A Fault Line Selection Algorithm Using Neural Network Based on S-Transform Energy

8/10/2019 A Fault Line Selection Algorithm Using Neural Network Based on S-Transform Energy

1/5978-1-4244-5961-2/10/$26.00 2010 IEEE 1478

2010 Sixth International Conference on Natural Computation (ICNC 2010)

A Fault Line Selection Algorithm Using Neural

Network Based on S-Transform Energy

Shu Hongchun, Qiu GefeiLi Chaofan

School of Electrical Engineering

Kunming University of Science and Technology

Kunming, Yunnan Province, China

Peng Shixin

Kunming Power Supply Bureau

Kunming 650011, Yunnan Province, China

AbstractAn approach to detect fault line in distribution network

using neural network based on S-transform energy is proposed undafter analyzing the variance of fault characteristic frequency of zerosequence current in each feeder line of overhead line and

underground cable mixed lines. In order to avoid the effect of TAsdisconnection angle, the short window data of first 1/4 cycle areselected. The S-transform is carried out to determine the maincharacteristic frequency of fault zero sequence current, and taking the

Short Window energy of the main characteristic frequency as thetarget input to form BP neural network model, thus the fault line can

be detected adaptively. State component and various noises can befiltered out utilizing S-transform to determine the main characteristic

frequency. Fault detecting margin can be enhanced by adjusting theweight of criterion through neural network training accurately. Thetheoretic analysis and simulations demonstrate the feasibility andvalidity of this approach, also the problem that training time is too

long and network result is too complex is well solved when usingtraditional neural network to detect fault line.

Keywords- S-transform energy; main characteristic frequency;

neural network; overhead line and underground cable

I.

INTRODUCTION

Fault in distribution network mostly is single phase fault.

In recent years, the massive researches on fault line selection

technology have been done by scholars. And lots of fault line

selection methods based on stable component[1,2], transient

component[3-10]and the selection of wave head of traveling

wave[11-18] were proposed. There are richness transient

component of single phase fault in distribution network, and

the acquisition of transient component does not need too high

sampling frequency, so the fault line selection method based

on transient component attracts scholars attention. By now,

the veracity and reliability of fault line selection can not

satisfy the project actual request by far, so it is necessary to

process fundamental research and project practice.

S-transformed is a kind of time-frequency analysis method

which based on continual wavelet transformation and short-

time Fourier transformation. The direct result of extracted

signal been S-transformed is a duplicate time-frequency

matrix, which include the information distributed by time and

frequency such as signal peak-to-peak value and phase etc. It

supplies the foundation information to extract each kind of

character of the signal.

BP network is a sort of multi-layered, forward feed neural

network. Its neuron's transfer function is the S form function,

and the output is a continual quantity between 0 and 1, which

realizes the random nonlinearity mapping from the input to the

output.By analyzing the variance of fault characteristic frequency of

zero sequence current in each feeder line of overhead line andunderground cable mixed lines, the short window data of first 1/4

cycle are selected to detect fault line in distribution network usingneural network based on S-transform energy. The S-transform iscarried out to determine the main characteristic frequency of faultzero sequence current, and taking the Short Window energy of themain characteristic frequency as the target input to form BP neural

network model, thus the fault line can be detected adaptively.

II. ELECTROMAGNETISM TRANSIENT CHARACTERISTIC

ANALYSIS OF MIXED CABLE-LINE GRID

Without lose of generality, take a mixed cable-line

resonance grounding system as example. The model of

110kV/35kV distribution grid with single phase grounding

fault is constructed as Fig. 1, it has 6 lines, and the neutral

point of Z transformer has been grounded via arc

suppression coil, the current transformer is LSJC-35 type. The

overhead lines L1=15kmL3=18kmL5=30kmcable-line

mixed line L4=17kmin which the length of overhead line is

12km and the length of cable is 5km, the cable L2=6km

L6=8km. In which the overhead line use JS1 pole and LGJ

70 line conductor, the line span is 80m, the type of cable is

YJV23-35/95.

