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ISSN (PRINT) : 2320 – 8945, Volume -1, Issue -1, 2013 117

Identification and Classification of Transmission

Line Faults Using Wavelet Analysis

V. Ashok, K. G. V. S. Bangarraju & V. V. N. Murthy

UCE-JNTUK,CPRI-Bangalore E-mail : [email protected] ,[email protected], [email protected]

Abstract - An accurate fault detection and classification is required to transmit power from generating station to various load centers reliably. Transmission line protection is mainly based on circuit breaker active tripping’s. This tripping action depends on the voltage and current waveforms during the fault. Wavelet analysis which is a signal processing tool to detect and analyze the fault occurring in transmission line. Discrete wavelet transform (DWT) is used for the analysis of the current waveform during the fault. An approach for transmission line classification also described in this paper. MATLAB/Simulink software is used to illustrate the effectiveness of the proposed approach, an extensive simulation studies have been carried out for different types of faults.

Keywords - Classification, DWT, Protection, Wavelets

I. INTRODUCTION

The power system protection is mainly depends on circuit breaker active tripping’s at the time of fault. This operation of circuit breaker is controlled by a series of protective relay. The quick and precise operation of these relays is required to prevent malfunctioning of the power system. For remedy of the faulted zone a precise study of the waveforms of voltage and current during the fault incidence is required. Many researchers have suggested techniques for fault type detection. These techniques mainly depend on studying the pattern of the voltage and current waveforms associate with the fault [1]. In this paper a new technique is proposed for identification and classification of different type of fault on a transmission line using discrete wavelet analysis. Wavelet analysis allows the decomposition of a signal into different levels of resolution called multi resolution analysis (MRA).Some application of the wavelet analysis are used in modeling of the power system transients, power quality and power system relaying. In next sections a brief explanation of wavelet analysis technique, methodology, a case study, results analysis and conclusion.

II. WAVELET ANALYSIS TECHNIQUE

Wavelet is a short duration wave. It is a mathematical basis function used to divide a given function or continuous-time signal into different scale components. This wavelet analysis is a signal processing tool which is very useful to analyze a signal. It allows the decomposition of a signal into different levels of resolution. The basic function is dilated at low frequencies and compressed at high frequencies, so that large windows are used to obtain the low frequency components of the signal while small windows are used to obtain reflect discontinuities. Unlike Fourier, which relies on a single basis function, wavelet analysis uses basis function of a rather wide functional form such as Haar wavelet and Dauchies wavelet [3]. This is a new form of signal analysis is far more efficient than Fourier analysis whenever a signal is dominated by transient behavior or discontinuities [4], [5]. In wavelet analysis we often speak about approximations and details. The approximations are high scale, low frequency components of the signal. The details are the low scale, high frequency components. The filtering process at its most basis level,like: the original signal decomposes through two complementary filters and emerges as two signals. This decomposition process can be iterated, with successive approximations being decomposed in turn, so that one signal is broken down into many lower resolution components. This decomposition process called as Multi Resolution Analysis(MRA).

Fig. 1 : Wavelet Resolution

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http://en.wikipedia.org/wiki/Continuous_signal

ITSI Transactions on Electrical and Electronics Engineering (ITSI-TEEE)


Fig. 2 : Three-level decomposition of a signal 'S'

The above fig.1 and fig.2 shows about wavelet resolution process and decomposition process of a signal. Wavelet transform are classified into discrete wavelet transforms (DWTs) and continuous wavelet transforms (CWTs). Note that both DWT and CWT are continuous-time transforms. They can be used to represent continuous-time signals. CWTs operate over every possible scale and translation whereas DWTs use a specific subset of scale and translation values or representation grid. The wavelet Transform of a continuous signal x(t) is defined as[1],

(1)

Where a and b are the scaling and translation parameters respectively and g is the mother wavelet function.

The discrete wavelet transform is defined as

(2)

Where g[n] is the mother wavelet, and the scaling and translation parameters a and b are functions of an integer parameter m, a=�0�and b=n�0� .

