[2011] a novel fault features extraction scheme forpower transmission line fault diagnosis

8/14/2019 [2011] a Novel Fault Features Extraction Scheme ForPower Transmission Line Fault Diagnosis

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A Novel Fault Features Extraction Scheme ForPower Transmission Line Fault Diagnosis

Adedayo A. Yusuff Adisa A. Jimoh Josiah L. Munda Email: [email protected] [email protected] [email protected]

Department of Electrical Engineering, Faculty of Engineering and the Built EnvironmentTshwane University of Technology Private Bag X680, Pretoria 0001, Staatsartillerie Road,

Pretoria West, South Africa

Abstract This paper proposes a novel transmission line faultdetection and classication scheme, based on a single-end mea-surements using time shift invariant property of a sinusoidalwaveform. Various types of faults at different locations, faultresistance and fault inception angles on a 400 kV - 361 . 65 kmpower system transmission line are investigated. The scheme isused to extract distinctive fault features over 18 of a cycle and

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2 of a cycle data windows. The performance of the feature extractionscheme was tested on a machine intelligent platform WEKAby using two types of classiers, Fuzzy logic reasoning (FLR),and support vector machine (SVM). The result shows that, thescheme can classify all types of short circuit faults on a doublyfed transmission lines. Accuracy between 95 . 95% and 100% isachieved.

I. INTRODUCTION

Faults often occur in power transmission system, whichcause supply interruptions, damages to equipment and affectthe power quality. Therefore, accurate fault detection, faultclassication and fault location estimation is very importantin power transmission system in order to restore power supplyas soon as possible with minimum interruption. Usually,

transmission line protection schemes are divided into threelogical stages; (1) Fault detection and classication, (2) Faultzone estimation or fault location and (3) Decision logic andsubsidiary modules. In cases where stages (2) and (3) dependon stage (1), it is always important that fault detection andclassication are done quick enough to ensure a prompt protec-tion. A major challenge in fault detection and classication liesin the data window size, the delay in feature extraction, and thefeature vector size needed for most algorithm implementations.

Fast Fourier transform (FFT), Discrete Wavelet transform(DWT), Discrete Wavelet Packet decomposition (WPD), HS-transform (HS), and Kalman lters have been used in fault de-tection and fault features extraction schemes. DWT is used in[1] for signal preprocessing and fault feature extraction. In [2]Kalman lters is used in extracting feature based on frequencycomponents; the frequency components are subsequently usedin fault classication. HS Transform (HS) [3], Fuzzy logic[4], articial neural network [5] have been also been usedextensively. Apart from fault feature extraction, various combi-nations of fuzzy logic, wavelets and neural network have beenused for fault detection and classication [6]. In general, FFTis used in estimating the fundamental frequency componentamplitude over 1 cycle period and depending on the change

in the waveform amplitude, a ag is raised. FFT is the basis of most protection relayss algorithms that use positive, negativeor zero sequences for fault detection and classications, sincein most of those schemes, there is a need to rst determinethe phasors of the currents and/or voltages. A major disad-vantage in using FFT lies in the needed data window size forestimating phasors. In order to get an accurate amplitude of

the fundamental frequency component of a waveform, FFTneeds a minimum of a cycle data window, irrespective of thesampling rate used. DWT and WPD schemes have been useddue to the multi resolution capabilities of wavelet transform.In recent time, various wavelet entropy measures are used forfault features extraction, namely, Wavelet Singular Entropy(WSE), Wavelet Energy Entropy (WEE), Wavelet DistanceEntropy (WDE), and Wavelet Time Entropy (WTE) as reportedin [12]. However, the disadvantages of using wavelet lies inits energy leakage [7], computational burden and the fact thatit is not shift invariant.

In the foregoing it can be seen that there is a need to reducethe window size needed for fault detection and classication.

Since all other stages of fault diagnosis depend on the outputof this stage, a performance improvement at fault detectionand classication stage will generally have a synergetic effecton the whole diagnostic scheme.

In this work, we address the problem of data window sizerequired in formulating the fault feature vectors. We use anintrinsic characteristic of sinusoidal waveforms that is shiftinvariant in terms of amplitude. In section II, we lay themathematical background necessary for the proposed scheme.Simulation of a two bus power system, and the applicationof the scheme to fault classication based on a machinelearning framework, WEKA [8], [9] are presented in sectionIII. In Section IV we discuss the accuracy and precisionof the scheme for fault classication, and the conclusion isgiven in section V. The scheme is implemented in MATLABand WEKA. The strength of the proposed scheme lies in itscomputational simplicity, and low processing data window.

