A Few Investigations on Fault Location Identification and ... · A Few Investigations on Fault...

A Few Investigations on Fault Location Identification and Classification

ADissertation Presentation

on

Swami Keshvanand Institute of Technology, Management and Gramothan, Jaipur.

20 May, 2017

6/12/2017

SupervisorDr. Akash SaxenaProfessor(Department of Electrical Engineering)

A Few Investigations on Fault Location Identification and Classification

ADissertation Presentation

on

Presented byPurva SharmaM.Tech. (Power System)(14ESKPS605)

Swami Keshvanand Institute of Technology, Management and Gramothan, Jaipur.

20 May, 2017

1

� Introduction to Fault Location Identification and Classification

� Research Objectives

� Identification of Fault Location

Contents

� Fault Classification Method

� Result and Discussion

� Conclusion & Future Scope

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Introduction to Fault Location Identification and Classification

Contents

2

Introduction to Fault

6/12/2017


3

� What is Fault ?

Fault or fault current is any abnormal electric current.

Eg: Short circuit is a fault in which current bypasses the normal load.

Open circuit fault occurs if a circuit is interrupted by some failure.

� Why Fault Occurs?


Occurrence of fault due to a variety of different factors:

� Lightning ionizing air

� Wires blowing together in the wind

� Animals or plants coming in contact with the wires

� Salt spray or pollution on insulators.

6/12/2017

Fault or fault current is any abnormal electric current.

Eg: Short circuit is a fault in which current bypasses the normal load.

Open circuit fault occurs if a circuit is interrupted by some failure.


Occurrence of fault due to a variety of different factors:

Animals or plants coming in contact with the wires

4

Classification of Faults

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Occurrence of Transmission Faults

Classification of Faults

� Fault Location Identification & Classification is needed ?

� Reliability

� Speed

� Selectivity

� Economy

� Simplicity

5

�Artificial Neural Network Based techniques model fault

as neural networks, fuzzy logic, etc.

� Flexible and can handle Complexity and Non-linearity

� Fault Tolerant (Noise)

� Computational cost and burden in ANN training

� Goodreliability

Fault Location Identification & Classification Approaches 1/4

� Goodreliability

6/12/2017

� Miladen Kezunovic et.al. (1996) EngineeringIntelligentSystem [2]

� B. Das et.al. (2005) IEEE Transactions onPowerDelivery [12]

� A. K. Pradhan et.al. (2001)Electric Power SystemsResearch, ELSEVIER [14]

detection & classification via non-parametric tools such

linearity


DENDRITES

INPUT LAYER

MIDDLE LAYER

OUTPUT LAYER

6

Intelligent

Power

Systems

IN

SYNAPSES

AXON OUT

LAYER

+

ACTIVATION

FUNCTION {f(ø)}

OUTPUT

Basic Architecture for ANN

�Signal Models Techniques based model for fault detection & classification by decomposing signals via

Wavelet Transform (WT), Hilbert Transform (HT) and Fourier Transform (FT).

� Signal decomposition quality

� Good time resolution

� Low frequency and small scaling factor


6/12/2017

�S.J. Huang (1999) IEEE Transactions on Power Delivery [29]

�A. Yadav (2015) Ain Shams Engineering Journal ELSEVIER [30]

�C.S. Chen (2006) IEEE Transactions on Power Delivery [41]

�A. Bernadic (2012) Electrical Power and Energy Systems, ELSEVIER [50]

based model for fault detection & classification by decomposing signals via

Wavelet Transform (WT), Hilbert Transform (HT) and Fourier Transform (FT).


7

Basic Methodology for Signal Processing

� Support Vector Machine based models a non-linear

higher dimensional space.

� Efficacy of the SVMis based on the choice of

technique.

� Map the data to high dimensional space foreasier


6/12/2017

�M.J.B. Reddy (2016) Engineering Science and Technology, an International Journal, ELSEVIER [63]

�B. Ravikumar (2008) IET Gener. Transm. Distrib. [64]

�V. Vapnik (1996) Advances in Neural Information Processing System [65]

linear transformation to draw the training input data to a

feature functions and performance of the optimization

easierclassification


8

Basic Methodology for SVM

�Hybrid Approach based interbred models inherit all short comings from their parent models.

