A Novel Integrated Protective Scheme for Transmission Line Using ANFIS - Dr. Adel a. Elbaset

7/30/2019 A Novel Integrated Protective Scheme for Transmission Line Using ANFIS - Dr. Adel a. Elbaset

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A Novel Integrated Protective Scheme for

Transmission Line Using ANFIS

Faculty of Engineering,

Minia University, Minia, Egypt

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To implement a novel application of ANFIS approach to faultdetection, classification and location in transmission lines a proposed

computer program based on Matlab software to calculate all tentypes of shunt faults that may occur in a transmission line should be presented first.This work divided into two papers

The first paper concerns with Modeling and Computer Simulation of Fault Calculations for Transmission Lines.The second paper concerns with A Novel Integrated ProtectiveScheme for Transmission Line Using ANFIS.

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In the first paper: Part IThis paper presents a proposed computer program based on Matlab software tocalculate all ten types of shunt faults that may occur in a transmission line.

Various fault scenarios (fault types, fault locations and fault impedance) areconsidered in this paper. The inputs of the proposed program are line length,source voltage, positive, negative and zero sequence for source impedance, linecharging, and transmission line impedance. The output of the algorithm is usedto train an artificial intelligence networks to detect, classify and locatetransmission lines faults. Simulation results have shown the effectiveness of thealgorithm under the condition of all types of shunt faults.

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Presents a novel application of ANFIS approach to faultdetection, classification and location in transmission.

Three ANFISs have been proposed to provide an integrated protective scheme for the transmission line.The first ANFIS has been proposed to detect all shunt faults.

In the Second paper: Part II

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The last one has been proposed to determine the distance of thefault from sending end.

The second ANFIS has been proposed to identify the type of

faults.


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Methodology

Bus 3

f f

Point F

Bus 2Bus 1

L L-LSending End S

Ss, Vs, Is Sr, Vr, Ir

Reciving End R

Figure 1. Faulted Transmission Line

The fault formula for balanced and various unbalanced faults are summarized below:- II-I Modeling of SLG Faults Using Z bus

Zf

Bus 3Bus 1 Bus 2

Ifa

Ifb

Ifc

a

b

c

Vsa

Vsb

Vsc

Ia

I b

Ic

Z.Lf Z.(L-L f )

Z.Lf Z.(L-L f )

Z.Lf Z.(L-L f )

Figure 2. Single-line-to-ground fault at point F

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The conditions at the fault bus 3 (point F) are expressed by the followingequations:-

Ifb=0 , I fc=0 (1)

The following equations give relationships between sequence components of fault currents at the fault point.

The fault line current at bus 3 can be calculated as follows [2,3,4,5]:

VPre f (0): The prefault voltage at point F (bus 3) and can be calculated byusing two port network as shown in Fig. 3

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Zs A 1

( L f )

B 1

( L f )

C 1

( L f )

D 1

( L f )

A ( L

-L f )

B ( L

-L f )

C ( L

-L f )

D ( L

-L f )

Zr

Z f

Vf

VsaVra

Figure 3. Power system in SLG fault

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II-III Modeling of DLG Faults Using Z bus [2,5]

Bus 3Bus 1 Bus 2

I fa

I fb

I fc

a

b

c

V sa

V sb

V sc

I a

I b

I c

Z.L f Z.(L-L f )

Z.L f Z.(L-L f )

Z.L f Z.(L-L f )

Z f

Figure 5. Double line to ground fault at point F

The symmetrical components of fault current is

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The fault line currents can be obtained from Equation

Where T is known as symmetrical components transformation matrix

II-IV Modeling of three-phase Faults Using Z bus

Bus 3Bus 1 Bus 2

I fa

I fb

I fc

a

b

c

V sa

V sb

V sc

I a

I b

I c

Z.L f Z.(L-L f )

Z.L f Z.(L-L f )

Z.L f Z.(L-L f )

Zf

Figure 6. A balanced three-phase fault at point F Slide 10 of 51


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The symmetrical components of fault current are given by the following Equation:-

The fault line currents can be obtained from Equation (6)

II-V Modeling of Sending Voltage and Line current during Fault [5]

Using sequence components of the fault currents the symmetrical components of thesending end bus voltage during fault can be obtained by the following Equations

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Where Z 1, Z 2 and Z 0 : The matrix impedance of transmission line per unitlength.Vsa (0): The prefault phase voltage at sending end. The phase voltages at

sending end during fault are

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The symmetrical components of fault current in line 1 to 3 is given by

The line fault currents from Bus 1 to Bus 3 (point F) can be obtained as follows:-

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Computer Simulation of Fault Calculation

The flow chart of the proposed computer program is shown in Fig. 7. The proposed computer program simulates various faults for different faultconditions. i.e. a-g, b-g, c-g, ab, bc, ca, ab-g, bc-g, ca-g, and abc fault) based onZbus. The condition parameters that have been taken into account for each faulttype are:

1) Variation of fault impedance, [0: 200 ] ().

