Double Circuit Transmission Line Fault Distance Location using Artificial Neural Network
Anamika Jain and A.S.Thoke
R. N. Patel
Department of Electrical Engineering National Institute of Technology Raipur, India
[email protected],
[email protected]
Department of Electrical and Electronics Engineering S. S. College of Engg. and Technology Bhilai, India
[email protected]
Abstract—Distance relays used for protection of transmission lines have problems of under-reach, over-reach and maloperation due to high impedance faults. Further the problem is compounded when the distance relays are used for protection of double circuit transmission lines due to effect of zero sequence mutual coupling. Different types of faults on a protected transmission line should be located correctly. This paper presents a single neural network for fault distance location for all the ten types of faults (3 LG, 3 LLG, 3 LL, 1 LLL) in both the circuits of a double circuit transmission line fed from sources at both the end. This technique uses only one end data and accurate fault distance location is achieved after one cycle from the inception of fault. The proposed Artificial Neural Network (ANN) based Fault Distance Locator uses fundamental components of three phase current signals of both the circuits & three phase voltage signals to learn the hidden relationship in the input patterns. An improved performance is obtained once the neural network is trained suitably, thus performing correctly when faced with different system parameters and conditions i.e. varying fault type, fault location, fault resistance, fault inception angle, presence of mutual coupling and remote source infeed. Keywords- Artificial Neural Network; Double circuit fault distance location; Mutual coupling; Transmission line; High impedance fault
I.
INTRODUCTION
Fault location estimation is a desirable feature in any protective relaying scheme for transmission lines. By accurately locating a fault, the amount of time spent by line repair crews in searching for the fault can be kept at a minimum. Locating the fault on the transmission line accelerates line restoration & maintains system stability. Different types of algorithms for finding fault location on EHV/UHV transmission lines have been developed and proposed over the years. These algorithms may be broadly classified as (i) those computing power frequency current and voltage phasors to find impedance and hence fault location [1, 2], (ii) those using differential equations of line and estimating line parameters [3] and (iii) those based on the traveling wave which uses one terminal data or two terminal data [4]-[5]. Most of the reactance-based schemes suffer from under reach due to high impedance fault and
over reach due to DC offset current. Also these schemes require remote end information if the transmission line is fed from sources at both the ends. These are required to minimize the errors due to the remote end infeed and load flow variations. Travelling wave schemes have problems with faults close to the bus and faults with close-to-zero fault inception angle. One of the new tools recently introduced into power system protection is Artificial Neural Networks (ANN). ANN is powerful in pattern recognition, classification and generalization. Neural Networks are useful for power system applications because they can be trained with off-line data. ANNs possess excellent features such as noise immunity, robustness and fault tolerance. Therefore, the decision made by an ANN-based relay will not be seriously affected by variations in system parameters. Consequently, various ANN-based algorithms have been investigated and implemented in power systems in recent years [6]. Faulty phase selection and distance location using neural network for single circuit transmission lines has been reported in [7]. Fault classification for double-circuit lines using selforganization mapping feature neural network is presented in [8], however it does not locate the faults. The work presented in [9] deals with the compensation of fault resistance using ANN for determination of location of fault. A single line to ground fault location method employing wavelet fuzzy neural network in the distribution lines of an industrial system is proposed in [10], other types of fault have not been considered. An adaptive distance protection of double circuit line using zero sequence thevenin equivalent impedance and compensation factor for mutual coupling is presented in [11]. Back propagation method based on Levenberg-Marquardt optimisation technique is used to locate the faults in [12]. In a companion paper [13], fault distance location for single line to ground faults on double circuit transmission lines using neural network has been reported. This paper presents an application of artificial neural network for fault distance location in a double end fed double circuit transmission line for all the ten types of shunt faults using only one terminal data considering the effects of mutual coupling, remote source infeed, varying fault type, fault location, fault resistance and fault inception angle. The
algorithm employs the fundamental components of three phase voltages and the six phase currents of the two parallel lines at one end only. The performance of the proposed scheme has been investigated by a number of offline tests. The simulation results show that all the ten type of faults can be correctly located after one cycle from the inception of fault. The algorithm is immune to effects of mutual coupling, remote source infeed, variation in fault type, fault location, fault resistance and fault inception angle. The technique does not require communication link to retrieve the remote end data and nor zero sequence current compensation for healthy phases. Such comprehensive work has not been reported earlier for fault distance location of double circuit line. II.
