GRD Journals- Global Research and Development Journal for Engineering | Volume 1 | Issue 6 | May 2016 ISSN: 2455-5703
Wavelet Feature Based Fault Detection and Classification Technique for Transmission line Protection Manish Kurre PG Student Department of Electrical and Electronics Engineering Disha Institute of Management and Technology, Raipur, CG, India
Shailesh. M. Deshmukh Assistant Professor & Head of Department Department of Electrical and Electronics Engineering Disha Institute of Management and Technology, Raipur, CG, India
Abstract In the present scenario, the efficiency of a power system depends on how a fault is accurately detected and classified, so that quick restoration and maintenance of power is accomplished. Fault detection, fault classification, needs to be performed using a fast and responsive algorithm at different levels of a power system. Effect of factors such as fault impedance, fault inception angle (FIA), and fault distance, which cause disturbances in power line can be analyzed by Wavelet based multi resolution analysis (MRA). This paper proposed, a fault detection and classification technique using MRA based on wavelet transform. The present paper also deals with the exploration of advantages and problems related with the proposed fault detection and classification technique. The method of fault detection and classification proposed in this work is based on the three-phase current and voltage waveforms measured during the occurrence of fault in the power transmission-line. The technique proposed in this paper, is verified using MATLAB/Simulink software and the obtained results shows that the wavelet based MRA is a good tool for detection and classification of faults. However it is also shown that the most critical problem related to this technique is the selection of appropriate threshold values for all the three phases. It has been also shown that this technique requires expert hands and knowledge of the system for the selection of proper threshold value. Keywords- Transmission line, fault detection and classification, wavelet feature, multi-resolution analysis
I. INTRODUCTION The two most important tasks involved in transmission-line relaying are fault detection and classification [19]. These two tasks must be accomplished in fast and precise manner as much as possible to protect the system from the harmful faults. The higher accuracy is also important to restore the system efficiently. Interruption of power flow in a power line is mainly due to the occurrence of faults at various levels. Meanwhile, the reliability and economical aspects of power transfer is highly affected by the occurrence of the faults which in turn affects the profit from the system. However the performance of the system can be enhanced by accurate and fast fault detection. Wavelet Transform (WT) [18] conducts both time and frequency domain analysis of current signals and transients in voltage waveforms and hence provides a tool for detection and classification of faults. It is usually observed that, the overhead lines are affected by transients, due to the travelling wave phenomenon after the inception of fault. With the analysis of the faults due to induced transients one can gather handy information which covers a wide range of aspects like location and detection of fault. Ideally, the identification of mother wavelet signifies the preciseness of wavelet analysis. The choice of the appropriate mother wavelet depends on the nature of the signal and on the type of information to be extracted from the signal. In this paper Wavelet multi resolution analysis is used as the solution for information extraction from transient signals caused by faults. Wavelet family, dB4 (Daubechies) is used as the mother wavelet. By applying wavelet MRA technique [6], extraction of third level detailed coefficient from the current signal after summation is performed. The existence of a fault is identified based on the detail coefficients summation magnitude, a generalized algorithm has been implemented for the transmission line faults classification.
II. DISCRETE WAVELET TRANSFORM Filter bank theory [1] is the foundation for the development of the discrete wavelet transform (DWT). At level (k), the wavelet transform coefficients of a signal are determined by using a high pass and a low pass filters. The obtained coefficients of low pass filter from the earlier stage are then down sampled by a factor two to reduce the dimension. The high pass filter is obtained from the mother wavelet function and further measures the details coefficients for the input signal. Similarly, the low pass filter delivers a smoothed version of the input signal and is derived from a scaling function, which is associated to the mother wavelet function [2] & [3]. For a function s(t), its continuous wavelet transform (WT) can be calculated from the following equation:
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Wavelet Feature Based Fault Detection and Classification Technique for Transmission line Protection (GRDJE/ Volume 1 / Issue 6 / 021)
1 t-τ wr ,m (t ) s (t ) Ψ dt m m
… (1)
1 C (m,τ ) s(t ) Wr',m (t )dt m0
… (2)
Fundamentally, in the wavelet decomposition, the wavelet function generates a detail version of the decomposed signal and the scaling function generates the approximated version of the signal that is decomposed. Consequently, Wavelet transform performs a Conversion of scale versus time signal to amplitude versus time signal. In the same context, the MRA utilizes the scaling functions along with the wavelet functions as building blocks for decomposition and construction of signal at different levels of resolution. Wavelet is a waveform of limited duration has an average value of zero. In general the implementation of discrete wavelet transform is done by using multi resolution analysis. The transmission line faults in power system are usually classified as Symmetrical faults and Unsymmetrical fault whereas the three-phase fault is termed as a symmetrical type of fault. A three level wavelet decomposition tree for multi-resolution analysis used in this paper is shown in figure (1).