� Strong classification capability

� Ability to select random samples and decomposition in sample data combination

� Fast response and reliable technique


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�E.A. Frimpong (2010) Journal of Scienceand

Technology [74]

�N. Gaur (2014)International Journal ofElectrical

and Electronics Engineering Research (IJEEER) [75

�A.A. Abohagar (2013)ARPN Journal ofEngineering

and Applied Sciences [79]

interbred models inherit all short comings from their parent models.

Ability to select random samples and decomposition in sample data combination


9

and

Electrical

75]

Engineering

Methodology for Hybrid Approach

� To identify fault location by different topologies ofneural

error indices.

� To investigate the effect of various mother wavelets

classification boundaries.

� To employ different statistical attributes of wavelettransforms

Norm, Mean and Standard Deviation values as inputfeatures

Research Objectives

(level 3, 5 and 7).

� To build supervised learning engines based on fourdifferent

Exact Fit Neural Network (RBEFNN), LayerRecurrent

Network (BPNN) and Elman Back Propagation NeuralNetwork

� To validate the approaches by four statistical testswhich

and Dunnett tests.

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neuralnetworks and compare their efficiency on the basisof

and decomposition levels of signals on determinationof

transformsof the voltage signals namely Maximum,Minimum,

featuresof supervised learning module on different MRAlevels

Research Objectives

different topologies of ANNs, which are namely RadialBasis

RecurrentNeural Network (LRNN), Back PropagationNeural

Network(EBPNN).

whichare namely Analysis of Variance (ANOVA), Tukey,Fisher

10

Part 1 : Fault Location Identification

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Part 1 : Fault Location Identification

11

� What is it?

Fault Detection, Isolation, and Recovery are a subfield of control engineering

which concerns itself with monitoring a system, identifying when a fault has

occurred, and pinpointing the type of fault and its location.

� Why it is needed?

Introduction to Fault Detection

� Why it is needed?

� Fault detection is used to provide a shorter time for technical crew to rectify fault and

thus help to save transformers and other equipment from damage and disasters.

� Improve efficiency of transmission system.

� Less maintenance cost.

6/12/2017

Fault Detection, Isolation, and Recovery are a subfield of control engineering

concerns itself with monitoring a system, identifying when a fault has

pinpointing the type of fault and its location.


Fault detection is used to provide a shorter time for technical crew to rectify fault and

thus help to save transformers and other equipment from damage and disasters.

Improve efficiency of transmission system.

12


VA

Bus1

B1 TLine_1

TimedBreakerLogicB1

B2

TimedBreakerLogic

TimedFaultLogic

RRL

I1C

I1B

I1A

6/12/2017Simulation Network

B2 Logic

Dial Position: 1=> A-g 2=> B-g 3=> C-g 4=> AB-g 5=> AC-g 6=> BC-g 7=> ABC-g 8=> AB 9=> AC 10=> No fault (0)11=>

I1C

iam11

(7)X1

X2

X3

dc1

Mag1 Mag2

dc2

F F T

F = 50.0 [Hz]

I1A

I1B

I1C

Main : Controls

10987654321

FT

2


TLine_2

FT

B2

500.0 [ohm]

0.1 [H]

ipm

inm

izm

Ia

13Simulation Network

icp

ibp

iap

1

1

1

icmibm11

(7)

(7)

(7)

(7)

(7)Ph1

Ph2

Ph3

Mag2 Mag3

dc2 dc3

F F T

F = 50.0 [Hz]izp

inp

ipp

izm

inm

ipm

icp

ibp

iap

icm

ibm

iam |A|

/_A

|B|

/_B

|C|

/_C

|P|

/_P

|N|

/_N

|Z|

/_Z

ABC

+-0

Type of Fault

Distance from theEnergy Management

Center (in Km.)

3 Phase 15

LG fault (A Phase) 30

LG fault (B Phase) 80

LG fault (C Phase) 115

Data Set used for Training


3 phase 135

LG fault (A Phase) 165

LG fault (B Phase) 180


LLG (AB Phase) 155

LLG (BC Phase) 65

LLG (CA Phase) 100

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Distance from theEnergy Management

Center (in Km.)

