1728 IEEE Transactions on Power Delivery, Vol. 11, No. 4, October 1996

ALGORITHMS FOR LOCATING FAULTS ON SERIES COMPENSATED LINES USING NEURAL NETWORK AND DETERMINISTIC METHODS

Damir Novosel Bernhard Bachmann David Hart ABB - TTI. USA

Abstract - This paper investigates a scheme to improve the reach measurement of distance relays and fault locators for series compensated lines. A deterministic method and a feedforward neural network method have been implemented for on-line calculation of the voltage across a non-linear capacitor installation. These techniques are compared and incorporated into a new relaying scheme which is independent of the series capacitor installation, operation of the capacitor protection, and the surrounding power system elements. The proposed scheme is simple and accurate and requires only local voltage and current at the bus. Detailed testing using EMTP has been done to show the benefits of the new adaptive scheme. The results demonstrate the suitability of the techniques for real world applications.

1. Introduction Benefits of installing series capacitors in the power system include increased power transfer capability, improved power system stability, reduced system losses, improved voltage regulation, and regulation of power flows. Protection of systems with series compensated lines [1,2,3] is considered one of the most difficult tasks for relay manufacturers and utility engineers. Protection and control of surrounding circuit elements, particularly transmission line protection, needs to be adapted to the variations introduced by these devices.

Problems caused by voltage inversion and current inversion can be solved by existing protection schemes [1,2,3,4]. The reach measurement problem for the fault detection and fault location applications are difficult to solve with the conventional approaches. The distance relay or a fault locator reach measurement depends on the status of the capacitor and the transient response of the capacitor protection circuit. If the capacitor is protected by a non- linear MOV (Metal-Oxide Varistor) element, the Capacitor compensation is a function of the fault location. Thus, due to the varying amount of capacitance in the circuit, distance relays may misoperate and fault locators may not be able to accurately determine the fault location.

96 WM 021-6 PWRD A paper recommended and approved by the IEEE Power System Relaying Committee of the IEEE Power Engineering Society for presentation at the 1996 IEEEiPES Winter Meeting, January 21-25, 1996, Baltimore, MD. Manuscript submitted July 25, 1995; made available for printing December 8, 1995.

Yi Hu Murari Mohan Saha ABB Relays AB, Sweden

Application of digital technology allows for modifications to be made on-line to improve the network protection and control in the presence of the controllable and non- controllable devices. For the fault detection applications it is not required to detect the exact fault location and these problems can be overcome as shown in [4]. However, accurate modeling of the capacitor installation is required for the fault location application. The appropriate on-line modeling of the non-linear electrical behavior of such devices can be achieved either by the simulation of the devices' behavior directly using a deterministic approach or by using artificial intelligence technology.

The linearized model described in [SI can be used to accommodate the relay setting based on the MOV conduction [2]. This model represents the MOV and the capacitor as a non-linear resistor in series with a reactance and is less accurate than the proposed techniques. Artificial intelligence tools have also been used to improve fault detection and fault location for the series compensated lines [6,7]1. In both references, the ANN has been trained with a number of EMTP cases that simulate different fault conditions in a selected network. The AI" models a response of the capacitor installation and the selected network to these conditions. The designed ANNs have been trained and tested with the same power network. This approach is sensitive to a specific installation and surrounding power network conditions. The real-world application on the ANN technique will depend on it's ability to generalize and provide a robust response to the various network conditions and networks. A new ANN method is considered in this paper to investigate possible benefits over existing and new deterministic methods.

The main objective of the work presented in this paper is to develop a method for estimating the voltage across the non-linear series capacitor (capacitor and MOV) using the locally available line current. This allows for a transfer of the voltage across the capacitor. The new calculated voltage can be used with any of the existing impedance measurement and fault location techniques. Compensation for the non-linear behavior of the series capacitor installation has been accomplished with the voltage transfer and this approach is not sensitive to the network where the capacitor is installed. A linearized model (LM) [ 5 ] , a Deterministic Differential Approach (DDA), and an Artificial Neural Network (ANN) will be investigated and compared in this paper. The selected simulation systems feature the detailed modeling of transmission systems, the

0885-8977/96/$05.00 0 1996 IEEE

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t

V(t) =-OX, j I , ( t ) dt (2) 4

where V(t) is the voltage across the capacitor and I,(t) is the current through the capacitor. The current I,(t) is the difference between the line current I(t) and the current IHOV(t) through the MOV. Therefore, (2) can be reformulated

capacitor, and the capacitor protection circuit, which is essential to properly test the protection algorithms and strategies.