II . M ATHEMATICAL BACKGROUND

Consider a sequence S , of samples of sinusoidal waveformat interval t . It is easy to show that a determinant functionD over M s formulated from S is unique and shift invariant,over S for all a + d = b + c.

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S = {y(k), y(k 1), y(k 2), y(k 3), y(k 4), }

M =y(k a ) y(k b)y(k c) y(k d)

(1)

and

D q = A 2 sin( t[(a + d) (b + c)

2 ])sin( t [

(b + d) (a + c)2

])(2)

Equation (2), can be expressed as

D q = A 2 sin( t )sin( t ) (3)

where = (b+ d ) (a + c)2 , and = (a + d ) (b+ c)2 .

For the case a + d = b + c = 3 , we have

D 3 =y(k) y(k 1)

y(k 2) y(k 3)(4)

and it is easy to show that

D 3 = A2

2 (2 2) (5)

where A is the amplitude of the sinusoidal waveform, =2cos( t) and = 2sin( t) A fault in a system is usuallyassociated with a change in the energy of the output signals.

Equation (5) shows that a change in the amplitude of asinusoidal waveform will be reected in D3 . We now showthat D 3 is shift invariant and it is unique for all M denedover S .

Consider a sequence S 1 of a sinusoidal waveform sampledat interval 300 .

S 1 = {0, 12

, 32

, 1, 32

, 12

, 0, 12

, 32

, 1, 32

,12

, 0}D 1 =

0 12 32 1

= 34 D2 =

12

32

1 32=

34

D 3 = 32 1

32

12

= 34 D4 =

1 3212 0

= 34

In general the determinant function D is shift invariant and

it is unique over the sequence S .

III . S IMULATIONThe 400 kV , 50 Hz power system shown in Figure 1 is

simulated in MATLAB/SIMULINK. It represents a segmentof Eskom transmission network. Eskom is the South Africaelectric power utility company. The system parameters usedin the simulation are shown in Table I and Table I I .

A simulation of all types of short circuit faults was per-formed as illustrated in Figure 1, where a fault is initated at afault location of 161.296 km from Perseus substation. Variouscases are simulated by varying fault resistance Z f between

0.001 and 200 , the fault location in steps of 10 km , andthe fault angle between 00 and 900 in steps of 300 . The voltageand current measurements are taken at the bus-bar in Perseusat a sampling frequency of 3.2 kH z . All the measurementsare ltered using a low pass 4th order Butterworth lter witha cut-off frequency of 400 Hz .

Four sequences were formulated S i a ,S i b ,S i c and S i n forphase A , phase B , phase C and the neutral N respectively.

Fig. 1. A segment of Eskom Transmission network

TABLE ITRANSMISSION LINES PARAMETER

length(km) R 0 () R 1 () X 0 () X 1 ()line 361.296 140.79 8.13 396.57 115.10

TABLE IIG ENERATORS PARAMETER

X/R |V |( p.u. ) Angle Short CircuitLevel (MVA)

G 1 18.24 1.02 48.01 14422.68G 2 19.31 1.02 60.66 20770.06

A. Feature Extraction

A good set of fault features forms the foundation of mostclassication and pattern recognition schemes. In order to beable to classify various kind of faults on power transmissionlines, the set of fault features should be compact in sizeand should have little computational burden. In most realworld applications the pertinent signal features are buriedin redundancy and noise, so it is often desired to extractthese information in the most economical way, without lossof important information.

Fault features are extracted based on (5) for all the phasevoltages, neutral voltage, phase currents and neutral current.The neutral voltages and current are formulated based on vn =


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va + vb + vc and in = i a + ib + ic . Di q (k) and Dv q (k)representthe current and voltage features respectively, where q S ={A,B,C,N }. S is a set of the phases and neutral. A featureset F consisting of Di q (k) and Dv q (k) is formulated, andthis is used in formulating the fault pattern matrix P . Thefault feature extraction scheme based on D q has a time delayof 4 data samples, irrespective of the sampling rate. This isbased on the fact that 4 data samples are used in estimatingD q = y(k)y(k 3)y(k 1)y(k 2). In order to ensure thatboth post-fault samples and pre-fault samples are not usedin estimating D q , a time delay of 4 samples after the faultincidence ensures that D q estimation is based on the post-faultsamples alone.