POWER SYSTEM NETWORK SIMULATION
The system studied is composed of 220KV double circuit transmission line 100 km in length, connected to sources at each end; its single line diagram is shown in Fig. 1 [13]. Short circuit capacity of the equivalent thevenin sources on two sides of the line is considered to be 1.25 GVA., Xs/Rs ratio of source is 10. The transmission line is simulated using distributed parameter line model using MATALB® 7.01 software as shown in Fig. 2. Double circuit transmission line parameters are shown in Table I.
Figure 1. Single line diagram of power system under study.
TABLE I.
DOUBLE CIRCUIT LINE PARAMETERS
Positive sequence resistance R1, Ω/KM Zero sequence resistance R0, Ω/KM Zero sequence mutual resistance R0m, Ω/KM Positive sequence inductance L1, H/KM Zero sequence inductance L0, H/KM Zero sequence mutual inductance L0m, H/KM Positive sequence capacitance C1, F/KM Zero sequence capacitance C0, F/KM Zero sequence mutual capacitance C0m, F/KM
0.01809 0.2188 0.20052 0.00092974 0.0032829 0.0020802 1.2571e-008 7.8555e-009 -2.0444e-009
For example a double phase to ground fault ‘A1B1N’double phase to ground fault occurs at 10 KM from end S on circuit 1 of the configuration shown in Fig.1 at 60 ms with 0Ω (zero) fault resistance and Φi=0° (zero degree) fault inception angle and δs= 45°, pre-fault power flow angle. The three phase voltage and current waveforms is shown in Fig. 3. As expected, a current is also induced in the ‘A2 & B2’phases of healthy circuit 2 due to zero sequence mutual
coupling between the two circuits. We can see that DC offset current is also present in the phase current signals. Due to this the conventional relays detect the healthy phases also to be faulty and may thus mal-operate. This is not desirable, only the faulted circuit should be disconnected while power continues to flow through the healthy circuit with reduced power transfer. Further, when high fault resistance is involved in ground faults at far end of the line, the conventional distance relays under-reach, and over-reach due to the effect of weakly damped DC offset current and/or remote end source infeed to fault branch. Preprocessing is useful method that significantly reduces the size of the neural network and improves the performance and speed of training process. Three phase voltage and six current input signals were sampled at a sampling frequency of 1 kHz and further processed by simple 2nd-order low-pass Butterworth filter with cut-off frequency of 400 Hz. Subsequently, one full cycle Discrete Fourier transform is used to calculate the fundamental components of voltages and currents. The input signals were normalized in order to make the ANN input level (±1) [14]. III.
ANN BASED FAULT DISTANCE LOCATOR
The basic procedure used to implement a neural network in the fault distance location algorithm in double circuit transmission line is described below. A. Selecting the right architecture One factor in determining the right size and structure for the network is the number of inputs and outputs that it must have. The lower the number of inputs, the smaller the network can be. However, sufficient input data to characterize the problem must be ensured. Only the magnitudes recorded at one end of the line are used. The inputs to distance relays are mainly the voltages and currents. Hence the network inputs chosen here are the magnitudes of the fundamental components (50 Hz) of three phase voltages and six currents measured at the relay location. As the basic task of fault location is to determine the distance to the fault, the distance to the fault, in km with regard to the total length of the line, should be the only output provided by the fault location network. Thus the input X and the output Y for the fault location network are:
X = [Va ,Vb ,Vc , I a1 , I b1 , I c1 , I a 2 , I b 2 , I c 2 ]
[ ]
Y = Lf
(1)
(2) The major issue in the design of ANN architecture is to ensure that when choosing the number of hidden layers and number of neurons in the hidden layers, its attribute for generalization is well maintained. In this respect, since there is no parametric/theoretic guidance available, the design has to be based on a heuristic approach [15].
Phase Currents of Healthy ckt-2 (Amp.)
Phase Currents of Faulty ckt-1 (Amp.)
3 Phase Phase Voltage (Volts)
Figure 2. Power system model simulated in MATLAB® 7.5 Simulink software.
Figure 3.
5
2
x 10
Ref. Va Vb Vc
1 0 -1 -2
0
20
40
60
80
100
120
140 150 Ref. Ia1 Ib1 Ic1
5000
0
-5000
0
20
40
60
80
100
120
140 150
1500 1000 500 0 -500 -1000 -1500
Ref. Ia2 Ib2 Ic2
0
20
40
60
80 Time in ms
100
120
140 150
Three phase voltages & currents during double phase to ground fault on ckt-1 at 10 KM from SS-1 end at 60 ms, Rf=0Ω, Φi=0° and δs= 45°.