2. Series Capacitor Model The simplified capacitor protection scheme [8] is shown in Figure 2-1. The MOV starts conducting immediately after the instantaneous voltage across the capacitor exceeds a certain voltage level V,f. The VI-characteristic of an MOV can be approximated by the familiar single exponential model:

I,, and V,l are defined in Figure 2-1. The value of a is typically chosen from 30 to 50. The MOV is a resistive device which absorbs energy and is protected from overheating by an overload protection. The overload protection calculates the energy developed through the MOV and initiates the gap to flash (by-passing the MOV). The protection settings used are defined in Figure 2- 1. In addition, a high-current function is provided to speed-up by-passing for severe internal faults if the current is larger than I,,,=.

Series capacitor

By-pass switch

*d Sparkgap

Protection (Imax)

X, - I,, - I, - maximum load current IMOV - MOV current Ipl - protective current level of MOV:

V , - protective (peak) voltage level of MOV:

Figure 2-1 Capacitor Protection Scheme

The capacitor protection must be modeled in the protection scheme to accurately transfer the voltage across the capacitor. The voltage and current relationship for the capacitor is defined by

reactance of the capacitor current that causes the gap to flash

I,I = I,*k , k = 2-3

V,, = 1.414*&*Ipl

t

V(t> = -wx, j(ICS- I,o,(t))dt (3 )

By using the modeled VI-Characteristic (1) and normalizing (3), it follows

-

From (4), the voltage and current relationship at the capacitor can also be given by the ordinary differential equation

(5 )

The variables are defined in Figure 2- 1. Equations (4) and (5 ) show that the voltage across the capacitor is a nonlinear function of the local line current only.

3. Description of Algorithms Deterministic methods (Linear Model (LM) and Deterministic Differential Approach (DDA)) and an Artificial Neural Network (ANN) method have been investigated in the paper. The linear model calculates an equivalent impedance of the capacitor installation by using a phasor approach. The DDA and the ANN techniques estimate the voltage across the capacitor in the time domain. This voltage i s used to calculate the line side voltage of the capacitor by summing the measured bus voltage and the voltage across the capacitor. The calculated line side voltage can be used with any of the conventional relays or fault locators.

A new impedance measurement algorithm (DIF) has been developed for use with the new voltage transfer techniques to reduce the impact of the subsynchronous frequency and other transients. This algorithm is based on the least- squares method solution of the line differential equation using sampled data.

3.1. Deterministic approaches Linear Model (LM) - The linearized model [5 ] represents the MOV as a non-linear resistor in parallel with the

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capacitor. The equivalent series connection ZCM0v = kMov+i o XcMOV is given by:

XcMOV = X,(.101-.005749 I,, + 2.088e-.85h61p" 1 I

for I,, =-> .98. I PI

I,, is the per unit current, and I, IpI, and X, are defined in Figure 2-1. Equations in (6) only apply when the capacitor current exceeds 98% of the protective level current. Otherwise, RcMOV= 0 and XcMOV=&. In addition when the overload protection operates, the capacitor is by-passed and kMOV=XcMOV=O" A recursive DFT method has been used for the phasor calculation of the line current. Because of the appropriate normalization, the linearized model is independent of a specific capacitor installation.

Deterministic Differential Approach (DDA) - Implicit as well as explicit numerical schemes for integrating the ordinary differential equation (5) have been investigated. Implicit algorithms require considerable computational work for each time step due to the nonlinear VI- characteristic of the MOV, and may have convergence problems. However, it allows for larger time steps. Numerical experiments show that explicit schemes are preferred, because the required smaller time steps are not as time consuming as the solution process of the nonlinear equations. In order to discretize (5) an explicit Euler scheme has been used.