Sequence Currents Components

I1 (positive Sequence)

I2 (Negative Sequence)

I0(Zero Sequence)

14.60 3.714 0.76

1.8246 1.8174 1.7118

0.7706 0.7032 0.6483

0.5745 0.499 0.4441

Data Set used for Training

0.5745 0.499 0.4441

1.8304 0.4773 0.0853

0.451 0.4049 0.3095

0.4313 0.3362 0.2828

0.4132 0.3175 0.2639

0.9647 0.6948 0.249

2.1954 1.5815 0.5992

1.4655 1.0425 0.3856

14

Type of Fault Location of fault from B1 and B

3 PhaseMeasurement of currents at 77 km from B

Measurement of currents at 77 km from B

LG fault (A Phase)



LG fault (B Phase)



Testing Data Set

(B Phase) Measurement of currents at 52 km from B

LG fault (C Phase)



LLG (AB Phase)



LLG (BC Phase)



LLG (CA Phase)



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and B2 Sequence Currents ComponentsI1 (positive Sequence)

I2 (Negative Sequence)

I0 (Zero Sequence)

Measurement of currents at 77 km from B1 3.1880 0.8267 0.1492





Measurement of currents at 52 km from B 0.4872 0.4002 0.3458

Testing Data Set










15

S. No.

Fault Type

Location of fault from

Generating end

LocationFrom Brakers

Distance Calculated by Different Neural Networks

RBEFNN

1. 3-phase 77 kmFrom B1 76.7335

From B2 123.0307

2. A-phase 177 kmFrom B1 178.4299

From B2 24.5174

From B 53.1773

Comparison between Neural Network Topologies

3. B-phase 52 kmFrom B1 53.1773From B2 147.9923

4. C-phase 49 kmFrom B1 49.8893From B2 150.9469

5. AB-phase 83 kmFrom B1 81.6827From B2 120.9469

6. BC-phase 137 kmFrom B1 137.8599

From B2 62.9980

7. CA-phase 128 kmFrom B1 129.2253From B2 72.1787

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Distance Calculated by Different Neural Networks

RBEFNN EBPNN CFBPNN NARX RBFNNN

76.7335 75.5484 78.4792 77.3226 84.9386

123.0307 120.8602 131.3247 122.5118 123.0288

178.4299 170.3627 175.2730 175.5905 176.9454

24.5174 30.8266 24.9812 20.2962 22.9340

53.1773 53.1644 52.5587 51.9692 52.0618

Comparison between Neural Network Topologies

53.1773 53.1644 52.5587 51.9692 52.0618147.9923 150.7887 153.4113 152.4766 147.992549.8893 50.3498 49.6656 49.2728 48.6826150.9469 155.8266 156.8074 156.9338 150.944181.6827 71.4144 79.4834 81.7966 83.2008

120.9469 125.8266 126.8074 126.9338 116.5434

137.8599 130.9962 139.6517 137.4203 137.0409

62.9980 64.3340 59.7994 63.8709 62.9987

129.2253 121.6394 131.2064 127.2657 128.357

72.1787 73.2865 67.3297 72.4052 72.1793

16

S. No.

Fault Type

Comparison Between Neural Networks on the Basis of Fault Range RBEFNN EBPNN

Range towards B2 & B1

respectively(km)

Fault Occurrence in the range (km)


respectively(km)

Fault Occurrence in the range (km)

1 3-� 0.2665-0.03070.2972 1.4516- 2.1398 3.5914

2 A-G 1.4299-1.5174

Estimated Ranges of Fault Location

2 A-G 1.4299-1.51742.9473 6.6373-7.8266 14.4639

3 B-G 1.1773-0.00771.185 1.1644-2.7887 3.9531

4 C-G 0.8893-0.05310.9424 1.3498-4.8266 6.1764

5 AB-G 1.3173-3.94695.2642 11.585-8.8266 20.4122

6 BC-G 0.8599-0.0020.8619 6.0038-1.334 7.3378

7 CA-G 1.2253-0.17871.404 6.3606-1.2865 7.6471

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Comparison Between Neural Networks on the Basis of Fault Range CFBPNN NARX RBFNNN

Occurrence in the range (km)


respectively(km)

Fault Occurrence in

the range (km)

Range towards B2

& B1 respectively

(km)

Fault Occurrence in

the range (km)

Range towards B2

& B1 respectively

(km)

Fault Occurrence in the range

(km)