B. Fault Classication

Two types of classiers, Support Vector Machine [10]and Fuzzy Lattice Reasoning [11], are tested on a machineintelligence platform called weka.

TABLE IIISVM CLASSIFIER PERFORMANCES USING FEATURES FROM 4 SAMPLE

DELAYED DATA

Class TP FP Precision Recall F-Measure ROCA-G 0.963 0 1 0.963 0.981 0.981B-G 0.951 0 0.997 0.951 0.974 0.976C-G 0.962 0 1 0.962 0.981 0.981A-B 0.938 0.004 0.958 0.938 0.948 0.967A-C 0.964 0 0.996 0.964 0.98 0.982A-G 0.944 0.001 0.987 0.944 0.965 0.971A-B-G 0.951 0 0.998 0.951 0.974 0.976A-C-G 0.965 0 0.997 0.965 0.981 0.982B-C-G 0.959 0.001 0.989 0.959 0.974 0.9793PHF 0.979 0.041 0.842 0.979 0.905 0.969Avg. 0.96 0.008 0.964 0.96 0.961 0.976

TABLE IVFLR CLASSIFIER PERFORMANCES USING FEATURES FROM 4 SAMPLEDELAYED DATA

Class TP Rate FP Rate Preci sion Recall F-Measur e ROCA-G 0.975 0.023 0.81 0.975 0.885 0.976B-G 0.931 0.091 0.506 0.931 0.656 0.92C-G 0.951 0.03 0.763 0.951 0.846 0.961A-B 0.926 0.006 0.943 0.926 0.935 0.96A-C 0.838 0.003 0.967 0.838 0.898 0.918B-C 0.504 0.004 0.927 0.504 0.653 0.75A-B-G 0.804 0.001 0.983 0.804 0.884 0.901A-C-G 0.884 0.004 0.96 0.884 0.92 0.94B-C-G 0.737 0.006 0.921 0.737 0.818 0.8653PHF 0.865 0.006 0.972 0.865 0.915 0.93Avg. 0.844 0.016 0.884 0.844 0.848 0.914

IV. D ISCUSSION

The proposed scheme has a delay of 4 samples. If thesampling rate is more than 16 samples per a cycle, whichis reasonable in modern day system, fault detection andclassication can be achieved in less than, a quarter of a cycle.The performance of the feature extraction scheme with respectto classication accuracy is shown in Table III - Table V I for a SVM and FLR classiers, while Table V III - TableXI show the confusion matrix for SVM and FLR classiers.

TABLE VSVM CLASSIFIER PERFORMANCES USING FEATURES FROM A QUARTER

SAMPLE DELAYED DATA

Cl ass TP Rate FP Rate Precision Recal l F-Measure ROCA-G 0.998 0 1 0.998 0.999 0.999B-G 0.994 0 1 0.994 0.992 0.982C-G 0.995 0 1 0.995 0.992 0.982A-B 0.998 0 0.996 0.998 0.991 0.984A-C 0.994 0 0.997 0.994 0.99 0.982B-C 0.994 0.001 0.989 0.994 0.996 0.972

A-B-G 0.994 0 1 0.994 0.992 0.982A-C-G 0.997 0 1 0.997 0.993 0.983B-C-G 0.992 0 1 0.992 0.991 0.9813PHF 0.995 0.033 0.98 0.985 0.988 0.981Avg. 0.991 0.006 0.995 0.991 0.992 0.983

TABLE VIFLR CLASSIFIER PERFORMANCES USING FEATURES FROM A QUARTER

SAMPLE DELAYED DATA

Cl ass TP Rate FP Rate Precision Recal l F-Measure ROCA-G 0.992 0.008 0.997 0.992 0.998 0.992B-G 0.994 0.003 0.999 0.994 0.988 0.98C-G 0.995 0.009 0.992 0.995 0.993 0.983A-B 0.999 0.004 0.994 0.999 0.996 0.993A-C 0.991 0.003 0.994 0.991 0.982 0.994B-C 0.895 0.002 0.994 0.985 0.991 0.927A-B-G 0.991 0.001 0.991 0.991 0.995 0.96A-C-G 0.996 0.003 0.993 0.996 0.998 0.952B-C-G 0.997 0.002 0.993 0.997 0.999 0.9583PHF 0.991 0.003 0.996 0.991 0.998 0.974Avg. 0.998 0.005 0.993 0.998 0.999 0.972

The faults type in the tables are given by a = A-G ,b = B -G , c = C -G , d = A-B , e = A-C , f = B -C , g = A-B -G ,h = A -C -G , i = B -C -G , j = 3P HF .

