 8 shows the simulation result and ANN output in
cases where the failure risk is between 0.4 and 0.5. This is
for cables of 10 kV and over, and a maximum cable length
of 75 m with a surge arrester capacity of 5 kA to 20 kA.
4) Fig. 9 shows the simulation results and ANN outputs in
cases where the failure risk is between 0.2 and 0.4. This
is for cables of 15 kV and over with a maximum cables’
impulse withstand voltage.
The ANN mentioned before can be used to decide the optimal
surge arrester ratings, the cable’s length, or its insulation level
to meet a certain failure risk. This can be done by changing the
input values and observe the failure risk.
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IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 21, NO. 3, JULY 2006
V. CONCLUSION
To help in surge protection and power system reliability
studies and to save time for the calculation of the failure risk of
medium-voltage underground cables, ANNs were introduced.
The obtained values of the ANN in this field encourage its
use for prediction of failure risk and other surge protection-related subjects. The special design of the ANN output and the
selection of the segmoidal function parameters enhanced its
analogue output values and reduced its error. Moreover, the
ANN succeeded in covering most of the possible cases along
with the usage of real parameter of existing surge arresters.
The developed ANN training patterns used the statistical
random values of lightning parameters. The statistical distribution of these parameters values were generated using the
Monte Carlo method. The statistical distribution of lightning
overvoltages is obtained from surges calculated by EMTP/ATP.
REFERENCES
[1] S. Bogarra, “Overvoltage Restriction in Power Systems,” Ph.D. dissertation, Dept. Elect. Eng., Polytechnic Univ. Catalonia, Barcelona, Spain,
2001.
[2] B. De Metz-Noblat, Lightning and HV electrical installations, in
Schneider Electric, no. 168, 1998. Tech. Library.
[3] IEEE Guide for the Application of Metal Oxide Surge Arrester for Alternating-Current Systems, 1991. IEEE C62.22.
[4] A. L. Orille, S. Bogarra, M. A. Grau, and J. Iglesias, “Fuzzy logic techniques to limit lightning surges in a power transformer,” in Proc. IEEE
Power Tech Conf., vol. 2, Bologna, Italy, Jun. 23–26, 2003.
[5] A. L. Orille, S. Bogarra, M. A. Grau, and J. Iglesias, “Lightning protection of power systems using fuzzy logic techniques,” in Proc. 12th IEEE
Int. Conf. Fuzzy Systems, May 25–28, 2003, pp. 1406–1411.
[6] Insulation Coordination. Part II: Application Guide, 1992. IEC 71.2.
[7] G. J. Anders, Probability Concepts in Electric Power Systems. New
York: Wiley, 1990.
[8] P. Piniceti and M. Giannettoni, “A simplified model for zinc oxide surge
arrester,” IEEE Trans. Power Del., vol. 14, no. 2, pp. 393–396, Apr. 1999.
[9] IEEE Working Group Surge Protective Devices Committee , “Modeling
of metal oxide surge arresters,” IEEE Trans. Power Del., vol. 7, no. 1,
pp. 301–309, Jan. 1992.
[10] P. Chowdhuri, Electromagnetic Transients in Power Systems. Somerset, U.K.: Research Studies, 1996.
[11] GE Tranquell Surge Arresters, Product and Application Guide, 2000.
[12] Overhead High Voltage Lines Standard, Dec. 1968. B.O.E. 27 of.
Ángel L. Orille-Fernández (M’95) was born in Ujo,
Spain, on June 21, 1946. He received the Ph.D. degree in electrical engineering from the Polytechnic
University of Catalonia, Barcelona, Spain, in 1988.
Currently, he is Professor of Electrical Engineering
with the Polytechnic University of Catalonia, where
he has been since 1989. He was Head of the Department of Electrical Engineering from 1995 to 2000.
Nabil Khalil was born in Cairo, Egypt, on October
9, 1965. He received the B.Sc. and M.Sc. degrees in
power engineering and electrical machines from the
University of Helwan, Cairo, Egypt, and the Ph.D.
degree in electrical engineering from the Polytechnic
University of Catalonia, Barcelona, Spain, in 1999.
Currently, he is with the Power Engineering
and Electrical Machines Department, University
of Helwan. His interests are artificial intelligence,
power system studies, and power system protection.
Santiago Bogarra Rodríguez was born in Gavá,
Spain, on May 8, 1966. He received the Ph.D.
degree in electrical engineering from the Polytechnic
University of Catalonia, Barcelona, Spain, in 2002.
Currently, he is Associate Professor of Electrical
Engineering with the Polytechnic University of
Catalonia.
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