Fig. 1: Three level wavelet decomposition used for MRA.
III. SIMULATION STUDY OF DIFFERENT FAULT CASES AND RESULTS A three-phase transmission line of rating 400 kv and line length of 300 km has been considered for the simulation studies of various fault cases; their detection and classification. Figure (2) represents the circuit diagram of simulated system in MATLB Simulink 2013(a). The complete system parameters used for the development of Simulink model are given in appendix A.
Fig. 2: Schematic circuit diagram of simulated system
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Wavelet Feature Based Fault Detection and Classification Technique for Transmission line Protection (GRDJE/ Volume 1 / Issue 6 / 021)
Three phase fault current obtained at source end for different abnormal conditions are utilized for the fault identification in simulated transmission line. Sampling time taken for the analysis is 10 ms which relates to sampling frequency of 1 KHz, and the total no of wavelet level consider is three, third level output corresponds to frequency band of 125 – 250 Hz. The detail coefficient of third level wavelet decomposition for the currents in phases A, B and C respectively over different fault inception angle and locations has been extracted. The extracted detail coefficients have been summed individually denoted as Sa, Sb, Sc respectively for phase A, B and C. These respective sums are again summed and compared with predefined threshold value for the detection of the fault in the transmission line. The flow diagram of the fault occurrence detection technique used in this paper is shown in Figure (3). Where the value of threshold T is selected as T = 4. Three Phase Current Signals
Wavelet Decomposition
Extract High Frequency Detail Coefficients
If (Sa+Sb+Sc)<=0 And Sa<T
No No Fault
Yes Fault Classifier
Fig. 3: Fault occurrence detection technique used in this paper.
The waveforms of the phase currents obtained for different fault cases are shown from figure (4) to figure (9). 10 Ia Ib Ic
8
6
4
2
0
-2
-4
-6
0
50
100
150
200
250
300
Fig. 4: Ia, Ib, and Ic for LG fault at D== 100 km, FIA = 0°, Rf = 0.001Ὠ 5 Ia Ib Ic
4
3
2
1
0
-1
-2
-3
-4
0
50
100
150
200
250
300
Fig. 5: Ia, Ib, and Ic for LG fault at D== 100 km, FIA = 0°, Rf = 1Ὠ
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Wavelet Feature Based Fault Detection and Classification Technique for Transmission line Protection (GRDJE/ Volume 1 / Issue 6 / 021)
6 Ia Ib Ic 4
2
0
-2
-4
-6
0
50
100
150
200
250
300
Fig. 6: Ia, Ib, and Ic for LL fault at D== 200 km, FIA =60°, Rf = 0.001Ὠ 6 Ia Ib Ic 4
2
0
-2
-4
-6
0
50
100
150
200
250
300
Fig. 7: Ia, Ib, and Ic for LLG fault at D== 200 km, FIA =60°, Rf = 0.001Ὠ 20 Ia Ib Ic 15
10
5
0
-5
-10
-15
0
50
100
150
200
250
300
Fig. 8: Ia, Ib, and Ic for LLL fault at D== 100 km, FIA =0°, Rf = 0.001Ὠ
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Wavelet Feature Based Fault Detection and Classification Technique for Transmission line Protection (GRDJE/ Volume 1 / Issue 6 / 021) 15 Ia Ib Ic 10
5
0
-5
-10
-15
-20
0
50
100
150
200
250
300
Fig. 9: Ia, Ib, and Ic for LLL fault at D== 100 km, FIA =60°, Rf = 0.001Ὠ
Let Sa, Sb, Sc be the summation of 3rdlevel detail coefficients for current signals for a, b, c phases respectively. Table-1 to Table-4, below show the values of Sa, Sb, Sc for different types of faults. From these tables it is observed that the magnitudes of Sa, Sb, Sc increases whenever any fault occurs in a transmission line. Based on the sampling rate the signal is divided into four decomposition levels. Among different levels only 3rd level is consider for analysis because the frequency corresponding to this level is covering 2nd and 3rd harmonics which are dominant in the fault conditions. Table 1: L-G fault with different fault distances the values of Sa, Sb, Sc with FIA = 0° Fault Location 10 km 30 km 100 km 150 km 200 km Sa -22.6535 -10.3160 -3.4470 -2.3011 -1.7086 Sb -1.0245 -1.0593 -1.0770 -1.0764 -1.0761 Sc 0.4680 0.4333 0.4155 0.4160 0.4165 Table 2: L-L fault with different fault distances the values of Sa, Sb, Sc with FIA = 0° Fault Location 10 km 30 km 100 km 150 km 200 km Sa
