Analysis of Performance of Impedance based and Travelling based Fault Location Algorithms on 400kV Transmission Line

Analysis of Performance of Impedance based and Travelling based Fault Location Algorithms on 400kV Transmission Line

Ankamma Rao J 1,

1Assistant Professor, Electrical & Computer Engineering Dept,

Samara University,Ethiopia

Bizuayehu Bogale 2

2Lecturer,

Electrical & Computer Engineering Dept, Samara University,Ethiopia

Asefa Sisay 3 3Lecturer,

Electrical & Computer Engineering Dept, Samara University, Ethiopia,

Abstract For the past fifty years, electric power systems have rapidly grown. This has resulted in a large increase of the number of lines in operation and their total length. These lines experience faults which are caused by storms, lightning, snow, freezing rain, insulation breakdown and short circuits caused by birds and other external objects. In most cases, electrical faults manifest in mechanical damage, which must be repaired before returning the line to service. The restoration can be expedited if the location of the fault is either known or can be estimated with reasonable accuracy. Speedy and precise fault location plays an important role in accelerating system restoration, reducing outage time and significantly improving system reliability. This paper provides a comprehensive review of the conceptual aspects as well as recent algorithmic developments for fault location on 400kV high voltage transmission line. Several fundamentally different approaches are discussed in the paper together with the factors affecting the assumptions of the underlying concepts and the various criteria used in the different approaches are reviewed.

Index TermsWavelet Transform; Fault current distribution factor; distance to fault location; Transmission line;WMM;fault inception angle; fault resistance; traveling waves.

1. INTRODUCTION

   An Electric Power System comprises of generation, transmission and distribution of electric energy. Transmission lines are used to transmit electric power to distant large load centers. The rapid growth of electric power systems over the past few decades has resulted in a large increase of the number of lines in operation and their total length. These lines are exposed to faults as a result of lightning, short circuits, faulty equipments, mis-operation, human errors, overload, and aging. Many electrical faults manifest in mechanical damages, which must be repaired before returning the line to service. The restoration can be expedited if the fault location is either known or can be estimated with a reasonable accuracy. Faults cause short to long term power outages for customers and may lead to significant losses especially for the manufacturing industry.

2. WAVELET TRANSFORM

   Wavelet transform (WT) is a mathematical technique used for many application of signal processing. Wavelet is much

   more powerful than conventional method in processing the stochastic signals because of analyzing the waveform time-scale region. In wavelet transform, the band of analysis can be adjusted so that low frequency and high frequency components can be windowing by different scale factor. Recently WT is widely used in signal processing applications, such as denoising, filtering, and image compression. Many pattern recognition algorithms have been developed based on the wavelet transforms. It also has been used widely by the power system researchers. According to scale factor, wavelet categorized different section. In this paper the wavelet which is named Discrete Wavelet Transform (DWT) by two scale factor was used. For any function ( f ). The function is the base Wavelet if it satisfies the equation

   (1)

   The function family s,b(t) generated through dilation parameter s and transmission parameter b is defined as,

   (2)

   Where R is a set of real numbers. The wavelet transform of any function x(t) is defined as,

   (3)

   is a conjugate of Wavelet transform

   .

   Wavelet transform Wf (s,b) depends on scale factor and translation factor. Through variation of scale factor, the wavelet transform can be applied to high frequency components where

   short time intervals are necessary. Therefore, it is a suitable approach to analyze the traveling

   waves. If s= (j Z, Z is a set of integers) and b R (R is a set of real numbers), then it is a dyadic wavelet transform. It is translation invariant and hence used in signal edge detection.

   Wavelet Modulus Maxima (WMM) of wavelet transforms are the local maxima of wavelet transform satisfying the following condition:

   (4)

   where, W x(t) m is the WMM of signal x(t), A is constant,

   In the Fig.1, Fig2 and Fig.3

   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

   Z , Z Source impedances at terminals A and B respectively

   and is the Lipschitz exponent. A B

   Modulus maxima represent the singularity of step signal. The polarity of WMM is identical to polarity of sudden change of the signal and its magnitude depends on the amplitude and gradient of the sudden change of the signal. In this paper, WaveLab is used to obtain WMM. WaveLab which is available from Stanford University can be used as an alternative to the MATLAB wavelet toolbox.