1.4792-8.3247 9.80390.3226-0.4882

0.81081.9386 -0.0288

1.9674

1.4095- 0.0546 -

Estimated Ranges of Fault Location

1.727-1.9812 3.70821.4095-2.7038

4.11330.0546 -0.066

0.1206

0.5587-5.4113 5.970.0308-4.4766

4.50740.0618 -0.0075

0.0693

0.6656-5.8074 6.4730.2728-5.9338

6.20660.3174 -0.0559

0.3733

3.5166-9.8074 13.3241.2034-9.9338

11.13720.2008 -0.4566

0.6574

2.6517-3.2006 5.85230.4203-0.8709

1.29120.0409 -0.0013

0.0422

3.2064-4.6703 7.87670.7343-0.4052

1.13950.357 -0.1793

0.5363

17

S. No. NetworksMSE

1. RBFNNN 0.0045

2. EBPNN 31.3161

Different Error Indices

3. CFBPNN 85.2761

4. NARX 12.3370

5. RBEFNN 0.0046

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Errors

MAE SSE SAE RMSE

0.0340 2.2797 17.1549 0.0673

1.7971 1.5783e04 905.7150 5.5961

Different Error Indices

3.4622 4.2974e04 1.7449e03 9.2345

2.0791 6.2178e03 1.0478e03 3.5124

0.0343 2.2948 17.2660 0.0675

18

� RBFNNN is the suitable topology to determine fault location in the transmission network as in

almost all cases.

� CFBPNN, NARX, EBPNN are not appreciated for fault location identification.

Result & Discussion

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0

1000

2000

3000

4000

5000

6000

7000

8000

9000

RBFNNN EBPNN CFBPNN

3.908

3345.4848

Ave

rage

Err

ors

Neural Networks

Comparison Between Different Neural Networks on the basis of Error Indices

RBFNNN is the suitable topology to determine fault location in the transmission network as in

CFBPNN, NARX, EBPNN are not appreciated for fault location identification.

Result & Discussion

8963.3745

19

CFBPNN NARX RBEFNN

214.3892 3.9334

Networks

Comparison Between Different Neural Networks on the basis of Error Indices

Part 2 : Fault Classification

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Part 2 : Fault ClassificationPart 2 : Fault Classification

20

Part 2 : Fault Classification

� Why classification needed?

� Conventional schemes: cannot adapt to changing operating conditions, affected by noise

& depend on DSP methods.

� Single –pole tripping / autoreclosure SPAR requires the knowledge of faulted phase (on

Introduction to Fault Classification

detecting SLG single-pole tripping is initiated on detecting arcing fault recloser is

initiated).

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Conventional schemes: cannot adapt to changing operating conditions, affected by noise

pole tripping / autoreclosure SPAR requires the knowledge of faulted phase (on


pole tripping is initiated on detecting arcing fault recloser is

21


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22

Approaches for Classification

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Approaches for Classification

23

Factors Affecting Wavelet Transform

Noise

• A random fluctuation

in a signal.

• White Gaussian Noise

MRA Levels

• Designing

WaveletTransform

justification

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• White Gaussian Noise

(awgn)

justification

algorithmof

• 7, 5 and

used

Factors Affecting Wavelet Transform

MRA Levels

method of

Transformand

justification for the

Detailed Coefficients

• Dimensional wavelet

analysis function

• 1-D and 2-D

24

justification for the

of WT

3 levels are

• 1-D and 2-D

Factors Affecting WT [

6/12/2017

Five Level of Decomposition Signal

Factors Affecting WT [MRA Level]

25

Seven Level of Decomposition Signal

Factors Affecting WT [Wavelet Case 1

Db4 0.713759 1.876209 3.163203 0.82243

Db8 0.420861 2.056379 2.476496 0.644928

Sym3 1.133737 2.816661 3.735113 0.857664

Sym6 0.605914 1.579659 3.019089 0.828803

Haar 2.169944 2.051328 5.304653 1.712539

Coif4 0.651939 1.415993 4.269543 0.592327

Wavelets Case Detailed Coefficients

Case 1

0.7386 1.210072 3.176839 3.971452 19.645710.360273 1.513456 3.152655 3.217598 29.18723

Sym3 0.81185 1.206105 5.399793 6.685645 23.75064Sym6 0.883718 1.959131 4.224428 5.861133 22.53988

Wavelet

Sym3Sym6

MRA Level -3

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Haar 1.118963 3.652849 3.345447 9.676313 22.54406Coif4 0.518051 0.99187 2.475639 3.963854 11.59798

Case 2

0.636146 1.872991 3.566518 4.297787 25.15730.371639 0.853491 3.392989 2.825107 5.757843

Sym3 0.715718 2.068232 2.86552 4.521744 40.61705Sym6 0.971372 2.775756 4.179655 4.737495 23.97184Haar 1.450414 3.051476 4.773264 8.762532 23.28545Coif4 0.604119 1.175953 3.690957 3.013191 21.56652