L1

35kV110kV

TZ

L

R

T

K

CBA ..

.C BA

.. .

. ..

... . ..

. ..Rf

.

... . ..

L2 L3 omission

L4

L6

L5 omission

i01

i04

i06

load

load

load

Fig.1 Radial resonant earthed system

When single phase fault occurs in resonance grounding

system with 10% over compensation, the transition resistance

is 20and the closing angle of fault is 90, the zero-sequence

current of fault line L1 and sound line L2, L3 is shown in


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Figure 2. The process of the electric charge and discharge in

the capacity between sound lines and earth are similar. Due to

action of the additional zero-sequence current source, the

transient component waveforms of zero-sequence current of

fault lines are the most distinct from the other lines.

0 0 .01 0.02 0 .03 0 .04-200

0

200

400

600

t/s

i/A

0 0.01 0.02 0.03 0.04-150

-100

-50

0

50

100

t/s

L2

L3

i/A

(a) fault line (b) sound line

Fig.2 Fault zero-sequence current of 90o

III.

CALCULATION OF SHORT WINDOW ENERGY USING S-

TRANSFORM

A. Theory of S-Transform

S-transformed is a kind of time-frequency analysis method

which based on continual wavelet transformation and short-

time Fourier transformation.

The discrete form of S-transform can be expressed as:

[ ] [ ]2 2 2

12 / j2 /

0

, 0N

k n km N

k

S m n X n k e e n

=

= + 1

[ ] [ ]1

0

1, 0

N

k

S m n x k nN

=

= = 2

in which

[ ] [ ]1

j2 /

0

1 Nkn N

k

X n x k eN

=

= 3

B.

Transient Energy

Zero sequence energy function of each feeder is defined

as:

0 00

( ) ( ) ( ) =1,2, ,6t

i iW t u i d i = 4

In which, Wi(t)-zero sequence energy function of Li after

fault, u0(t)-zero sequence voltage of bus i0i(t)0-zero

sequence current of Li.

Transient energy function of the first 1/4 cycle after fault is

defined as:T

40 0

0

T( ) ( ) ( ) =1,2, ,6

4i i

W u i d i = 5

Utilizing S-transform to extract the characteristic of each

frequency of each signal and according to formula (1),(2) and(5) simultaneously transient energy of each line on fn by S-

transform is defined as:

[ ]2

( , ) =1,2, ,6i nm

W S m n i

= 6

C. Determination of Characteristic Frequency

Sample data zero current in the first 1/4 cycle after fault

was analyzed utilizing S-transform and frequency spacing oftwo adjacent frequencies is obtained according to thedefinition of frequency S-transform resolution.

200Hzsf

fN

= = 7

The energy distribution of fault line L1and sound lines on

frequency fn are shown in Fig.3.

5 10 15 20 250

1

2

3

4x10

m

W5

5 10 15 20 250

1

2

3

4x10

m

W

4

(a) fault line L1 (b) sound line L2

5 10 15 20 250

100

200

300

400

500

m

W

5 10 15 20 250

1

2

3

m

Wx10

4

(c) sound line L3 (d) sound line L4

5 10 15 20 250

500

1000

1500

m

W

5 10 15 20 250

2

4

6

8

m

Wx104

(e) sound line L5 (f) sound line L6

Fig.3 Energy distribution at each frequencyL1 fault

As shown in Fig.3, short window energy of all lines is

largest on frequency 400Hz and the energy on this frequencycan reflect the characteristics of fault short window energy.

5 10 15 20 250

20

40

60

80W

m 5 10 15 20 250

1000

2000

3000

m (a) sound line L1 (b) sound line L2

5 10 15 20 250

10

20

30

40

50

m

W

5 10 15 20 250

5

10

15x10

m

W4

(c) sound line L3 (d) fault line L4

5 10 15 20 250

20

40

60

80

100

m

W

5 10 15 20 250

2000

4000

6000

m

W

(e) sound line L5 (f) sound line L6

Fig.4 Energy distribution at each frequencyL4 fault

When fault occurred on L4 with 20o closing angle through

200 transition resistance, transient energy of each line on

each frequency point fn were obtained by performing S-


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transform on zero sequence current of each line in 1/4 cycleafter fault, which is shown in Fig.4.