3. FAULT LOCATION ALGORITHMS

   A.Impedance Based Alorithm

   A B

   Fig.1 fault network

   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

   A B Where

   (5)

   (6)

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

   Fig.2 Incremental positive sequence network for method

   A

   B

   Fig.3 Negative sequence network for method

   (7)

   (8)

   Table 1. Coefficients for determining signals defined in Equations (7) and (8)

   Voltage drop across the fault path (as shown in the third term in Equation (5)) 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

   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:

   (15)

   (16)

   Table 2: Alternative sets of weighting coefficients

   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:

   (17)

   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:

   (18)

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

   (19)

   Thus, symmetrical components of a fault current are:

   (9)

   (10)

   The Equation (18) can be rewritten as

   (20)

   It follows from Equation (10) that the total faults current ) can be expressed in the following alternative ways, depending on which symmetrical component is preferred:

   (11)

   (12)

   Substitute Equation (21) in the basic Equation (5)

   (21)

   (22)

   (13)

   (14)

   Multiplying the Equation (22) by the term yields

   d= (v x td)/2 (29)

   (23)

   Eliminating the term by taking imaginary parts of the

   Equation (23) and then rearranging, the resultant formula for the sought distance to fault (d (p.u.) ) is obtained as

   (24)

   (25)

   The signals that are processed in the fault-location algorithm

   (25) are determined from measurements acquired at one line terminal (here, at the terminal A).Table 2 shows how to set the coefficients involved in the algorithm.

   In formula (24), the angle of the current distribution factor (for the positive or negative-sequence) is involved. In 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.

   1. ravelling Wave Based Algorithm

      The proposed fault location algorithm using Wavelet Transform is show in the following steps :

      1. Get the signals from transducer output.

      2. Transform the signals into modal domain.

      3. Apply Discrete Wavelet Transform and obtain the Wavelet Transform Coefficients (Wmm).

      4. If the mode 0 (Wmm0 )is zero, then the fault is identified as an ungrounded fault and the fault distance is given by the equation :

         d= (v x td)/2 (26)

         where d is the fault location from source A, v is the wave velocity of mode 1 having magnitude slightly less than velocity of light, and td is the time gap between first two peaks of WTC of mode 1.

      5. If the mode 0(Wmm0) is nonzero, then the fault is identified as a grounded fault and the calculate the time gap tdm between the first peaks of mode 0 and mode 1.

   Fault Type	Actual fault locatio n	Impedance Based Alogirthm		Travelling Wave Based Algorithm
   		desti	Error%	desti	Error%
   AG	10	9.6817	0.3183%	9.7584	0.241%
   BG	20	19.7033	0.2967%	19.661	0.338%
   CG	30	29.36	0.639%	29.334	0.665%
   AB	40	40.0723	0.0723%	39.317	0.682%
   BC	50	49.20	0.793%	49.220	0.779%
   CA	60	59.18	0.812%	59.179	0.820%
   ABG	70	69.23	0.766%	69.224	0.775%
   BCG	80	79.36	0.637%	79.357	0.64%
   CAG	85	84.364	0.636%	84.678	0.322%
   ABC,A BCG	90	89.96	0.039%	89.988	0.012%

   If tdm > tl/2, then

4. 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 single circuit transmission line was modeled using distributed parameter model as shown in Fig.3

   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 Zero Sequence Capacitance, C0 : 8.5 e-0.009 F/km

5. SIMULATION RESULTS

   The simulation is carried out for these algorithms by varying various fault parameters like fault inception angle, fault resistance, fault type, fault location. The accuracy of fault location of these three algorithms are compared and shown in Table.3.

   Table 3. Test results for RF=54 ohms and FIA=720 for 400KV Transmission line

   t l= (2l/v) t (27)

   d x

   d=(v x tdl)/2 (28)

   where tl/2 is the travel time delay between mode 0 and mode 1 if the fault is located at the center of the line, x is the distance to the fault, v is the wave velocity of mode 1, and tx is the time delay between two consecutive peaks of the WTC mode 1.

   Else, the fault distance using (Fault is in second half section of line).

   The fault location error is calculated as

   (30)

6. CONCLUSION

An accurate traveling wave based algorithm for fault distance location on double circuit transmission line fed from sources at both ends is presented covering all types of faults in both the circuits. The algorithm effectively eliminates the effect of varying fault type, fault location, fault resistance, fault inception angle, mtual coupling and remote source infeed. 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%.

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