Abstract – A comparison of results between two different methods of fault location for radial distribution networks with a single measurement point is presented. A parametric short circuit phasor simulation is conducted for three different distribution circuits with the same topology, to calculate the fault data at the measurement point for equally spaced faults. Fault distances are then calculated using a reactance algorithm and a support vector machine and the results compared. The results show the feasibility of using such methods as practical distribution systems fault diagnosis.Keywords – Fault location, distribution networks, nomographs, support vector machines.

Introduction.

The use of fault location techniques is a present requirement of the smart grid enabled distribution management systems. The management strategies used in such management systems call for the use of concepts like service continuity, quality of service, reliability etc in the modern interpretation of the role of the electric power systems which implies an improvement in the precision of the fault location methods used in distribution systems. Such fault location methods are absolutely necessary since they make the job of fault detection and elimination, easier and significantly decreases the time used for such tasks. .[1]

Several techniques like reactance algorithms, travelling wave, high frequency components, computational intelligence etc, have been used to solve the problem of fault location in electric power systems. [2] [3-8]. Most of these techniques were developed for power transmission lines because of their higher power levels, larger sizes, homogeneity, simpler topology, etc.

Transmission lines carry higher power levels than any other so their faults tend to have the worst, system wide, consequences and are usually responsible for affecting large amounts of users.

The distances in transmission lines range from several dozens to a few hundred kilometers, longer than the distances in any distribution lines.[5, 9-10]

Transmission lines are usually homogeneous, fully transposed, topologically simple etc making them especially suited for relatively simple and economic solutions in terms of measurement resources and computing power. Reactance

González Gómez Abel, works for Megger, LTD, in Toronto, Canada. (e-mail: abel.gonzalez@megger.com)

algorithms like that developed by Takagi et al[2] which can be used to obtain error levels under 2% whenever an adequate estimation of the current and voltage phasors is made are an example of the kind of relatively simple and economic solutions mentioned before. Other reactance algorithms have been developed that use measurement at both sides of the line provide a higher precision but are not so simple to implement and the cost of implementation is definitely higher. However their use is recommended whenever possible.

For distribution systems such general solutions are yet to be found. The reasons for the former are the following: [9, 11-12]

Distribution lines are usually non homogeneous. The topology of distribution lines is not simple which

creates great problems for fault location methods using data from only one line terminal.

The existence of variable loads between the fault locators or data registering equipment and the fault itself.

Fault locators or data registering equipment are usually not located directly on the lines but on the line feeders makes the evaluation of the different contributions to the fault even harder.

The relatively high influence of the fault resistance and the grounding method in distribution networks over the short circuit currents and voltages.

In most cases it is impossible to obtain fault data directly from the faulted branch of the circuit.

As opposed to transmission lines, distribution lines usually feed relatively small loads so the budget fraction devoted to measurement equipment in such lines is small. The consequence is that the equipment in such lines is usually limited to power meters, low spectrum overcurrent devices and reclosers, sectionalizers and fuses connected downstream.

As a result the most diverse and simple and ineffective techniques have been employed as a fault location method in distribution lines such as[11]:

Restoration through switching. Restoration through recloser operation. Indication through fuse and fault locator operation. Downed wires, customer calls, maps. Relay targets. DC thumping of underground circuits. Smelling burnt cables.

Abel González Gómez, member IEEE. Alexis Martínez del Sol, member IEEE.

A Comparison of Two Data Intensive Methods for Fault Location in Distribution Systems.

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However the power market deregulation that has opened the distribution market to different utilities which are now very interested in decreasing the downtimes associated to faults and increasing the reliability and the power quality as a distinguishing feature of their operation. As a result an increasing interest in fault location methods for distribution systems has occurred specially in the last decade.