-9.7777
-5.7237
-2.4045
-1.7240
-1.3583
Sb
9.4579
5.4039
2.0847
1.4043
1.0385
Sc 0.3198 0.3198 0.3197 0.3197 0.3197 Table 3: L-L-G fault with different fault distances the values of Sa, Sb, Sc with FIA = 0° Fault Location 10 km 30 km 100 km 150 km 200 km Sa
-24.6637
-12.3979
-4.7238
-3.3270
-2.5969
Sb
-5.4240
-1.2719
-0.2338
-0.2008
-0.1995
Sc 0.4859 0.5200 0.5572 0.5785 0.5995 Table 4: L-L-L fault with different fault distances the values of Sa, Sb, Sc with FIA = 0° Fault Location 10 km 30 km 100 km 150 km 200 km Sa
-33.5438
-19.2419
-7.7057
-5.3731
-4.1062
Sb
-14.3145
-8.1274
-3.2171
-2.2452
-1.7089
Sc
47.8584
27.3694
10.9228
7.6183
5.8150
Now the fault classification technique developed in this paper based on the sum of detail coefficients is shown in figure (10).
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Wavelet Feature Based Fault Detection and Classification Technique for Transmission line Protection (GRDJE/ Volume 1 / Issue 6 / 021)
Fault Detector
Acquire Sa, Sb and Sc from Fault Detector
Is 0< (Sa+Sb+Sc)< T1
YES
Line FAULT
NO
Is (Sa+Sb+Sc)> T2
Ground FAULT
Fig. 10: Proposed Fault Classification Technique.
For the proper classification of the faults the selection of threshold values are critical and very much depends on the system. Here in this paper, the threshold values selected are T1 = 2 and T2 = 5. Finally the complete fault detection and classification system developed is tested against various faults on the system and the results of this testing is tabulated in Table5. S. No. 1 2 3 4 5 6 7 8 9 10 11
Table 5: Different fault detection and classification results of proposed technique Fault Type Correct detection Classification Result Phase A to Ground Fault Yes Ground Fault Phase B to Ground Fault Yes Ground Fault Phase C to Ground Fault Yes Ground Fault Phase AB to Ground Fault Yes Ground Fault Phase AC to Ground Fault Yes Ground Fault Phase BC to Ground Fault Yes Ground Fault Phase ABC to Ground Fault Yes Ground Fault Phase A to Phase B Line Fault Yes Line Fault Phase A to Phase C Line Fault Yes Line Fault Phase B to Phase C Line Fault Yes Line Fault Phase A to Phase B to Phase C Line Fault Yes Line Fault
From Table-5, it is clearly observable that the proposed wavelet transform based MRA technique is reliable and robust in accurately detection and classification of different faults occurred in the transmission line. The only limitation of this proposed technique is the selection of the proper threshold values for comparison of wavelet coefficients during the decision making for both the detection and classification of faults.
IV. CONCLUSIONS In this paper, we have proposed a robust and reliable technique for the protection of the 400 kv transmission line. The proposed technique accurately detects the fault occurrence and also provides the classification of faults in two categories. Furthermore, the obtained results also confirmed the higher protection efficiency of the proposed technique. The only limitation of this technique is the selection of proper threshold.
APPENDIX -A Line data of 400-kV, 300 km length. Line Length = 300 km R1 0.249168 /km All rights reserved by www.grdjournals.com
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Wavelet Feature Based Fault Detection and Classification Technique for Transmission line Protection (GRDJE/ Volume 1 / Issue 6 / 021)
L1 0.00156277 H /km
C1 19.469E-9 F/km R0 0.60241 /km
L0 0.004830 H /km C0 12.06678E-9 F/km Source impedance Sending End Z S1 17.177 j 45.5285
Z S 0 2.5904 j14.7328 Receiving End Z S1 15.31 j 45.9245 Z S 0 0.7229 j15.1288
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