Accurate Fault Location Technique on Power Transmission Lines with use of Phasor Measurements

DOI : 10.17577/IJERTV4IS020429

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Accurate Fault Location Technique on Power Transmission Lines with use of Phasor Measurements

Ankamma Rao J

Bizuayehu Bogale

Assistant Professor Assistant Professor

Electrical& Computer Engineering Dept Electrical & Computer Engineering Dept Samara University, Ethiopia Samara University, Ethiopia

Abstract This paper presents a new approach to fault location on power transmission lines. This approach uses the phasor measurements at one end of the line and benefits from advantages of digital technology and numerical relaying, which are available today and can be applied for off-line analysis. This technique uses only end of data and accurate fault distance location is achieved after one cycle from the inception of fault. The analysis for fast identification of fault is evaluated based on the representation of the travelling waves through wavelet modulus maxima. The present criterion can detect the instant of fault, location of fault and kind of fault. MATLAB/ Simulink software was used to test the proposed approach. Various fault conditions were simulated by varying fault type, fault resistance, fault location and fault inception angle, on a given power system model. The simulation results demonstrate the validity of the proposed approach of faulted phase selection.

KeywordsFault location, Current distribution factor, MATLAB, Fault impedance, Fault resistance, Fault inception angle.

  1. INTRODUCTION

    Location of faults in power transmission lines is one of main concerns for all electric utilities as the accurate fault location can help to restore the power supply in the shortest possible time. Fault location methods for transmission lines are broadly classified as impedance based method which uses the steady state fundamental components of voltage and current values [1-3], travelling wave (TW) based method which uses incident and reflected TWs observed at measuring end(s) of the line [4,5], and knowledge based method which uses artificial neural network and/or pattern recognition techniques [6,7]. All the above methods use the measured data either from one end of transmission line or from all ends. The method which uses data from all ends requires synchronized measurement with time stamping and online communication of data to central location [8-13]. This paper describes a fault location determination method using fault current distribution factors on 400 KV transmission line.

  2. PROPOSED FAULT LOCATION ALGORITHM

    Fig.1 Fault network diagram

    Fig.2. Incremental positive sequence network diagram

    Fig.3.Negative sequence network diagram

    d Estimated distance to the fault (units: p.u)

    VA_P Protective distance relay voltage at the line end A IA_P Protective distance relay current at the line end A IF Total fault current

    ZL Total line impedance

    VF Fault voltage

    ZA, ZB Source impedances at terminals A and B respectively EA, EB Source voltages at terminals A and B respectively IA1 Incremental positive sequence current.

    IA2 Negative sequence current

    Z1L Total positive sequence line impedance

    Z2L Total negative sequence line impedance

    Z1A , Z1B Positive sequence source impedances at terminals A and B respectively

    Z2A , Z2B Negative sequence source impedances at terminals A and B respectively.

    To derive the Fault location algorithm, the fault loop composed according to the fault classified type is considered. This loop contains the faulted line segment (between points AA and F) and the fault path itself. A generalized model for the fault loop is stated as fallows

    (1)

    Where

    (2)

    Fault loop voltages and current can be expressed interns of the local measurements and with using coefficients gathered in Table 1

    (3)

    (4)

    Table1. Coefficients for determining signals defined in Equations (3) and (4)

    Voltage drop across the fault path (as shown in the third term in Equation (1)) is expressed using sequence components of total fault current (IF0, IF1, IF2). Determining this voltage drop requires establishing the weighting coefficients. These coefficients can accordingly be determined by taking the boundary conditions for particular fault type. However, there is some freedom for that. Thus, it is proposed firstly to utilize this freedom for avoiding zero sequence quantities. This is well known that the zero sequence impedance of a line is considered as unreliable parameter. This is so due to dependence of this impedance upon the resistivity of a soil, which is changeable and influenced by weather conditions. Moreover, as a result of influence of overhead ground wires, the zero sequence impedance is not constant along the line length. Thus, it is highly desirable to avoid completely the usage of zero sequence quantities when determining the voltage drop across the fault path. This can be accomplished by setting as shown in Table 2, where the alternative sets of the weighting coefficients are gathered. Secondly, the freedom in establishing the weighting coefficients can be utilized for determining the preference for using particular quantities. The negative sequence (Table 2) or the positive sequence (Table 2) can be preferred