2) Variation of fault angle, [0: 90] (degree).3) Variation fault locator [1:200] km.

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Application and Results

The faulted transmission line is represented by distributed parameters. As an

application, a 200 km overhead transmission line with the parameters of thetransmission line model of Fig. 1 is as followsSource voltages:Source S: V 1 = 400 kV; source R: V 2 = 400 kV.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.The significant parameters above are selected based on in real operations [8].

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Influence of the Fault Distance and Fault Impedance

Figure 8. Influence of Fault Impedance, Fault distance at Fault current For Single Line-to-ground Fault at =10 o.

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Figure 9. Influence of Fault Impedance, Fault distance at Fault voltage For DLG Fault at =10 o

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Figure 10. Influence of Fault Impedance and Fault distance at Fault currentFor DL Fault at =10 o.

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Figure 11. Influence of Fault Impedance, Fault distance at Fault currentFor Three phase Fault at =10 o.

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Figure 15. Influence of Fault Impedance, Fault distance at Fault voltage For Three phase Fault at =10 o

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Figure 25. Influence of Fault distance and Fault inception angle atFault current For DLG Fault at Zf =30 ohm.

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Figure 20. Influence of Fault Impedance and Fault distance atFault voltage For SLG Fault at Lf =5km.

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Figure 22. Influence of Fault Impedance and Faultdistance at Fault voltage For DL Fault at Lf=5km.

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Figure 27. Influence of Fault distance, Fault inception angle atFault current For Three phase Fault at Zf =30 ohm.

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Figure 29. Influence of Fault distance and Faultinception angle at Fault voltage For DLG Fault at Zf=30 ohm.

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CONCLUSIONS This paper presents a computer package to perform transmission line faultanalysis based on Z bus methods along with the symmetrical componentsmethod. From the results obtained, the salient conclusions of this paper are:-

1. Calculates the fault conditions and to provide protective equipment designedto isolate the faulted zone from the reminder of the system in the appropriatetime.

2. Presents a highly accurate transmission line simulation technique whichutilized to calculate voltages and currents at the relay location (Sending end S)for different fault types, fault conditions and different power system data.

3. Calculate faults along different line lengths.

The results show that the method is suitable for design a protective schemefor transmission line based on artificial intelligence. As the method is easyapplicable and it is flexible, it can be used for modeling other anytransmission lines

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A Novel Integrated Protective Scheme for Transmission Line Using ANFIS

In the Second paper: Part II

This paper presents a novel application of ANFIS approach to fault detection,classification and location in transmission lines using measured data from oneterminal of the transmission line.

Three ANFISs have been proposed to provide an integrated protective scheme forthe transmission line.

The first ANFIS has been proposed to detect all shunt faults.

The second ANFIS has been proposed to identify the type of faults.

The last one has been proposed to determine the distance of the fault fromsending end.

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ANFIS Algorithm The ANFIS is a fuzzy Sugeno model of integration where the final fuzzyinference system is optimized via the ANNs training. The ANFIS makes useof a hybrid learning rule to optimize the fuzzy system parameters of first-

order Sugeno system, which can be graphically represented by Fig. 1. Itmaps inputs through input membership functions and associatedparameters, and then through output membership functions to outputs.

A 1

A 2

B 1

B 2

x 1

x 2

N

N

y

x 1

x 1

x 2

x 2

w 1

w 2

w 1

w 2

y 1=w 1f 1

y 2=w 2f 2

Layer 1 Layer 2 Layer 3 Layer 4 Layer 5

Fig. 1. ANFIS architecture for a two-input, two-rule first-order Sugeno model .

For a first-order Sugeno fuzzy model, a typical rule set with two fuzzy if-then rules can be expressed as [18]:Rule 1If x 1 is A 1 and x 2 is B1, then y 1= p 1x1 + q 1x2+r 1,Rule 2If x1 is A 2 and x 2 is B2, then y 2= p 2x1 + q 2x2+r 2,Where

[A 1,A 2, B1, B2] are called the premise parameters.[p i, q i, r i] are called the consequent parameters, i =1,2.The consequent parameters (p, q, and r) of the n th rule contribute through a first order polynomial of the form:

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ANFIS constructs a fuzzy inference system (FIS) whose membership functionparameters are tuned (adjusted) using either a back propagation algorithm alone,or in combination with a least squares type of method. ANFIS is much morecomplex than the fuzzy inference systems, and is not available for all of the fuzzyinference system options.