The exponential VI-characteristic of the MOV causes the integration of (5) to be very sensitive and unstable for large time steps. If the sampling rate is smaller than 64 samples per cycle, the explicit schemes are not able to integrate (5). An appropriate interpolation technique has been chosen to accommodate the required integration step to the practical sampling rate of the relay. To accelerate the computation of the algorithm, a piece-wise linear approximation technique is used to represent the non-linear MOV characteristic.

Equation ( 5 ) only holds until the MOV overload protection operates the bypass breaker. However, the voltage at the MOV and the current through the MOV are calculated at each time step using the linearized VI-characteristic. These calculations allow for the estimate of the energy calculation and prediction of the capacitor by-pass time. This approach has been implemented in combination with the DDA method.

3.2. Artificial Neural Network (ANN) approach The feedforward neural network with a quasi-Newton method for minimization of the error function [9] has been

implemented to achieve an appropriate model of the capacitor and its protection scheme. The normalized voltage-current dependency in (4) can be considered as independent of the specific parameters of the capacitor installation. This provides the capability to construct a neural network model that can be used for various applications: different compensation levels, different capacitor protection installations, and power network configurations.

The feedforward neural network consists of three layers (an input layer, a hidden layer, and an output layer). The input layer has 10 inputs (the normalized line current samples and the differences of the line current to approximate the derivative). The number of inputs and the considered time interval were chosen independent of the sampling rate implemented in the protection device. Let n be the number of samples per cycle (e.g. 16, 32, 64, etc.), m = nJ8, and I k

= I(tk) (normalized by Ipl). The ten inputs sj used to predict the output 0 1 = Vk=V(tk) (normalized by V,,) are:

SI = Ik 3 s2 = Ik - Ik.m, s3 = Ik.m, s4 = 1k.m - Ik.2 m, s5 = Ik.2 m,

s6 = Ik-2m - Ik-3 m, s7 = Ik-3 m, s8 = Ik-3 m - Ik-4 m, s9 = Ik-4 my SI0 = Ik-4 m - Ik-5 m.

By this definition the information used for prediction corresponds to the time interval of half a cycle, and the minimum sampling rate is 8. The number of hidden units has been varied from 4 to 10 and the best performance is achieved with 9 hidden units. Direct connections between the input layer and the output layer have also been defined for a better learning of the linear dependency of the inpugoutput relationship. For the activation functions of the hidden units the sigmoid function seems to be appropriate. The thresholds were modeled as additional weights. The above inputs and the output were normalized to mean value 0 and standard deviation 1. The ANN has been trained on a power system 1 (Figure 4-1) [IO]. The training cases were generated for different fault inception angles (O', 90°, 180°, 270°), locations (10, 30, 50, 70 and 90%) and types (a-G, bc, bc-G and abc). The load flow direction was from S to R.

As with the deterministic technique, an estimation of the energy calculation and prediction of the by-pass time is calculated at each time step using the linearized VI- characteristic.

3.3, Impedance measurement algorithm (DIF) Based on the differential equation representation of the transmission line in conjunction with a least-square estimate method the following- impedance measurement technique (DIF) has been used with the DDA or ANN techniques. Since any other impedance technique can be used, the DIF technique was not investigated separately. The faulted line i s modeled as a series R-E circuit where

(7) dt

For this approach the dc-offset and the subsynchronous frequencies are not an error signal, since they also satisfy the differential equation. Because measurements are made of V(t) and I(t), an approximation of (7) can obtained by integrating over 2 successive time periods using the trapezoidal rule to calculate R and L to the fault. However, this algorithm is very sensitive on signal disturbances, e.g., measurement errors and transients. In this paper, a least squares method has been used for stabilizing the algorithm. Assuming that R and L are constant in time and by using several time intervals, the following relationship holds:

Ax = b, with r 1

L _I

v, + v,, b = [ 1, w h e r e V , = V ( t , ) a n d I , = I ( t n )

The least square estimate described by (8) with a half cycle data window is used to calculate R and L to the fault. A sampling rate of 16 sampleskycle and a low-pass anti- aliasing filter are used in the study.