    For example, considering AG fault one has:

    (4)

    Thus, symmetrical components of a fault current are:

    (5)

    It follows from Equation (5) that the total faults current ) can be expressed in the following alternative ways, depending on which symmetrical component is preferred:

    (6)

    (7)

    (8)

    (9)

    The total fault current (IF) is expressed as weighted sum of its symmetrical components (IF1, IF2, IF0 ) , which can be

    determined with use of fault current distribution factors:

    (10)

    (11)

    Multiplying the Equation (17) by the term yields

    (12)

    Taking into account a set of weighting coefficients that for zero and expressing the symmetrical components of total fault current with use of fault current distribution factors and one obtains:

    (13)

    Considering that for the fault current distribution factors for positive- and negative-sequence, with respect to their magnitude and angle, we have

    (14)

    (15)

    The Equation (13) can be rewritten as

    (16)

    Substitute Equation (16) in the basic Equation (1)

    (17)

    (18)

    Eliminating the term by taking imaginary parts of the

    Equation (18) and then rearranging, the resultant formula for the sought distance to fault (d (p.u.) ) is obtained as

    (19)

    (20)

    In formula (19), the angle of the current distribution factor (for the positive or negative-sequence) is involved. It is proposed to assume that this angle equals zero ( = 0), i.e., that the fault current distribution factor is a real number. In practice, this assumption is not completely fulfilled and thus there is a certain error due to this.

  3. POWER SYSTEM MODEL

    The SimPowerSystem which is an extension to the Simulink of MATLAB software was used to simulate the double end fed power system. The 100 km, 400 kV transmission line was modeled using distributed parameter model as shown in Fig.4

    Table:2 Alternative sets of weighting coefficients

    REFERENCES

    Fig.4 Power System model

    The transmission line parameters are as follows: Positive Sequence Resistance, R1 : 0.0275 / km Zero Sequence Resistance, R0 : 0. 275 /km

    Zero Sequence Mutual Resistance, R0m : 0.21 /km Positive Sequence Inductance, L1 : 0.00102 H/km Zero Sequence Inductance, L0 : 0.003268 H/km Positive Sequence Capacitance,C1 : 13 e-0.009 F/km

  4. SIMULATION RESULTS The fault location error is calculated as

  5. CONCLUSION

(21)

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  9. Izykowski J, Rosolowski E, Saha MM (2004) Locating faults in parallel

    In this paper, the new accurate algorithm for locating faults on power transmission line with use of the phasor measurements of voltages and currents at one end of the line has been presented. Since numerical relays often store oscillographic and phasor information following the occurrence of a fault, the algorithm can be implemented using information which should be readily available. The complexity of all ten types of faults, fault locations (0-100km), fault inception angles (0-900), fault resistance (0-100 ) are considered. The simulation results show that all ten types of faults are correctly located with fault location error less than 1%.

    Fault Type

    Fault Resistance

    Actual Fault

    Location

    Estimated Fault

    Location

    Error (%)

    LG

    84

    18

    17.1766

    0.8234%

    LG

    194

    36

    35.4792

    0.5208%

    LG

    106

    54

    53.1764

    0.8236%

    LG

    55

    75

    74.1868

    0.8132%

    LG

    25

    86

    85.2781

    0.7219%

    LLG

    100

    12

    12.3849

    0.3849%

    LLG

    166

    64

    64.2147

    0.2147%

    LLG

    128

    48

    48.1589

    0.1589%

    LLG

    74

    8

    8.1988

    0.1988%

    LL

    6

    44

    44.0529

    0.0529%

    LL

    14

    94

    95.5680

    1.5680%

    LLL

    2

    26

    25.3589

    0.6411%

    LLL

    16

    56

    56.1358

    0.1358%

    Table 4: Results for all 11 types of faults

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