The characteristics of distribution systems mentioned above have made a unified approach for the solution of the fault location problem very difficult. This is the reason for the great diversity of methods found in the literature to deal with this problem which is a computational intensive one when it is looked from the Substation Management system perspective. Such methods can be classified in the following groups:

1. Fault loop impedance calculation algorithms. Nomographs.

2. Statistical methods.3. Hybrid methods.4. Artificial intelligence methods e.g. neural networks,

neuro fuzzy systems, genetic algorithms.5. Matching.

The difficulties associated with the wide use of artificial intelligence methods in the past were usually associated to the computational complexity of the distribution network itself which made it very hard to obtain a training data set with high accuracy and which contains enough test cases. The improvements in the quality of the power system simulation tools, the increasing computational power and lower price of even desktop hardware capable of performing such simulations have put this tools within the grasp of the Distribution Management Systems making their use a real possibility. Also the use of computational intelligence methods has been limited in scope because of the inherent difficulty in the interpretation of the results.However, it is the perception of such methods that has limited its widespread use in utilities around the world. It has been the experience of the author that there is a generalized view in the utilities sector that artificial intelligence methods lie within the realm of the fantastic and that their place is in research labs or in the academic world.This paper shows a comparison between the results obtained by using two of the aforementioned approaches. First the results of fault location using nomographs is presented. Then the results of fault location using a combination of an artificial neural network ANN and a support vector machine, SVM. The simulation of several real cases of distribution networks is used to benchmark the results of the different methods used. Matlab/Simulinnk/Power Systems Blockset simulations of those networks are used to provide the training cases.

I. NOMOGRAPHS

The use of nomographs has been recommended for fault location on distribution lines.[11] A nomograph is defined as[11] “A graph that plots measured fault location versus actual fault location by compensating for known system errors.” The definition implies that a short circuit calculation program can be used to simulate faults along the lines and use

the resulting fault voltage and phasors to calculate the fault distance using some fault location algorithm.Then plotting the calculated fault distance versus the simulation parameter(the distance to the simulated fault) a nomograph can be created.[13] uses this approach to create a nomograph for a 69kV line. Such diagram is a straight line from the point of origin as can be expected for the topology of the line used for the study.For a radial distribution line with a different topology the results are different and no straight lines from start to end can be expected as will be seen.A pitfall of the use of nomographs is that changes in the system configuration change the form of the graph so for every configuration change a new set of nomographs has to be developed for the distribution system under study. This was a real challenge a few years ago when the computational burden imposed by short circuit calculation programs made it impossible to have a real time update of the influence of the network conditions in the short circuit reactance of the system for faults along the different lines. But the increasing power of today´s computers has made it possible to perform such calculations in a very short time even using general purpose programs such as Matlab.

II. REACTANCE GRAPHICS

Figure 1 Radial Distribution Network

For a radial network like the one in Figure 1 the reactance distribution as seen from point zero for equally spaced short circuits along the line is the one shown in Figures 2, 3 and 4. This reactance profile is the result of a parametric simulation of three circuits with the same topology and different impedances. The simulation was performed using SimPowerSystems and single phase, phase to phase, and three phase short circuits were simulated every 100m along each of the lines. 18000 different simulation cases were analyzed. Each run of the simulations took no more than a few minutes in a non-optimized software/hardware platform.

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Figure 2 Reactance vs Distance Mixed Line

Figure 2 shows that the radial distribution network used for the simulation has different reactance values for equal distances from the origin or equal reactance values for different distances from the origin. This is the reason why multiple distance values are obtained when reactance algorithms like Takagi´s are used to find the distance to the fault in this kind of circuits.Also the form of the reactance diagram changes for the following conditions:

Load changes. Distributed Generation insertion in different points in

the network. Changes in the network grounding system.

A circuit with the same topology but with different cables or lines will present a different reactance vs distance characteristic as can be seen in the following graphics.

Figure 3 Reactance vs Distance Aerial line.

Figure 4 Reactance Vs Distance underground Line

The reasons for the differences between the reactance vs distance curves can be attributed to the different impedances and line sizes shown in the following table.

III. TAKAGI ALGORITHM APPLICATION

The fault distance can be calculated using the Takagi algorithm. The following graphics show the fault distances calculated.