4. Comparison and Simulations This section presents the simulation results for two different series compensated systems: a single-line network (Figure 4-1) and a parallel-line network (Figure 4-2). In addition, for the single line network, three different network configurations have been modeled and simulated. System 1 (Figure 4-1) has a single capacitor (60% compensation) on a single line. The line with the capacitor is an untransposed line with a flat line configuration (unbalanced network). System 2 has the same configuration as in 1, but with the reverse load flow. System 3 also has the same configuration as in 1, but with the capacitor out of service (shorted). System 4 (Figure 4- 2) has parallel lines with capacitors (60% compensation) at one end of each line. The lines with the capacitors are in a delta configuration (network is balanced).

The detailed modeling of the series capacitor's protection and the MOV overload protection has been implemented with the same criterion as existing real-world installations. The EMTP Electromagnetic Transient Program was used to simulate the power system voltages and currents. The generated waveforms are loaded into MATLAB for testing of the relaying algorithms [IO].

Load flow direction bur H

X,= 120.62ohm generators - busG I I Y - Y .<"SA I

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, gsneraIorR

I h LOAD LOAD

25 MILES

I I- 25 MILES ,* 200 MILES-

Figure 4-1 Single line system (simulation system 1) X,= 109.32 ohm

Load flow direction X.y X,'60% I generator R = 65 59 ohm bus , bus G

generators

ii

LOA0 25 MILES

I I- 25 MILES ,+ ZOOMILES - LOAD

Figure 4-2 Parallel line system (simulation system 4)

Comprehensive simulations have been performed to evaluate the linear model (LM). Figure 4-3 (a-G fault at 10% of the line, simulation system 1 - Figure 4-1) demonstrate the error of the linear model and comparison with the DDA and ANN methods. The calculations with the LM are dependent on the phasor calculation of the normalized current Ipu~ One cycle after the fault inception the error was app. 10%. In addition, the graph shows that the fault location estimation is incorrect after by-pass.

half cycle after fault inception

Figure 4-3 Comparison of the LM, DDA, and ANN methods for an a-G fault at 10% of the line

The linearized model is simple and may be accurate enough for the fault detection applications. The disadvantages of the linearized model are: (i) less accurate than the other tested methods, (ii) poor performance during transition periods (pre-fault to fault, pre-bypass to bypass), and (iii) difficult to estimate bypass time which is based on energy accumulation of MOV. Thus, the following section describes detailed results with the DDA and ANN techniques only.

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The following cases were selected for each system: a-G fault (0 and 10 ohm fault resistance) and b-c fault; fault locations at lo%, 50%, 80% and 100% of the G-H line; fault inception angles of O', 45' and 90'. Tables below indicate: the exact fault location; fault inception angle; first estimate (half cycle after the fault inception), last estimate (two cycles after the first value), and average and standard deviation for the values between. The DIF measurement algorithm has been used with both DDA and ANN techniques. A half cycle after fault inception, the proposed protection scheme already provides accurate results.

As in conventional relays, there is no compensation in the scheme for errors caused by the unbalanced line model, charging currents, mutual coupling, and reactance effect. Thus, errors increase as the faults are further from the protection installation. These errors should be compensated for the fault location application [ l l ] . The capacitor installation has no considerable effect on the compensation methods because the calculated line side voltage is used. In addition, the DDA and ANN methods for the modeling of the capacitor installation are not affected by the error sources above.

Different fault locations, inception angles, and fault resistance for simulation system 1 - Simulation results for system 1 are summarized in Table 4-1 (no fault resistance) and Table 4-2 (10 ohm fault resistance). The simulation cases are different than the cases used for the ANN training to test a generalization capability of the ANN. The results for the DDA and the ANN technique are listed for comparison. The impedance trajectories are shown in Figure 4-4 for the DDA. The protection is set at 80% of the line. The results agree very well for both techniques and the voltage transfer has not been influenced by the different fault locations, inception angles, and fault resistance. The initial data window after the fault contains pre-fault data causing the travel of the impedance. However, in practice, a transient fault detector would start the data window after fault inception with the trajectories not crossing the operating zone for the faults outside the zone.