The ANN architecture, including the number of inputs to the network and the number of neurons in hidden layers, is determined empirically by experimenting with various network configurations. Through a series of trial and error, and modifications of the ANN architecture, the best performance is achieved by using a three layer neural network with 9 inputs and 1 output as shown in Fig. 4. The number of neurons for the hidden layer is 40. The final determination of the neural network requires the relevant transfer functions in the layers to be established. After analysing the various possible combinations of transfer functions normally used, such as logsig, tansig and linear
functions, the tansig function was chosen as transfer function for the hidden layer, and pure linear function “purelin” in the output layer. IW{1,1}
LW{2,1}
b{1}
b{2}
9 Inputs
40 Neurons
1 Output
Figure 4. Structure of ANN based fault distance locator.
B. Learning rule selection The back-propagation learning rule is used in perhaps 80–90% of practical applications. Improvement techniques can be used to make back-propagation more reliable and faster. The back-propagation learning rule can be used to adjust the weights and biases of networks to minimize the sum-squared error of the network. This is done by continually changing the values of the network weights and biases in the direction of steepest descent with respect to error. The simple back-propagation method is slow because it requires small learning rates for stable learning, improvement techniques such as momentum and adaptive learning rate or an alternative method to gradient descent, Levenberg–Marquardt optimisation, can be used. Various techniques were applied to the different network architectures, and it was concluded that the most suitable training method for the architecture selected was based on the Levenberg–Marquardt (Trainlm) optimization technique. C. Training process To train the network, a suitable number of representative examples of the relevant phenomenon must be selected so that the network can learn the fundamental characteristics of the problem and, once training is completed, provide correct outputs in new situations not used during training. To obtain enough examples to train the network, a software package MATLAB® 7.01 is used. Using SIMULINK & SIMPOWERSYSTEM toolbox of MATLAB all the ten types of fault at different fault locations between 0-100% of line length and fault inception angles 0 & 90° have been simulated as shown below in Table II. The total number of ground faults simulated are 12x10x2x3 = 720 & 8x10x2 = 160 thus total fault cases are 880 and from each fault cases ten numbers of post fault samples have been extracted to form the training data set for neural network. Thus the total number of patterns generated for training are 8800 patterns. TABLE II.
TRAINING PATTERNS GENERATION
Parameter
Fault type
Set value LG: A1N, A2N, B1N, B2N, C1N, C2N LL: A1B1, A2B2, B1C1, B2C2, A1C1, A2C2 LLG: A1B1N, A2B2N, B1C1N, B2C2N, A1C1N, A2C2N LLL: A1B1C1, A2B2C2
Fault location (Lf in KM)
1, 10, 20, 30, …80 and 90 km
Fault inception angle (Φi)
0° & 90°
Fault resistance (Rf)
0, 50 & 100 Ω
Pre-fault power flow angle (δs)
45˚
The ANN based fault distance locator was trained using Levenberg–Marquardt training algorithm using neural network toolbox of Matlab. This learning strategy converges quickly and the mean squared error (mse) decreases in 300
Figure 5. Training of ANN based fault distance locator for all the ten types of faults in both the circuits of transmission line.
epochs to 4.66127e-04 in around half an hour time as shown in Fig. 5. Once the network is trained sufficiently and suitably with large training data sets, the network gives the correct output after one cycle from the inception of fault. IV.
TEST RESULT OF ANN BASED FAULT DISTANCE LOCATOR
Once training was completed, ANN based Fault distance locator was then extensively tested using independent data sets consisting of fault scenarios never used previously in training. For different faults of the validation/test data set, fault type, fault location, fault resistance and fault inception angle were changed to investigate the effects of these factors on the performance of the proposed algorithm. The network was tested and performance was validated by presenting all the ten types of fault cases with varying fault locations (Lf=0-90KM), fault resistances (Rf=0-100Ω) and fault inception angles (Φi=0-360°). As shown in table III all the faults cases are correctly located. Fig. 6-9 shows the output of the fault distance locator for different types of faults. It is clear from these figures that all the faults were correctly located after one cycle time from the inception of fault. Table III shows some of the test results of ANN based fault locator under different fault conditions. It can be seen that all results are correct with reasonable accuracy. At various locations different types of faults were tested to find out the maximum deviation of the estimated distance Lf measured from the relay location, from the actual fault location La. Then the resulted estimated error “e” is expressed as a percentage of total line length L as:
e=
L f − La L
× 100%
(3)
In all the fault cases, the results have shown that the errors in locating the fault are less than -7% to +1.97%.