As shown in Fig.4, energy of L2, L4, L5, L6 concentrateon f0=0Hz mostly, i.e. direct current component, and energy

of L1 and L3 concentrate on the frequency 3600Hz and

3000Hz respectively. While transient energy of fault line L4 is

100 times larger than other ones. Transient energy sum on

each frequency point fn were obtained according to formula

(6) by adding the energy on each frequency point of all lines.

[ ]2

( , ) =1,2, ,6ni m

W S m n i= 8

In term of formula (8), sum up the energy on each

frequency point fn of each line in Fig.4 and the result is shown

in Fig.5.

5 10 15 20 250

5

10

15x10

m

W

4

Fig.5 Total energy distribution at each frequency

From Fig.5 and Fig.6, it can be concluded that the

frequency with largest energy sum on each frequency point of

each line is consistent with the one on which fault linesenergy is mostly concentrated, therefore the frequency with

largest short window energy of zero sequence current in all

lines is defined as main characteristic frequency ftz of fault.

D.

Transient Energy of Characteristic Frequency

Short window energy on main characteristic frequency ofeach line can be calculated according to formula (6) after main

characteristic frequency is determined:

[ ]2

_ ( , ) =1,2, ,6i tem

W S m k i= 9

In the cases that shown in Fig.3 and Fig.4, the

distribution of short window energy on main characteristic

frequency of each line is shown in Fig.6.

1 2 3 4 5 60

1

2

3

4

5

i

Wx10

1 2 3 4 5 60

5

10

15x10

i

W

4

(a) line L1fault (b) line L4fault

Fig.6 Energy distribution at characteristic frequency of each line

IV. DESIGN OF BPNETWORK

A. Structure Design Based on BP Network

(1) Input layer and output layer design

Fault line's short window energy is the largest, so fault lineselection can be realized by comparing the short windowenergy of all the lines. When fault occurs, taking short window

energy of 6 lines of the system in Fig.1 as input layer

information, thus the dimension of input layer, whichrepresent short window energy of the 6 lines, is 6.

(2) The selection of hidden layer numberThe performance of the network depends on the number of

hidden layer directly. A single hidden layer BP network may

approach any complicated function. With the single hidden

layer, the triplex layer neural network can carry out any

complicated function mapping. The triplex layer network is

chosen in this article chooses with single hidden layer to makethe network in simplification.

(3) The neuron number of hidden layer

By the methods of the hidden layer neuron number

determination such as trim method, growth method, self-adapting method etc, the range of the hidden layer neuron

number is determined in [5,14]. According to the sample dataand the designed network structure, the neuron number of the

concealment layer is finally fixed on 8 through simulation

testing utilizing the data mining tools like WEKA and the

neural network toolbox.

The structure of BP network is shown in Fig.7.

Fault

Fault

Input

Layer

Output

Layer

Hidden Layer

Fault

Fault

Fig.7 Structure of BP network

B.

The Parameter Design of BP Network

(1) The selection of network initial value

The range of initial value from the input layer to the

hidden layer is (0, 1/ 6 ), and the range of initial value from

the hidden layer to output layer is setting as (02/ 3 ).

(2) The setting of network study parameter

The experiment is processed with the goal of definite

performance as 0.1. Train the network unceasingly by

selecting different study rate and momentum constant, afterthe weight values reach the steady state, it is found that the

effect of network study is most ideal when setting studyparameter as 0.3 and momentum constant as 0.2.

V. THE TRAINING OF BPNETWORK

A.

The Selection of Training Samples

Simulation was performed when single phase grounding

fault occur in the distribution grid shown in Fig.1, while faultposition was separately set at 20%, 40%, 60%, 80% of the

total length in each line, the transition resistance is separately20, 60, 80, 100, 120, 150, 200, 250 and the

closing angle is separately 0, 30, 45, 60, 90. 20 different

types of bus faults were designed and thus 860 groups of

waiting training samples were obtained.