Table 4-1 Estimated fault location for different fault locations and inception angles (R=O a)

When the overload protection has operated, the last value may be in error if the energy calculation has not been accurate. An error in the timing of the overload protection operation will cause a considerable error in the estimate of the voltage across the capacitor installation. The average and the standard deviation values shown in every table are a quantitative indicator of these problems. The calculation of the voltage across the capacitor installation and, consequently, the energy estimation are more accurate when using the DDA technique.

Table 4-2 Estimated fault location for different fault locations and inception angles (

fault at IO%, 50%, 80% & 100% of system 1 (DDI

X(ohms)

R(ohms)

Figure 4-4 Impedance measurement at different fault

Reverse load flow (system 2 ) - The ANN was trained for one load flow direction (from S to R) only. Theoretically, a neural network should be trained for all possible conditions to be able to generalize properly. Thus, training cases with different load direction are needed if the load flow direction impacts results. The reverse load case tests the robustness and the generalization capability of the ANN approach. The results summarized in Table 4-3 reveal that there i s a certain impact of the prefault load flow direction on the ANN technique. It is also shown by comparing actual and estimated voltages across the capacitor (V,.,) and on the line side of the capacitor VI,,,^.^) (Figure 4-5). The impact is mostly in the transition period from the prefault to the fault condition. However, as soon as the

locations (DDA technique)

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L _.. .._ ...,. ...,. ~ , . . . . . . ~ ~

50 0 48.3 47.8 55 1 55.1 48.7+/- 3.5 51 5 t/-4.0 45 46.8 45.4 54.6 103 49.4+/- 3.5 75.0+/-21.7 90 46.6 45.3 54.3 102 48 .742 .9 68.1tf-27.5

80 0 x2 763 x1.s x9.s 7Y 3 +/- I x x2.0+/- 6.6

data window moves into the fault condition, the prefault load flow direction will have very little impact on the network performance. The DDA technique does not experience this problem.

100

Table 4-3 Estimated fault location for different fault locations and inception angles (R = 0 S Z , reverse load flow)

YO 74.6 74 4) X4 XX.3 7Y.5 TI- 2.0 X I . ? T/- 1 I) 0 118.8 111.91 1M.R 132.4 1303d-6.8 131 4+/. 14.6

45 97.1 92.91 1289 122.8. f26.3 +/- 9.7 l2Y.Y +/- 16.0 90 7 9 4 81.31 126.4 123.2 121.0+/-14.1 125.7+/.17.1

5 reverse load flow case (ANN) x 10

Vline.

0 0.02 0.04 006 0.08 0.1 0 I2 -6 I 14

actual: - eshmated: - - time (sec.)

Figure 4-5 Voltage transfer under reverse load flow directions for the ANN technique (fault at 50% of the line)

Parallel line system - The techniques have also been simulated on a parallel line system (system 4 in Figure 4- 2). The ANN trained for the single line system is used in the simulations. These simulations further investigate the generalization capability of the ANN. The capacitor value and the MOV characteristic have about 10% difference compared with the single line system (system 1). The results have shown that the error of the ANN technique is relatively small for the simulations on the parallel line system. The normalized approach used in the ANN training has resulted in a robust network, having a good generalization of the non-linearities of the capacitor-MOV installation. The results for the DDA and ANN techniques are listed in Table 4-4. Large errors for the faults at the end of the line are not result of the errors in the voltage transfer but are caused by the other errors sources (charging currents, mutual coupling, etc.). These errors, as previously

described, could be overcome for the fault location applications by the existing methods [ 111.

