Wavelet Transform Based Relaying Scheme for Double Circuit Transmission Line Protection

DOI : 10.17577/IJERTV6IS080046

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Wavelet Transform Based Relaying Scheme for Double Circuit Transmission Line Protection

Trilok Soni1, Shoyab Ali2

1, 2Department of Electrical Engineering

1,2Vedant College of Engineering and Technology Kota, India

Abstract – An application of wavelet transform based multi resolution analysis approach for double circuit transmission line protection from various faults is revealed in this paper, to intensify the result of various problems linked with distance security of double circuit transmission lines. The fault current signal is explored by choosing wavelet transform program of MATLAB and selecting db4 as mother wavelet to retrieve the fault current coefficients for fault detection in double circuit transmission line. Results of simulation work displays that the proposed relay based on wavelet transform can lift the work of orthodox protection schemes.

Index Terms – Double circuit transmission lines, relaying schemes, wavelet transform, fault detection, location, and classification.

  1. PREFACE

    Multi terminal transmission lines are entirely standard owing to the growth of power transmission system both in convolution and expanse. Nevertheless, a double circuit transmission line constitutes unusual issues due to multi- terminals. On this subject, Adel Aktaibi et al. in [1] proposed a hybrid technique which consists of WPT and dq-axis components for current differential protection of transmission lines. . In their work, they developed and implemented hybrid technique by localizing frequency sub-bands of dq-axis components of the differential currents using WPT for finding out the best signature of the internal faults. P.D. Raval et al. in

    [2] proposed a method for series capacitor compensated multi bus EHV transmission system protection by using PNN. A classification of various types of transmission line faults has been done by the usage of PNN technique. Wavelet transform has been used for feature extraction of current signals. Y. Manju Sree et al. in [3] developed a protection scheme for

    Gaurav Kapoor3

    3Department of Electrical Engineering

    3Modi Institute of Technology Kota, India

    transmission line for cross country faults considering current transformer saturation. In [10] J. Uday Bhaskar et al. presented a wavelet and fuzzy based protection scheme for the protection of three terminal transmission systems. Suman Devi et al. in [11] presented a DWT based scheme for transmission line fault detection. Xinzhou Dong et al. in [12] presented a principal of directional comparison based on travelling wave. Xinzhou Dong et al. in [13] implemented and applied directional protection based travelling wave theory in UHV transmission line. An algorithm based on singular value and Euclidean norm for the fault detection and classification in transmission lines has been developed by Daniel Guillen et al. in [14]. The algorithm has been developed by using DWT and singular value decomposition (SVD). Daniel Guillen et al. in [15] wavelet singular entropy (WSE) technique has been developed for the detection and classification of transmission line faults. Fault classification has been done through the Euclidean norm based on the all phases WSE information. A procedure for double circuit transmission line fault detection using wavelet transform is the main intention of this paper. Simulations results of MATLAB/Simulink demonstrate that the proposed wavelet transform scheme for double circuit transmission line protection provides precise response in various fault scenarios.

  2. SIMULATION STUDIES

    The system accommodates three phase transmission line of 200 km length having double circuits fed by three phase 400 kV, 50 Hz voltage source as shown in Fig. 1. Simulation of a double circuit transmission line model has been done by utilizing MATLABs SimPowerSystems toolbox.

    Double Circuit

    multi-terminal transmission lines combined with wind energy source. Wavelet transform technique has been used for the analysis of different types of faults. In [4] Tamer S.

    Abdelgayed et al. developed a new technique based on

    harmony search algorithm (HSA) for determining suitable wavelet functions for the transmission line fault classification.

    Generator

    Bus-1

    WT

    Relay

    Transmission Line

    Fault

    Bus-2

    Load

    Ahmad Abdullah in [5] developed a wavelet based neural network algorithm for classifying transients which include transmission line faults on protected and adjacent transmission line. In [6] Ahmad Abdullah proposed wavelet entropy based lightning fault detection on transmission lines. Ahmed R. Adly et al. in [7] proposed a fault identification algorithm for fault discrimination, classification and faulty phase selection of a high voltage transmission line. Ozkan Altay et al [8] developed and proposed a scheme that uses single end travelling wave fault location on transmission line

    Fig. 1 Single line diagram under task

  3. WAVELET TRANSFORM BASED RELAYING

    SCHEME

    For analyzing high frequencies, the signal is passed through high pass series connected filters and for analyzing low frequencies, the signal is passed through low pass series connected filters. For any function (f) DWT is drafted as:

    0

    (. ) = 1 () [00]

    0

    by the usage of wavelet analysis without using the velocity of propagation. Saeed Asghari Govar and Heresh Seyedi in [9] proposed an adaptive CWT based differential protection of

    Where the mother wavelet can be connote by , the scale parameters can be expressed as a0m and the parameter of translation is designated as am, n0, b0.