The following is a layer by layer description of a two input two rule first-orderSugeno system.Layer 1. Generate the membership grades:

Layer 2. Generate the firing strengths. Each node in this layer calculates the firing strengths of each rule The outputs of this layer can be represented as

Layer 3. Normalize the firing strengths. The i th node of this layer calculates the ratio of the i th rules firing strength to thesum of all rules firing strengths:

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Layer 4. Calculate rule outputs based on the consequent parameters. The output of each node in this layer is simply the product of the normalizedfiring strength and a first-order polynomial. Thus, the outputs of this layer aregiven by

Layer 5. Sum all the inputs from layer 4. There is only single fixed node labeled with . This node performs the summationof all incoming signals. Hence, the overall output of the model is given by

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FAULT DETECTION , CLASSIFICATION AND L OCATION ALGORITHM Power System under Study

To evaluate the performance of the proposed ANFIS integrated protective scheme,

Let us consider a faulted transmission line extending between two sources as shownin Fig. 2 is considered in this study

Fig. 2. Faulted transmission line.PT: Potential Transformer, CT: Current Transformer, CB: Circuit-Breaker, FD:

Fault Detector, FC: Fault Classification, FL: Fault Locator.

Bus 3

f f

FL

CT

PT

CBPoint F

Bus 2Bus 1

L L-LSending End S

Ss, Vs, Is Sr, Vr, Ir

Reciving End R

DataAcquisition

Unit

ANFIS 1

ANFIS 2ANFIS 3

FD

FC

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h f d f f l d ( ) h f f l d


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The first ANFIS used for fault detector (FD). The ANFIS for fault detector outputis indexed with either a value of 1 (the presence of a fault) or 0 (the non-faultysituation).The second ANFIS is used to identify the type of fault located in the firstprotection zone of the transmission line covering 100% of the line length fromthe sending end data only and classify the fault (i.e., a-g, b-g, c-g, a-b, b-c, c-a,a-b-g, b-c-g, c-a-g).

To classify the fault, the following methodology has been adopted. Initially, inorder to represent the fault type correctly, a binary coding system has beendeveloped. In this coding system, a 4-b binary number is used to represent thetype of fault. Thus, for a line-to-ground (a-g) fault, the 4-b number would be 0-0-0-1. Similarly, for a line-to-line (b-c) fault, the corresponding 4-b number wouldbe 0-1-1-0. Similarly, this 4-b binary number also represents the other types of fault. The complete binary coding system and equivalent decimal numbers forrepresenting all possible types of faults is given in Table 1

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A li ti f ANFIS d R lt


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Application of ANFIS and ResultsThe design process of the ANFIS fault detector, classifier and locator go through the following steps:

1- Generation a suitable training data.In order to use the ANFIS technique for fault detection, classification and location the input parameters limit should be determined

precisely. The input parameters are obtained from recording devices sparsely located at sending end in a power system network. Examplesof recording devices may include digital fault recorders (DFR), digital relays, or other intelligent electronic devices (IED).The outputindicate where the fault occurred and classified. Due to limited available amount of practical fault data, it is necessary to generatetraining/testing data using simulation. To generate data for the typical transmission system, a computer program have been designed togenerate training data for different faults.

2- Selection of a suitable ANFIS structure for a given application.Various ANFIS are designed to accurately detect, classify and locate all types of faults on transmission lines.

3- Training the ANFIS.Various network configurations were trained in order to establish an appropriate network with satisfactory performances. The ANFISs are

trained to detect presence of fault, classify fault and finally where the fault position is.

4- Evaluation of the trained ANFIS using test patterns until its performance issatisfactory .When Network is trained, ANFISs should be given an acceptable output for unseen data. When output of test pattern and networks error reached an acceptable range then, fuzzy system is adjusted in the best situation which means the membership functions and fuzzy rules arewell adjusted.All of these steps above are done off-line and when the structure and parameters of ANFIS are adjusted, it can be used as an on-line faultdetector, classifier and locator.

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4.1. ANFIS FOR F AULT DETECTOR AND CLASSIFIACTION


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Fig. 9. Results of Test ing ANFIS for fault Detector

Fig. 10. Relation between RMS Error and Number of Test cases

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Fig. 11. Membership function of Inputs Variable for Fault detection

Fig. 12. Structure of ANFIS for Fault Detect ion

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The structure of an ANFIS with four inputs and one output is shown in Fig. 12.The ANFIS has the following design parameters:-Type - Sugeno,

-Gaussian and Generalized bell-shaped membership functions,-Two linguistic terms for each input membership function,-16 linear terms for output membership functions,-16 rules (resulting from number of inputs and membership function terms),-Fuzzy operators: product (and), maximum (or), product (implication), maximum(aggregation), average weight (defuzzification).