Figure 5 Fault Distance and Takagi CC3F. Aerial Line.

Figure 6 Fault Distance and Takagi CC3F. Mixed Line

Figure 7 Fault Distance and Takagi. CC3F underground line

The error from the application of the Takagi method for such cases can be seen in the following graphics:

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Figure 8 Error Vs distance.The fault location error from the application of the Takagi method to distribution lines is usually high and increments with the distance from the origin at which the fault is simulated. A more homogeneous line has a smoother error line which may indicate that a better parameterization of the algorithm could improve its performance. It is possible to use the incorrect results of Takagi´s algorithm even with high error levels whenever the precision levels are kept constant by using nomographs.

IV. USE OF NOMOGRAPHS.

Using Takagi´s algorithm results and plotting them vs the distance to the fault used in the simulations, to form a nomograph like the one defined in section II results in the following graphics:

Figure 9 Nomograph Aerial Line

Figure 10 Nomograph Mixed Line

Figure 11 Nomograph Underground Line

These graphics can be used, despite the errors of the application of Takagi´s algorithm to obtain an accurate assessment of the distance to the fault. If the result of the application of the Takagi algorithm is available for a given fault; the true location of the fault can be obtained by simply using the appropriate nomograph. Discerning between multiple location results can be made using more information from the network. Such tabular intersection methods can be easily implemented in digital relays or as a function of distribution management software.

V. ARTIFICIAL NEURAL NETWORKS AND SUPPORT VECTOR MACHINES.

The computational intelligence technique selected for this work was a combination of an artificial neural network and a support vector machine. In this case the support vector machine is used as the input to the system and provides the classification of the fault types to the ANN which provides the actual location.

A support vector machine is an automatic learning system that efficiently solves classification and regression problems and that has positioned itself above other computational intelligence techniques for the solution of classification problems where only a small amount of data is available. SVM’s are based on statistical learning theory and stand out in the solution of real problems being a robust tool in noisy and complex domains.

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SVM’s try to find the best solution by isolating the hyperplane that optimally separates the training patterns according to their classes. Such classes must have been previously mapped to a high dimensional space in order to minimize the structural risk. The success of the SVM is based on three elements:

1. They have a strong mathematical foundation.2. They are based on the concept of minimizing the

structural risk by minimizing the probability of making an erroneous classification on new test cases which is especially important when there are only a few training cases.

3. Disponen de potentes herramientas y algoritmos para hallar la solución de manera rápida y eficiente.

In power systems the SVM have been successfully employed in the following scenarios:

1. Transient stability analysis.2. Demand forecast3. Topology verification of the power system.4. Fault current discrimination and inrush current in

Transformers.5. Security evaluation in the power system.6. Adaptive stabilizers for power systems.7. Disturbance classification.8. Fault Location.

A more detailed description of the theoretical foundations of SVM can be found on [14]. For this work an implementation similar to that presented in [15] was used. This is not a new approach and was selected for the relative simplicity of implementation and for the stability of the results provided.The ANN-SVM was applied to the same problem as before; that is to the location of faults along the radial distribution network presented above.Several types of faults were simulated along the radial distribution network to provide both, the training cases and the test cases for the SVM. Such faults were generated at each of the inflexion points of the network’s topology. No high impedance faults were simulated as part of this study.To provide a better location the ANN-SVM needs to have as much data about the fault as possible. Such data can come from the classifier itself or can be provided to it by an external source. An example of such data is the fault type, i.e phase A, forward fault. The SVM part of the classifier is used to provide a classification of the type of power system faults. This is of extreme importance since similar values for currents and voltages can correspond to very different fault distances depending on the type of the fault. In the case of multiple branches, like in figure 1 the fault location is estimated to be on more than one branch of the feeder. To know the true fault location we need to know the statuses of the switches and fuses that are encountered along the path of the feeder.It is usual to present the results of fault location studies involving some kind of computational intelligence technique, like the SVM, in terms of the outputs of such classifiers. That approach is well suited when the objective of the work is to present the merits of the classifier. However, in this case the focus is on presenting the merits of the approach itself in terms

of the precision in the fault location which is the most important parameter for the user. So the results are presented merely in terms of error vs fault location along the line for different types of faults. 30 different fault places were selected along the lines.The data used to train the classifier was obtained from the same simulations used for the precedent case.