    The subsequent equation also defines WT:-

    ()2

    2(2

    )

    Where a mother wavelet is designated as (t) having finite energy which is a function of time.

    Each sub band is accommodated with the neighbouring immense frequency sub band half of the samples [18-21].

    [][2 ]

    [][2 ]

    Where yH [k] and yL[k] is the high pass (g) and low pass

    (h) filters gain, after sub sampling by twice.

    By gathering the one full cycle window of sampled information of each consistent signal of current, initiation of the recommended scheme has been done. Testing of WT based fault detection strategy has been done on the developed MATLAB model of double circuit transmission line. Following current measurement of all three phases, wavelet analysis (using db4 wavelet) of three phase current has been done for their decomposition and extracting high frequency detailed coefficients and after that summation of the square of the detailed coefficients has been done including calculation of wavelet energy of each phase. The relaying scheme is shown in Fig. 2

  4. SIMULATION RESULTS OF WT BASED FAULT

    DETECTOR

    Wavelet transform based fault detection strategy has been tested in MATLAB/Simulink model of double circuit transmission line [22]. Four types of cases has been studied- normal operating condition (no fault condition), single line to ground fault operating condition (phase-A1G fault), double line to ground fault operating condition (phase-A1A2G fault) and triple line to ground fault operating condition (phase- B1B2C2G fault). Simulation results of WT based fault detector relay during healthy condition is shown in Fig.3 and it constitutes plots of line current, its decomposition coefficient and detail coefficient at level-1. Fig.4-9 demonstrates simulation results of relay dring phase-A1G (single line to ground) fault condition. Table-1 and 2 summarizes relay output during healthy and phase-A1G fault operating condition. The relay output includes detail coefficients at level-1 (Ca, b, c D1), energy level (Ea, b, c) and sum of squares of detail coefficients at level-1 of each phase

    Fig. 2 Scheme for fault detection

    (Sum_S_Da1, Db1, Dc1) of a double circuit transmission line. From Table-1 and 2 it can be seen that during the fault inception the magnitude of detail coefficient, energy and sum of square of detail coefficient of faulty phase (s) increases in comparison to the relay output during healthy condition. For example, the magnitudes of detail coefficient, energy and sum of square of detail coefficients in healthy condition are 0.0017, 99.3461 and 2.3219*10^-5. But on the application of phase-A1G fault the magnitudes of detail coefficient, energy and sum of square of detail coefficients are 145.0327, 99.7841 and 4.3625*10^5. The DWT based fault detector exploits this change. It is clear from Table-1 and 2 that during healthy condition the magnitude of detail coefficient, energy and sum of square of detail coefficient of all phases is very much less in comparison to that after fault inception. Thus, DWT based fault detector is able to differentiate between healthy and faulty condition and it easily detects fault [23].

    0.2

    0.15

    0.1

    0.05

    0

    -0.05

    -0.1

    -0.15

    -0.2

    Phase-A1 (No Fault)

    -3

    150

    100

    50

    0

    -50

    -100

    -150

    -200

    x 10

    x 10

    120

    100

    80

    40 60

    Samples

    20

    0

    2

    1

    0

    -1

    -2

    -3

    x 10

    Samples

    250

    200

    150

    100

    50

    0

    1

    0.5

    0

    -0.5

    -1

    Time (s)

    2500

    2000

    1500

    1000

    500

    0

    Current (A)

    Current (A)

    Magnitude

    Magnitude

    Magnitude

    Magnitude

    Fig. 3 Phase-A1 current, its wavelet decomposition and detail coefficient during healthy condition

    4

    Phase-A1 (A1G Fault)

    4

    1

    0.5

    0

    -0.5

    -1

    -1.5

    500 1000 1500 2000 2500 3000 3500

    Time (s)

    6

    4

    2

    0

    -2

    -4

    -6

    -8

    50

    100

    150

    200

    250

    300

    350

    1.5

    1

    0.5

    0

    -0.5

    -1

    -1.5

    0 200 400 600 800 1000 1200 1400 1600

    Samples

    Samples

    0

    0

    0.02

    0.01

    0

    -0.01

    -0.02

    -0.03

    0 200 400 600 800 1000 1200 1400 1600

    Samples

    Current (A)

    Magnitude

    Magnitude

    Fig. 4 Phase-A1 current, its wavelet decomposition and detail coefficient during A1G fault condition

    0.3

    0.2

    0.1

    0

    -0.1

    -0.2

    -0.3

    -0.4

    Phase-B1 (A1G Fault)

    0

    500 1000 1500 2000 2500 3000 3500

    Time (s)