III. METHODOLOGY

The identification and the classification of the faulty phase/phases is an important aspect of transmission line protection. Fault detection and classification has been a topic of interest for several years and as a result of this a number of techniques have been developed by different researcher from time to time. Some of the important techniques are wavelet transform technique, neural network based technique and fuzzy and fuzzy-neural network based techniques [6].In this paper discrete wavelet analysis has been used to analyze the faulty signal. At the incident of fault, variation of both voltage and current at the location of protective relay is expected. Severity of the distortion from normal waveform depends mainly on the type of fault. The

normal operation of system has taken as reference for our analysis. Wavelet decomposition of the normal state will gives information about occurrence of fault. Faults are simulated for the power system including with ground faults and without ground faults. This can be identified by zero sequence component of the system. Taking the maximum value of the percentage of energy spectrum of signal as the base value of each decomposed waveform and comparing it with the three phase currents during the fault and it is possible to differentiate between similar types of faults. The faulty signal also analyzed with respective approximation and detailed coefficients [1]. These wavelet coefficientscan be helpful to get actual nature of the faulty signal.

IV. CASE STUDY

The general block diagram of a power system is shown in figure.3; it consisting of two power stations (source) connected through a single circuit transmission line of 320 kilometers long. The Simulation model of the system has been developed using Matlab/Simulink software.

Fig 3 : A 400kv Transmission System with Fault at Mid-Point

The line parameters of the power system model are [6],

Line length = 300 km;

• Source voltages:

source 1: v1 = 400 kV; source 2: v2 = 400∠δ kV,

where δ is the load angle;

• Source impedance (both sources):

positive sequence impedance = 1.31 + j15.0 ;

zero sequence impedance = 2.33 + j26.6 ;

• Frequency = 50 Hz;

• Transmission line impedance:

positive sequence impedance = 8.25 + j94.5 ;

zero sequence impedance = 82.5 + j308 ;

positive sequence capacitance = 13 nF/km;

zero sequence capacitance = 8.5 nF/km.

http://en.wikipedia.org/wiki/Discrete_wavelet_transform



http://en.wikipedia.org/wiki/Continuous_wavelet_transform





.

Fig. 4: Simulink model of 400kv Transmission line

. Post fault samples of three phase current, and

voltages have been collected from the circuit breaker of the bus bar 1 and 2 at source 1&2. By using the fault signal data wavelet decomposition bas been performed. The sampling interval is 1ms and the circuit breaker transition time is set to 0.04. The transition time of the fault breaker is set to 0.1 to create (apply) fault in the transmission line network. Applying different type of faults and taking part of the waveform from the circuit breaker, before and after the fault incident is analyzed by discrete wavelet analysis. To determine the involvement of ground in fault, presence of zero sequence components have been considered. It has been observed that for without ground fault is less than 1 and with ground faults is more than 100 [6].

The differentiation of phase-phase like faults have been classified by comparing the percentage of energy of the current and voltage signals. The faulty signal also analyzed using approximated and detailed wavelet coefficients at different decomposition levels.

The simulation can be done by taking the faulty waves from circuit breaker either one end or two ends of the transmission line. Effectiveness of the proposed methodology has been evaluated by conducting different trials. The figure .4 showsMatlab/Simulink model which is used for a case study and a program has been developed on Matlab platform to analyze faulty wave which is taken from the Simulink model.As the transmission line model with two sources on both sides

is a widely accepted model for development of transmission line protective relaying algorithms [6].it has been considered in this present proposed technique.

V. ANALYSIS OF RESULTS

This proposed technique has been developed on the basis of extensive simulation studies carried out on the above 400kv, 320 kilometer transmission line for variations in fault resistance and fault location and load angle using Matlab software. For different types of faults the percentage of energy level of faulted wave has been shown in table. I & table. II and it is observed for involvement of ground during the fault