The classication accuracy of the classiers increase withthe delays in data samples used in feature extraction. A delayof 4 data samples produces 96.4% precision when a SVMclassier is used as could be seen in Table III . However, alower precision of 88.4% is achieved when a FLR classier isused and this is shown in Table IV . The results of a delay of 16samples is shown in Table V and Table V I for SVM classiersand FLR classiers with an accuracy of 99.5% and 99.3%for SVM and FLR respectively. This shows a considerableimprovement compared to the case of a delay of 4 samples.

The fault detection pick-up time capability of the pro-posed scheme compared to Wavelet Distance Entropy (WDE),Wavelet Time Entropy (WTE) as reported in [12] is given inTable V I I . It is obvious that the proposed scheme has a betterfault detection pick up time compared to WDE and WTE fast.

TABLE VIICOMPARISON OF FAULT DETECTION PICK -UP TIME BASED ON 20 kHz

SAMPLING FREQUENCY

Schemes Detection time (ms)WTE 4.015WDE 4.001The proposed scheme 0.2

V. C ONCLUSION

A novel feature extraction scheme for an ultra-fast classi-cation of fault type on power transmission line is proposed inthis paper. The data window size and computational burden are


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low. Various level of classication accuracy can be achievedby using a data window size of between 18 and

14 of a cycle.

A 99.9% accuracy was achieved with a delay of 14 of a cycle.

TABLE VIIISVM C ONFUSION MATRIX FOR 4 SAMPLE DELAYED DATA

a b c d e f g h i j3379 0 0 0 0 0 3 0 0 128

0 3339 0 0 0 2 5 0 35 1290 0 3377 0 1 0 0 11 3 1180 0 0 3293 0 0 0 0 0 2170 0 0 0 3385 0 0 0 0 1250 0 0 0 0 3314 0 0 0 1960 0 0 37 0 0 3339 0 0 1340 0 0 0 13 0 0 3388 0 1090 9 0 0 0 2 0 0 3366 1330 0 0 108 0 41 0 0 0 6871

TABLE IXSVM CONFUSION MATRIX FOR A QUARTER SAMPLE DELAYED DATA

a b c d e f g h i j3504 0 0 0 0 0 0 0 0 60 3383 0 0 0 0 0 0 0 127

0 0 3386 0 1 0 0 0 0 1230 0 0 3396 0 0 0 0 0 1140 0 0 0 3382 0 0 0 0 1280 0 0 0 0 3315 0 0 0 1950 0 0 15 0 0 3384 0 0 1110 0 0 0 9 0 0 3393 0 1080 0 0 0 0 0 0 0 3378 1320 0 0 0 0 38 0 0 0 6982

TABLE XFLR CONFUSON MATRIX FOR 4 SAMPLE DELAYED DATA

a b c d e f g h i j3424 3 0 0 0 0 31 52 0 00 3267 2 0 0 0 16 0 222 30 100 3337 0 1 0 0 71 1 0

135 0 0 3250 0 0 0 0 0 1250 201 347 0 2942 0 0 0 0 200 1716 0 0 0 1770 0 0 0 24466 218 0 0 0 0 2821 4 0 150 22 334 0 0 0 0 3103 0 10 814 107 0 0 0 1 2 2586 0153 115 249 195 98 140 0 0 0 6070

TABLE XIFLR CONFUSION MATRIX FOR FOR A QUARTER SAMPLE DELAYED DATA

a b c d e f g h i j3482 0 0 0 0 0 3 0 0 00 3453 0 0 0 0 1 0 5 10 0 3423 0 0 0 0 v2 0 00 0 0 3473 0 0 0 0 0 30 0 0 0 3479 0 0 0 0 10 0 0 0 0 3002 0 0 0 21 1 0 0 0 0 3234 0 0 00 0 3 0 0 0 0 3180 0 10 0 0 0 0 0 0 1 3220 00 1 0 3 3 2 0 0 0 6675

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[2011] a novel fault features extraction scheme forpower transmission line fault diagnosis

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