    0

    50

    100 150 200 250 300 350

    Samples

    0.02

    0.01

    0

    -0.01

    -0.02

    -0.03

    1.5

    1

    0.5

    0

    -0.5

    -1

    -1.5

    Current (A)

    Magnitude

    Magnitude

    Fig. 5 Phase-B1 current, its wavelet decomposition and detail coefficient during A1G fault condition

    0.3

    0.2

    0.1

    0

    -0.1

    -0.2

    -0.3

    -0.4

    Phase-C1 (A1G Fault)

    0

    500 1000 1500 2000 2500 3000 3500

    Time (s)

    0

    50

    100

    150

    200

    250

    300

    350

    0.04

    0.02

    0

    -0.02

    -0.04

    -0.06

    1

    0.5

    0

    -0.5

    -1

    0 200 400 600 800 1000 1200 1400 1600

    Samples

    Samples

    Current (A)

    Magnitude

    Magnitude

    Fig. 6 Phase-C1 current, its wavelet decomposition and detail coefficient during A1G fault condition

    0.2

    0.1

    0

    -0.1

    -0.2

    -0.3

    Phase-A2 (A1G Fault)

    0

    500 1000 1500 2000 2500 3000 3500

    Time (s)

    0

    50

    100 150

    200 250 300

    350

    1

    0.5

    0

    -0.5

    -1

    0 200 400 600 800 1000 1200 1400 1600

    Samples

    Samples

    0.03

    0.02

    0.01

    0

    -0.01

    -0.02

    0 200 400 600 800 1000 1200 1400 1600

    Samples

    Current (A)

    Magnitude

    Magnitude

    Fig. 7 Phase-A2 current, its wavelet decomposition and detail coefficient during A1G fault condition

    0.2

    0.15

    0.1

    0.05

    0

    -0.05

    -0.1

    -0.15

    -0.2

    Phase-B2 (A1G Fault)

    0

    500 1000 1500 2000 2500 3000 3500

    Time (s)

    0

    50

    100 150

    200 250 300

    350

    Samples

    0.03

    0.02

    0.01

    0

    -0.01

    -0.02

    1

    0.5

    0

    -0.5

    -1

    -1.5

    Current (A)

    Magnitude

    Magnitude

    Fig. 8 Phase-B2 current, its wavelet decomposition and detail coefficient during A1G fault condition

    0.2

    0.1

    0

    -0.1

    -0.2

    -0.3

    Phase-C2 (A1G Fault)

    0

    500 1000 1500 2000 2500 3000 3500

    Time (s)

    0

    50

    100 150

    200 250 300

    350

    0 200 400 600 800 1000 1200 1400 1600

    Samples

    Samples

    OUTPUTS

    PHASES

    A1

    B1

    C1

    A2

    B2

    C2

    Ca, b, c D1

    0.0017

    0.0017

    0.0016

    0.0017

    0.0017

    0.0016

    Ea, b, c

    99.3461

    99.6224

    99.5312

    99.3461

    99.6224

    99.5312

    Sum_S_Da1, Db1, Dc1

    2.3219*10^-5

    1.7902*10^-5

    1.5929*10^-5

    2.3219*10^-5

    1.7902*10-5

    1.5929*10^-5

    Fig. 9 Phase-C2 current, its wavelet decomposition and detail coefficient during A1G fault condition TABLE 1 RELAY OUTPUT DURING HEALTHY CONDITION

    TABLE 2 RELAY OUTPUT DURING PHASE A1G FAULT CONDITION

    OUTPUTS

    PHASES

    A1

    B1

    C1

    A2

    B2

    C2

    Ca, b, c D1

    145.0327

    0.0179

    0.0179

    0.0209

    0.0293

    0.0292

    Ea, b, c

    99.7841

    98.0099

    98.3003

    97.6424

    99.1634

    99.1931

    Sum_S_Da1, Db1, Dc1

    4.3625*10^5

    0.0036

    0.0033

    0.0064

    0.0019

    0.0018

  5. CONCLUSION

Wavelet transform based double circuit transmission line fault detection layout is presented in this paper and tested for various types of faults. For different system circumstances and parameters a 400 kV, double circuit transmission line of 50 Hz frequency and 200 km length is simulated by using MATLAB software. The proposed scheme correctly detects the faulty phase based on the simulation results for all the cases that have been tested by using MATLAB software.

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  3. Xinzhou Dong et al., Implementation and Application of Practical Travelling Wave based Directional Protection in UHV Transmission Lines, IEEE Transactions on Power Delivery, pp. 1-9, 2015

  4. Daniel Guillen et al., Detection and Classification of Faults in Transmission Lines using the Maximum Wavelet Singular Value and Euclidean Norm, IEEE-IET-GTD-2015, pp. 2294-2302, vol.-9, July-

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