I 101 01 91 12.31 10.21 10.21 9.3 +/-0.71 10.4+/-2.51

Table 4-4 Estimated fault location for different fault locations and inception angles (R= 0 SZ, forward load flow, parallel line system)

Capacitor not in service or in the fault loop - When the capacitor is not in the fault loop, there is no need to transfer the voltage. This occurs when the capacitor is not in service or a fault is between the relay and the capacitor (capacitor located in the middle or at the end of the line). If the voltage transfer from bus side to line side is still used under this condition, an erroneous impedance measurement will result. The above problem will not affect the fault location estimation since there is enough time to detect the capacitor status or the capacitor status may be available. On the other hand, if information on the capacitor status is not provided to the relay in a timely manner, the relay will underreach. The relay reach of the proposed scheme is still higher than if the conventional scheme is used and will not cause misoperation of the relay.

If the capacitor is located at the end of the line and the line voltage is available to the relay, the voltage transfer is not needed. Unfortunately, in practice, the line side PT may not be installed. Also, the directional element with the line side voltage may fail if appropriate measures are not used.

Capacitor in the middle of the line - If the fault is on the far side of the capacitor installation, the proposed methods, with modification, can be applied. The modification consists of calculating the voltage drop between the relay and the capacitor to accurately transfer the voltage across the capacitor. If the fault is on the near side of the installation, the capacitor is not in the fault loop and the discussion in the previous paragraph applies.

5. Conclusions This paper describes a new scheme to improve the reach measurement of distance relays and fault locators applied in series compensated lines. This scheme consists of the following:

0 A method for estimating the voltage across the series capacitor using only the measured line current. This

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part can be accomplished by a deterministic method or an Artificial Neural Network (ANN) method. A new impedance algorithm (DIF), based on the least- square method solution of the line differential equation to reduce subsynchronous resonance oscillations and other transient phenomena. A method for calculation of the energy accumulation to predict the overload protection operation.

0

0

The results of this paper can be summarized as follows:

The deterministic and ANN methods provide an accurate modeling of the non-linearity of any series capacitor installation. This allows for on-line adjustment to be made in order to improve the reach measurement of a fault locator or a distance relay. Simulations on different networks have shown that both methods are very accurate and have a robust response to the changes in the system conditions and parameters of the capacitor installation. However, the first technique provides more accurate results.

0 A prediction of the voltage across the capacitor installation is in error if the capacitor status is not available. This problem should not affect the fault location estimation since there is enough time to detect the capacitor status. Also, this is not a major problem for the relay application since the relay will underreach. However, relay reach is still higher than if the conventional scheme is used. The proposed scheme i s simple, fast, accurate and reliable. It requires only local voltage and current at the bus. It is well suited for implementation in a digital fault locator or a digital distance relay to improve their performance. For the fault location application, the scheme needs to be combined with the existing fault location techniques using data either from one or from both ends to overcome other error sources (reactance effect, mutual coupling, charging currents, etc.) [ I I].

6. References 1. CIGRE, Group 34, “Application Guide on

Protection of Complex Transmission Networks”, May 1991.

2. F. Andersson and W. Elmore, “Overview of Series-Compensated Line Protection Philosophies”, Western Relay Protective Conference, Washington State University, Spokane, Washington, October 1990. W. J. Cheetham, A Newbould, and G. Stranne, “Series-Compensated Line Protection: System Modeling and Test Results”, I f h Annual Western Relay Protective Conference, Washington State University, Spokane, Washington, October 1988. C. Ohlen, et. al., “EMTP used in testing of a protection scheme for series compensated

3.

4.

5.

6.

7.

8.

9.

10.

11.