TABLE III. TEST RESULTS OF ANN BASED FAULT LOCATOR
A1N
Fault Inception angle Φi (°) 45
80
Fault Location La (KM) 67
64.470
-2.53
A2N
90
90
77
76.447
-0.553
B1N
225
90
88
87.024
-0.976
B2N
270
80
89
88.624
-0.376
C1N
360
95
95
92.405
-2.595
C2N
180
70
38
35.449
-2.551
A1B1
135
-
5
4.435
-0.565
A2B2
0
-
15
15.066
0.066
B1C1
360
-
33
33.181
0.181
B2C2
135
-
90
89.490
-0.51
C1A1
90
-
22
22.144
0.144
Fault type
Figure 6.
Test result for B1-N fault at 89Km source “S” end with Rf=80Ω, Φi=270˚ (75ms fault inception time) & δs=45˚.
Figure 7. Test result for A1C1 fault at 22Km from source “S” end at Φi=90˚ (65ms fault inception time) & δs=45˚.
Fault Resistance Rf (Ω)
Calculated Output Lf (KM)
Error e (%)
C2A2
315
-
64
64.545
0.545
A1B1N
135
30
85
82.955
-2.045
A2B2N
45
60
57
56.459
-0.541
B1C1N
0
10
35
34.606
-0.394
B2C2N
270
80
89
88.632
-0.368
C1A1N
315
0
4
5.3833
1.3833
C1A1N
225
30
58
50.838
-7.162
C2A2N
90
40
24
20.105
-3.895
C2A2N
360
95
63
62.551
-0.449
A2B2C2
180
-
15
12.418
-2.582
A2B2C2
360
-
85
86.973
1.973
V.
COMPARISON WITH THE EXISTANG SCHEMES
The salient features of some of the existing ANN based fault location schemes and the proposed scheme are presented in Table IV. The proposed scheme has several advantages : a single ANN for all the ten types of faults in both the circuits, wider range of fault resistance and fault inception angle, remote source infeed and mutual coupling effects considered. Accuracy of the algorithm is good in most of the fault cases as shown in Table III except in one case wherein it is 7%. Response time is 1 cycle comparable to the conventional distance relay. Figure 8. Test result for A1B1N fault at 57KM from source “S” end at Rf=60 Ώ, Φi=45˚ (62.5 ms fault inception time) & δs=45˚.
Figure 9. Test result for A2B2C2 fault at 85KM from source “S” end at Φi=360˚ (80ms fault inception time) & δs=45˚.
VI.
CONCLUSIONS
An accurate neural network based algorithm for fault distance location on double circuit transmission line fed from sources at both ends is presented covering all 20 types of faults in both the circuits. The algorithm employs the fundamental components of three line voltages and the six line currents of the two parallel lines at one end only. The algorithm determines fault distance location after one cycle from the inception of fault. The algorithm effectively eliminates the effect of varying fault type, fault location, fault resistance, fault inception angle, mutual coupling and remote source infeed. The performance of the proposed scheme has been investigated by a number of offline tests. The complexity of all the ten types of faults in both the
circuit of line, fault locations (0-90%), fault inception angles (0-360°) and fault resistances (0-100Ω) are considered. The simulation results show that all the ten types of faults can be correctly located after one cycle from the inception of fault. TABLE IV.
COMPARISION OF NEURAL NETWORK BASED FAULT LOCATION SCHEMES
Schemes suggested by
Fault locator inputs
Line configuration
R.N. Mahanty and P.B. Gupta [16]
Samples of 3phase V and I
Single circuit line for LG & LL faults only
A.J. Mazon et al [12]
Samples of 50Hz compoents of 3phase V and I of each circuits
Double circuit line for LG faults only
Bhavesh R. Bhalja & R.P. Maheshwari [17]
Δp, δq and resistance
Double circuit line for LG faults only
Proposed scheme
Samples of 50Hz compoents of 3phase V and I of each circuits
Double circuit line for all 10 types of faults in both the circuits (total 20 types of fault)
Fault resistance Rf range (Ω)
Fault Inception angle Φi (°)
Other Factors considered
Response time and Accuracy
0-90°
Other types of faults and wide variation in inception angle not onsidered.
Response time not indicated and error is 6%.
0-20
-
Other types of faults and variation in inception angle not onsidered.
Response time not indicated and error is 0.19%
-
Mutual coupling, remote source infeed.
Not indicated
0-200
0-360°
Mutual coupling, remote source infeed and all 10 types of faults in both the ckts.
1 cycle time from inception of faults and % error is 7% to +1.97%.
0-200
0-100
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[9]
The technique does not require communication link to retrieve the remote end data and nor zero sequence current compensation for healthy phases.
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