A. Results using the ANN-SVM:

The same network shown in Figure 1 was used as a test case scenario. Only results for the simulation of the aerial line are shown.

The lengths of the circuit segments from node to node, in km, is shown in Table 1below for comparison with the absolute errors in meters.

Table 1 Circuit segment lengths.

From To km0 1 101 1A 4.5

1A 1A1 11A 1A2 21 2 52 2A 3.52 3 83 3A 0.7

3A 3B 33A 3C 1.33 4 5

The simulated faults or test cases used for the verification of the results are located according to Table 2.Table 2 Fault locations used for verification.

Case. Location Case. Location1 0-1 30% 15 2A2 0-1 60% 16 2-3 30%3 1-1A 30% 17 2-3 60%

4 1-1A 60% 183-3A 30%

5 1A-1A2 30% 193-3A 60%

6 1A-1A2 60% 203A-3C 30%

7 1A2 213A-3C 60%

8 1A1-1A2 30% 22 3C

9 1A1-1A2 60% 233A-3B 30%

10 1A2 243A-3B 60%

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11 1-2 30% 25 3B12 1-2 60% 26 3-4 30%13 2-2A 30% 27 3-4 60%14 2-2A 60% 28 4

0 5 10 15 20 25 300

50

100

150

200

250

Phase to Ground Faults

Test Case

Erro

r in

met

ers

Figure 12 Location Error. Phase to Ground Faults.

0 5 10 15 20 25 300

20406080

100120140160180

Line to Line faults.

Test Case.

Erro

r in

met

ers.

Figure 13 Location Error. Line to Line Faults

0 2 4 6 8 10 120

2

4

6

8

10

12

3 Phase Faults

Test Case

Erro

r in

met

ers

Figure 14 Location Error. 3 Phase FaultsThe results indicate that the neural network is capable of following the topology of the line and get better location results at the beginning of the line as expected. Better results are obtained for phase faults than for single phase to ground faults.

VI. CONCLUSIONS

The direct applicability of reactance algorithms to distribution networks is restricted to low complexity systems.

The multiple fault location estimation is a problem that cannot be addressed using single terminal reactance methods for distribution networks unless additional information of the network is available.

The use of nomographs for the location of faults in distribution networks with acceptable error levels despite de errors of the reactance algorithms from which such graphs are derived is possible whenever the precision level of the reactance method used is kept constant.

The use of SVM’s has proved to be an effective tool for the location of faults in distribution systems. However, the application of such methods to the problem of fault location has been limited by factors such as, ease of use, availability of usable training data, lack of computing power and lack of confidence on their effectiveness.

It is the believe of the authors that this methods should be revisited in the light of the new technologies now available and that they will play an important role in the management of electrical networks in the near future.

VII. BIBLIOGRAPHY

[1] Y. Zhang and M. Prica, "Toward Smarter Current Relays for Power Grids," in IEEE PES General Meetin, Montreal, Canada, 2006, p. 8.

[2] T. Takagi, et al., "Development of a New Type Fault Locator Using the One Terminal Fault-Voltage and Current Data," IEEE Proceedings on Power apparatus and Systems, vol. PAS 101, p. 7, August, 1982 1982.

[3] D. Novosel, et al., "Unsynchronized two-terminal fault location estimation," IEEE Trans. on Power Delivery, pp. 130-138, 1996.

[4] M. M. Saha, et al., "Review of fault location techniques for distribution systems," presented at the Power Systems and Communications Infrastructures for the Future, Beijing, 2002.