7.

network,” CIGRE 1995 SC 34 colloquium, Stockholm, June 1995. D. L. Goldsworthy, “A Linearized Model for MOV-Protected Series Capacitors”, IEEE Transactions on Power Systems, Vol. 2, No. 4, pp. 953-958, November 1987. Q.Y.Xuan, et. al., “Adaptive Protection for Series Compensated EHV Transmission Systems Using Neural Networks”, IEE Control Conference ‘94, 21-24 March 1994. Y.H. Song, “Accurate Fault Location Scheme Based on Neural Networks Applied to EHV Transmission Systems”, ICPST ‘94 Beijing, China, 1994. IEEE PSRC, WG K13, “Series Capacitor Bank Protection”, March 1994. J.A. Hertz, R.G. Palmer, and A S . Krogh, “Introduction to the Theory of Neural Computation”, Addision Weseley, 350 Bridge Parkway, Redwood City, CA 94065, USA, 1991. D. Hart, et. al., “Digital Techniques for Testing Numerical Relays,” Proceedings of the Stockholm Power Tech Conference, Stockholm, June 1995. D. Novosel, et. al., “Fault Location Using Digital Relay Data,” Computer Applications in Power, Volume 8, Number 3, July 1995.

Biographies Damir Novosel i s presently employed as an Advisory Engineer at the ABB Transmission Technology Institute in Raleigh, Ne. He received his Ph.D. from Mississippi State University in 1991. His research area is computer based protection and control and application of AI. He is a Senior Member of IEEE. Bernhard Bachmann is employed as a Research Engineer at the ABB Corporate Research Center in Baden-Daettwil, Switzerland. He received his Ph.D. from the University of Zurich, Switzerland in 1994. His research areas are numerical analysis, scientific computation, and AI tools. David Hart is presently employed as a Fellow Engineer at the ABB Transmission Technology Institute in Raleigh, NC. He received his Ph.D. from Clemson University in 1991. His research area is digital protection and control. Yi Hu is presently employed as a Senior Engineer at the ABB Transmission Technology Institute in Raleigh, NC. He received his Ph.D. from University of Manitoba, Canada in 1994. His research area is digital protection and control. Murari Saha is presently employed as a Senior R&D Engineer at ABB Relays, Sweden. He received his Ph.D from the Technical University of Warsaw, Poland in 1975. He is a Senior Member of IEEE, a Member of IEE, registered Euro Engineer and a Chartered Engineer. His areas of interest are measuring transformers? power system analysis and simulation, and digital protective relays.

Discussion

Adly A. Girgis and Srinivas Varadan, Clemson University :

The authors are to bc commended for their efforts in dealing with the protection of series compensated lines. The paper is well written and marks clarity in both meaning and understanding of the subject. The comparison of the two methods, DDA and ANN, investigated in this paper demonstrates that the DDA approach provides better results. This is due to fact that any ANN scheme is most effective when the functional mapping between the input and output is not clearly defined or known apriori. Typically, during the training process, a neural network extracts the inherent relationship between the input and output. It is this ability of a neural network that makes its use so attractive. In this case however, the terminal relationship between the current and voltage across the series capacitor is well established, eq. (5). It therefore seems intuitive that the DDA approach would be more effective in this problem of locating the fault distance on a series compensated line. Needless to say the ANN approach requires significant training time and fine tuning as far as network topology is concerned. The authors indicated that the computation of the voltage across the capacitor is used to calculate the line side voltage. It appears that the authors assumed that the fault is on the far side of the capacitor. Also they assumed that this is known apriori. The authors' response to the above comments will be highly appreciated.

Manuscript received February 12, 1996.

R.J. MARTTILA, (Ontario Hydro Technologies, Toronto, Ontario, Canada): The authors have presented an interesting paper on measurement techniques for determining the location of faults on series- compensated transmission lines.

The presentation concentrates on transmission lines with compensation at line ends, in which case the approaches apply only when the potential source is on the bus side of the capacitor. In many installations, the potential source is on the line side for various reasons. Also, distance relays appear to be more secure with the potential source on the line side [A]. It would be of interest to demonstrate the performance of the methods on lines with the capacitor at the midpoint of the line. In this application, what criteria would be used to decide the requirement to include or exclude the voltage drop across the capacitor/MOV combination?

[A] R.J. Marttila, "Performance of Distance Relay MHO Elements on

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MOV- Pro t ec t ed S erie s- Compensated Transmission Lines," IEEE Tran. on Power Delivery, Vol. 7, No. 3, Jul 1992, pp 1167-1 178.