There are 16 rules which are sufficient to assign a detector using ANFIS. Some of these rules are as follows:1-If (Ia is in1mf1) and (Ib is in2mf1) and (Va is in3mf1) and (Vb is in4mf1) then (Output is out1mf1) (1)2-If (Ia is in1mf1) and (Ib is in2mf1) and (Va is in3mf1) and (Vb is in4mf2) then (Output is out1mf2) (1)3-If (Ia is in1mf1) and (Ib is in2mf1) and (Va is in3mf2) and (Vb is in4mf1) then (Output is out1mf3) (1)4-If (Ia is in1mf1) and (Ib is in2mf1) and (Va is in3mf2) and (Vb is in4mf2) then (Output is out1mf4) (1)5- If (Ia is in1mf1) and (Ib is in2mf2) and (Va is in3mf1) and (Vb is in4mf1) then (Output is out1mf5) (1)

15-If (Ia is in1mf1) and (Ib is in2mf1) and (Va is in3mf2) and (Vb is in4mf2) then (Output is out1mf15) (1)16- If (Ia is in1mf1) and (Ib is in2mf2) and (Va is in3mf1) and (Vb is in4mf1) then (Output is out1mf16) (1)

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Fig. 13. Results of Testing ANFIS for Fault classification

Fig. 14. Relation between RMS Error and Number of Test cases for Fault classification

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Fig. 15. Membership function of Input Variables for Fault Classification

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The ANFIS has the following design parameters:Type - Sugeno,Gaussian and Generalized bell-shaped membership functions,Two and three linguistic terms for each input membership function,36 linear terms for output membership functions,36 rules (resulting from number of inputs and membership function terms),Fuzzy operators: product (and), maximum (or), product (implication),maximum (aggregation), average weight (defuzzification).There are 36 rules which are sufficient to assign a detector using ANFIS.

SOME OF THESE RULES ARE AS FOLLOWS:If (Ia is in1mf1) and (Ib is in2mf1) and (Va is in3mf1) and (Vb is in4mf1) then (Output is out1mf1) (1)If (Ia is in1mf1) and (Ib is in2mf1) and (Va is in3mf1) and (Vb is in4mf2) then (Output is out1mf2) (1)If (Ia is in1mf1) and (Ib is in2mf1) and (Va is in3mf2) and (Vb is in4mf1) then (Output is out1mf3) (1)

If (Ia is in1mf1) and (Ib is in2mf1) and (Va is in3mf2) and (Vb is in4mf2) then (Output is out1mf4) (1)If (Ia is in1mf1) and (Ib is in2mf2) and (Va is in3mf1) and (Vb is in4mf1) then (Output is out1mf5) (1)

If (Ia is in1mf1) and (Ib is in2mf1) and (Va is in3mf2) and (Vb is in4mf2) then (Output is out1mf35) (1)If (Ia is in1mf1) and (Ib is in2mf2) and (Va is in3mf1) and (Vb is in4mf1) then (Output is out1mf36) (1)


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Fig. 17. Results of Testing ANFIS for Single Line-To-Ground fault

Fig. 18. Relation between RMS Error and Number of Test cases for Fault Locator

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Fig. 19. Membership function of Input Variables for Fault Locator

Fig. 20. Structure of ANFIS For fault locator Slide 47 of 51


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3- The proposed ANFISs can be implemented for all types of shunt fault includinghigh impedance and low impedance fault.4- The ANFIS has the following design parameters for the configuration for detectingfault are:Type - Sugeno,Gaussian and Generalized bell-shaped membership functions,Two linguistic terms for each input membership function,16 linear terms for output membership functions,16 rules (resulting from number of inputs and membership function terms),

4- The ANFIS, which used for fault classification has the following design parameters:Type - Sugeno,Gaussian and Generalized bell-shaped membership functions,Two and three linguistic terms for each input membership function,36 linear terms for output membership functions,36 rules (resulting from number of inputs and membership function terms),

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5- The ANFIS, which used for fault location has the following designparameters:Type - Sugeno,

Gaussian membership functions,Three and Four linguistic terms for each input membership function,144 linear terms for output membership functions,144 rules (resulting from number of inputs and membership functionterms),

6- The proposed methodology for output based on ANFIS can be usedfor integrated protective scheme of transmission line.

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Thanks for your attention and listening

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A Novel Integrated Protective Scheme for Transmission Line Using ANFIS - Dr. Adel a. Elbaset