[3] D. Novosel, et al., "Unsynchronized two-terminal fault location estimation," IEEE Trans. on Power Delivery, pp. 130-138, 1996.

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[4] M. M. Saha, et al., "Review of fault location techniques for distribution systems," presented at the Power Systems and Communications Infrastructures for the Future, Beijing, 2002.

[5] T. Bouthiba. Fault location in ehv transmission lines using artificial neural networks.

[6] A. S. Bretas, et al., "A BP neural network based technique for HIF detection and location on distribution systems with distributed generation," presented at the Proceedings of the 2006 international conference on Intelligent computing: Part II, Kunming, China, 2006.

[7] M. Kezunović, et al., "A Novel Method for Transmission Network Fault Location Using Genetic Algorithms and Sparse Field Recordings," in IEEE PES Summer Meeting, ed, 2002.

[8] S. Vasilic and M. Kezunovic, "New Design of a Neural Network Algorithm for Detecting and Classifying Transmission Line Faults," in IEEE PES Transmission and Distribution Conf, ed, 2001.

[9] M. M. Saha, et al., Fault Location on Power Networks, 1st ed. London: Springer, 2010.

[10] A. Gopalakrishnan, et al., "Fault Location Using Distributed Parameter Transmission Line Model," IEEE Trans. on Power Delivery, pp. 1169-1174, 2000.

[11] IEEE, "C37.114-2004 - IEEE Guide for Determining Fault Location on AC Transmission and Distribution Lines," ed, 2004.

[12] J. J. Mora Flores, "Localización de faltas en sistemas de distribución de energía eléctrica usando metodos basados en el modelo y métodos basados en el conocimiento," PhD Doctoral, Escola Politécnica Superior, Universitat de Girona, Girona, 2006.

[13] Creating Nomographs Using the SEL-Profile Transmission Line Fault Analysis Program. Available: http://www.selinc.com.cn/ResourceFolder/Articlefiles/2008-5/1358618.PDF

[14] Lipo Wang. Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing). Springer. 2005.

[15] Thukaram, D et al. Artificial Neural Network and Support Vector Machine Approach for Locating Faults in Radial Distribution Systems. IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 20, NO. 2, APRIL 2005.

[16] J. J. Mora Flores, Classification methodology and feature selection to assist fault location in power distribution systems. Rev. Fac. Ing. Univ. Antioquia N.° 44. pp. 83-96. Junio, 2008. Available: http://jaibana.udea.edu.co/grupos/revista/revistas/nro044/09rev_44.pdf

Biographies

Abel González Gómez, Received his bachelor in Electrical Engineering and his MSc in Electrical Engineering from the Universidad Central de Las Villas, Cuba in 1996 and 2000. From 1996 to 2000 Worked as an Assistant professor for the Faculty of Electrical Engineering at the Universidad Central de Las Villas, Cuba, from 2000 to 2010 as a Teletraffic Engineer, Control Engineer and Head of the Marketing Department for the Cuban

Telecommunications Company and a professor of Marketing for the Universidad Central de Las Villas, Cuba. From 2010 to 2013 worked as a Design Engineer for Arteche Medicion y Tecnologia in Zapopan, Jalisco, Mexico. From 2013 works as an applications Engineer for Megger, LTD in Markham, Ontario. His research areas are the analysis operation, control and protection of electric power systems and the application of artificial intelligence and soft computing techniques.

Alexis Martínez del Sol Received his PhD in Electrical Engineering at the Universidad Central de Las Villas, Cuba in 1997. From 1987 to 1999 worked at the Electrical Engineering Faculty of the Universidad Central de Las Villas as an Assistant professor, Head of the Electrical Engineering Department and Member of the Scientific Council.

From 1999 to 2000 was a Research Professor at the Department of Electro-Mechanical Engineering of the Universidad de Guadalajara. From 2005 on he works for the Arteche Group. His research areas are the analysis operation, control and protection of electric power systems and the development of

application software. He was a member of the Mexican National Researchers System(Level I)

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