Manuscript received February 20, 1996.

A. Apostolov (Tasnet, Inc., 5271 102nd Ave. N, Pinellas Park, FL 34666, USA):

The authors are to be commended for a very interesting paper on Locating Faults on Series Compensated Lines. The answers to the following questions will clarify some aspects of the practical application of the proposed method:

The authors state that both single-phase-to-ground and phase-to-phase faults were considered in the EMTP simulation and results. However, it is not clear if the results given in Tables 4-1 through 4-4 are from one of these two types of faults or the average from both. Was the Neural Network trained separately with data for each type of fault, or a combined set was used ?

If the line has a series capacitor in the middle, there is no way to determine from the relay location if the fault is in front or behind the capacitor. What is the proposed solution to this problem ?

The algorithm has bcen tested on a model of a 100 miles long line. Both the DDA and ANN estimate the fault location at 100 % with an error of up to 15 - 17 % for different inception angles, no mutual coupling and zero fault resistance. It will be beneficial if the authors can provide some comments on these results.

Manuscript received February 21, 1996.

D. Novosel, B. Bachmann, D.G. Hart, Y. Hu, and M.M. Saha: The authors wish to thank discussers for their interest in the new scheme for locating faults on series compensated lines.

All discussers are interested in application of the proposed scheme for the capacitor located in the middle of the line. For the case of a fault on the near side of the capacitor (the capacitor is not in the fault loop), voltage transfer is not required. If the capacitor is not in the fault loop and the new scheme is used, the relay will underreach. However, this will not cause any misoperations of the relay and the reach is higher than if the conventional scheme is used. For the fault location application, it is necessary to detect if the capacitor is in the fault loop (or that the fault is behind the capacitor) to transfer the voltage. Since fault location does not have to be determined in real time, enough time is available to detect if the voltage drop should be included or excluded. Two possible methods of determining if the capacitor is in the fault loop are:

I . Use of the largest low frequency transient together with the fundamental frequency.

2 . Use of differences in the impedance trajectory for the cases when the capacitor is in or out of the fault loop.

Mr. Marttila points out some benefits of installing the potential source on the bus side of the capacitor located at the line end. Certainly, in this case, voltage transfer is not needed. However, in a number of field installations, only bus side

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potential is available. Furthermore, the discusser points out that distance relays will be more secure with the potential source on the line side [A]. This is true for the measurement units of the distance relay, however, directional units (e.g. positive, negative, or zero sequence directional units) of the distance relay are more secure with the potential on the bus side.

Answers to other questions presented by Dr. Apostolov are as follows:

1. The results in the Tables 4-1 to 4-4 are for the single- phase-to-ground faults only. Errors in fault location estimation are usually largest for this fault type (zero sequence effects, highest value of the fault resistance, etc.) Tests on a model of a 100 mile long single line with no fault resistance have shown that the error of both algorithms (DDA and ANN) increases if the faults are further from the protection installation. The line is modeled in EMTP as an untransposed line with a flat-line configuration. Effects of coupling between the lines and charging currents are not modeled in the relay or fault locator. Thus, a reason for the error (up to 1517% for the

2.

line more accurately for the fault location applications, these errors can be reduced [ 1 11.

Dr. Girgis and Mr. Varadan correctly stated that “it seems intuitive that the DDA approach would be more effective” than the ANN approach. The authors have started the parallel development using these two approaches. Initially, it was not clear that the non-linearities (MOV) and overload protection in the capacitor installation could be modeled accurately in a relay using the deterministic approach. In addition, even when the accurate deterministic models have been developed, it seemed useful to compare the accuracy of two solutions and select the better one.

Again, the authors wish to thank the discusser for their comments.

REFERENCE

C1. D. Novosel, D.G. Hart, E. Udren, and M.M. Saha, ”Fault Location Using Digital Relay Data,“ IEEE ComDuter Aplications in Power, Volume 8, Number 3, June 1995.

fault at the remote bus) is that the line model in a relay is a simple positive sequence impedance. By modeling the Manuscript received April 2, 1996.