Fig. 5 : No-fault condition on the transmission line



Fig. 6 : LG (Phase-A to ground) faulton the transmission line

Fig. 7 : LL (Phase-A to Phase-B) faulton the transmission line

Fig. 8 : LLL (Phases-ABC)fault on the transmission line

Fig. 9 : Detection ofLG fault using Symlet wavelet

Fig. 10 : Detection of LL fault using symlet wavelet

Fig. 11 : Detection of LLG fault on transmission line



Fig. 12 : Detection of LLL fault on transmission line

Table I: Wavelet Energy Levels for With-Out Ground Fault

Table II: Wavelet Energy Levels for With Ground Fault

Type of Fault

AG BG CG ABG BCG CAG ABCG %

Energy

Ea 99.65

82 97.1383

96.4346

94.1111

83.4404

89.7138

91.1167

Eb 96.45

67 99.8445

96.4008

95.4431

99.4361

95.0082

99.3986

Ec 96.54

54 97.0531

99.6562

86.2163

98.2805

91.5415

93.9202

The fig.5 shows No-fault condition of transmission line and it is taken as a reference waveform for further analysis of the fault. Fig.6 shows the phase to ground fault condition of the transmission line and it is can be either phase A or B or C to ground fault. Fig 7 shows the double phase fault and it can be occurred between any of two phases. The fig.8 shows three phase fault of the transmission line.

Based on the wavelet decomposition of different faults, it can be analyzed using symlet wavelet to identify various faults accurately. In Fig.9 to fig.12 have been shown, after wavelet decomposition of faulty

signals, which are associated with phase and ground faults and the approximations and details of wavelet coefficients also observed to get basic nature of faults. The fault classification has been done based on the percentage of energy levels of different faults, which are collected from the circuit breaker of the transmission line. By comparison of different energy levels of faults it can be revealed and differentiated easily that type of fault occurred on transmission line. From the above table.I, it has been observed that in phase –phase fault condition the percentage of energy of the faulty phases are less than the un-faulted phases value. From table.II, with ground fault condition, it has been observed that the percentages of energy level of faulty phases are greater than the un-faulty phase’s value due to presence of zero sequence component of the system.

VI. CONCLUSION

The proposed discrete wavelet analysis for identification and classification has been evaluated on a transmission line network. The faulted phases have been detected and type of faults also classified by using percentage of energy levels. A Matlab based program also developed to verify effectiveness of the proposed technique for different system configurations.

VI I. ACKNOWLEDGMENT

The authors are very grateful to their institutes, UCE-JNTUK Andhra University and present organization (C.P.R.I) for direct and indirect support. The authors thankful to Prof.M.Ramalingaraju, other faculty and associates of the electrical and electronics engineering department who are directly or indirectly helped for this work. The authors also thankful to the person behind this work, Mr.Tony Thomasfor his cooperation, encouragement and suggestions.

VIII. REFERENCES

[1] S.M. El Safty and M.A. Sharkas, “Identification of Transmission line faults using Wavelet Analysis”, IEEE Transactions on Industrial Applications, ID: 0-7803-8294-3/04, 2004.

[2] Fernando H. Magnago and Ali Abur, “Fault Location Using Wavelets”, IEEE Transactions on Power Delivery, Vol. 13, No. 4, pp.1475-1480,1998.

[3] Amara Graps, “An Introduction to Wavelets”, IEEE Computational Science & Engineering, pp.50-61, 1995.

[4] Mattew N.O. Sadiku, Cajetan M. Akujuobi and Raymond C.Garcia, “An Introduction to Wavelets in Electromagnetics”, IEEE microwave magazine, pp.63-72, 2005.

Type of Fault

No-Fault

ABC AB BC CA %

Energy

Ea 99.999

4 90.6569 94.6128 99.9994 90.1130

Eb 99.993

3 98.7553 94.5621 99.0723 99.9933

Ec 99.994

6 94.2969 99.9946 99.0631 90.2045



[5] Shyh-Jier Huang and Cheng-Tao Hsieh Ching-Lien Huang, “Application of Morlet Wavelets to Supervise Power System Disturbances”, IEEE Transactions on Power Delivery, Vol.14, No. 1, pp.235-243, 1999.

[6] R.N.Mahanty,P.B.Dutta Gupta, “A fuzzy logic based fault classification approach using current samples only”,EPSR,pp.501-507 ,14 Feb 2006.

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