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Initial training samples were pretreated, i.e. short windowenergy data from zero sequence current of the 6 lines were

extracted and finally 860 groups of training samples wereobtained.B.

The Pretreatment of Sample Data

To eliminate the effect of energy absolute value of

networks study ability, normalized pretreatment is performed

on study sample data according to formula (9). Thus the

sample date ranges in the zone [0,1] after normalizedpretreatment.

* min

max min

W WW

W W

=

9

In which, W*-short window energy of line after

normalized pretreatment, W-absolute value, WmaxWmin

maximum and minimum short window energyin 6 lines

C.

Error Criterion Function

Order N as the sum of the modes including training set

and the average value of error energy is:

1

1( )

N

av

n

E E nN =

= (10)

In which,E(n)-instantaneous value of error energy.D.

Stop Condition of Training

Two conditions that the value of minimum error criterion

function and the maximum iteration times are chosen as the

condition to stop training and judge convergent of network.

VI. IDENTIFICATION OF BPNETWORK

As for a trained network, the process of identification is

just a recall process of network. Prescribe that the output valueof component reaching 0 when it is less than or equal to 0.2

and reaching 1 when it is more than or equal to 0.8. The output

value of component reaches 1 means the lines correspondingly

is fault, while all output values of component reach 0 resultingin the bus fault.

VII. METHOD OF FAULT LINE SELECTION

The schematic diagram of fault line selection is shown in

Fig.8. Firstly, characteristic frequency of transient zero

sequence current is determined according to the maximumenergy sum criterion, then the transient energy on each

frequency point of each line is extracted as the characteristicvalue to perform of training neural network and thus fault lineis obtained through adjusting the weighting value and outing

the value.

Fig.8 Schematic diagram of fault line selection

VIII. SIMULATION

Samples with different closing angle and transition

resistance of various fault types were selected as testing onesto verify the stability and validity of the system throughnetwork recall. Limited by the pages, only part simulation

results are shown in Table 1.compared with the actual

situation, precision of fault line selection reaches 99.31%.

TABLE I. RESULT OF FAULT LINE SELECTION

Fault type LiXf

(km)

Rf()

(deg)Output of network recall Fault line

Line fault

1 2

40 0 [10.00010000.0002] L1

300 30 [0.92240.00010.00240.010.00020.248] L1

40 60 [0.98250.00040.1470.2480.00660.0541] L1

300 90 [0.968500.01120.0040.00030.1786] L1

5

15300 0 [0.00180.0020.02650.0020.9980] L5

40 30 [0.00050.00110.0250.000510.046] L5

28300 60 [00.16740.01530.00010.8760.004] L5

400 90 [0.00260.00350.02680.00210.91340.001] L5

Bus fault bus 0

300 0 [0.05750.00010.19680.00330.09890.0054] bus

40 30 [0.00170.02970.00540.00130.00190.057] bus

20 60 [0.09640.00010.00080.006400.0029] bus

200 90 [0.2250.00540.0160.18510.380.021] bus

Arc fault 3 10 40

0 [0.14370.12620.998700.11710.0296] L3

30 [0.0010.00010.79250.03660.17630.0361] L3

60 [0.13890.07120.99990.34250.00010] L3

90 [0.05940.06370.86270.04140.01060.016] L3

Note: Li-fault line; Xf-distance from fault point to bus; Rf-transition resistance-fault closing angle.

IX. CONCLUSION

The fault line selection algorithm proposed in this paperusing neural network based on S-transform adopts T/4 short

window data, thus CTs saturation disconnection angle is

avoided; utilizes S-transform to extract fault transientinformation, the effect of fundamental frequency component

and other non characteristic frequency band component are

eliminated, so the reliability and sensitivity are both improved;

ANN1

ANN2

ANN6

.

.

.

FAULT

FAULT

FAULT

FAULT

FAULT

FAULT

1_

2_

6_

W

W

W

te

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A Fault Line Selection Algorithm Using Neural Network Based on S-Transform Energy