Case 3

0.940144 1.115556 4.908637 5.232029 20.592920.385798 2.003249 2.346147 4.197784 21.97504

Sym3 0.663598 1.84854 1.605121 5.44612 16.29644Sym6 0.558097 1.537246 3.811475 3.819625 19.26654Haar 2.599901 2.077082 8.064723 11.01759 24.8611Coif4 0.554357 1.65243 2.064446 5.37087 16.05069

Coif4

Sym3Sym6

Coif4

Sym3Sym6

Coif4

MRA Level-5

Factors Affecting WT [Detailed Coefficients]Case 2 Case 3

1.658096 3.730678 0.826305 0.862857 2.616252

1.29629 4.951034 0.97043 0.961173 3.793076

1.39294 4.476939 1.035632 1.727839 4.606788

2.41666 3.85898 0.71703 1.181927 2.531521

1.830974 7.378665 1.053443 3.147947 3.587445

0.948357 2.740918 0.724129 1.167673 5.879898

Wavelet Case Detailed CoefficientsDb4

Case 1

0.575033 1.548597 2.544602 2.795307 26.30602 27.09269.313052Db8 0.34784 0.987824 1.887022 2.666019 20.49774 17.6392213.86932

Sym3 0.656633 0.798083 2.585748 4.577607 10.50051 52.02595.371245Sym6 0.478826 0.819661 2.567838 3.068277 13.45675 39.42155.317498

26

Haar 1.246075 4.269707 4.068322 11.32063 39.66035 17.8086413.84013Coif4 0.362614 1.048251 1.796296 2.756604 19.50653 22.029666.18294Db4

Case 2

0.266428 1.283327 1.859164 4.518619 20.95951 29.418568.102571Db8 0.349089 0.577492 2.148454 2.620553 9.902047 27.657517.68515

Sym3 0.578189 1.630403 2.218373 4.975714 21.428 26.8682314.72246Sym6 0.532871 0.810271 2.772784 1.989814 9.695496 36.1322414.94963Haar 1.649545 1.817449 4.523627 12.61647 22.40845 33.5974115.92313Coif4 0.362614 1.048251 1.796296 2.756604 19.50653 22.029666.18294Db4

Case 3

0.284649 1.213986 1.792712 2.947267 12.61708 37.6124815.01072Db8 0.361765 1.109706 1.710555 2.339287 12.73655 30.702997.163929

Sym3 0.694373 1.821469 1.753059 5.897371 15.99661 39.760887.643863Sym6 0.414328 1.035031 2.162178 3.298734 11.93223 35.5354313.35099Haar 2.21945 2.381956 4.727247 9.948659 18.00192 39.7082410.67156Coif4 0.367493 0.937326 1.603592 3.208361 15.79962 26.653183.938067

MRA Level-7

Part-3 Design of Classification Engine

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3 Design of Classification Engine

27

Design of Classification Engine

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Classification Engine

Design of Classification Engine

1 0 0 0 0 0 0

0 0 1 0 0 0 0

0 0 0 1 0 0 0

0 0 0 0 1 0 0

28

0 0 0 0 0 1 0

0 0 0 0 0 0 1

0 1 0 0 0 0 0

Classification Engine

Performance of Classifier Engine

Case 1 • When Environment is noise free

Case 2 • When Environment is noisy (SNR=20db)

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Case 2 (SNR=20db)

Case 3• When Environment is more noisy

(SNR=30db

Performance of Classifier Engine

When Environment is noise free

When Environment is noisy (SNR=20db)

29

(SNR=20db)

When Environment is more noisy (SNR=30db)

Results of Classifier Engine for Network type → Back Propagation Neural Network[BPNN]

Wavelet type ↓MRA Level ↓

Errors and Efficiency

MSE MAE RMSE SSE SAE CON.

HAAR

7 0.0556 0.1111 0.2359 1362 2722 0.2871

5 0.0590 0.0981 0.2302 1299 2404 0.2669

3 0.0649 0.1307 .2547 1589 3186 03594

Daubechies Mother Wavelet

DB4

7 0.0452 0.0893 0.2125 1106 2187 0.2254

5 0.0500 0.0798 0.2235 1224 1955 0.2446

3 0.0420 0.0804 0.2048 1027 1970 0.2191

7 0.0456 0.0899 0.2135 1117 2202 0.2229

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Wavelet

Db8 5 0.0379 0.0770 0.1946 927.8957 1887 0.1926

3 0.0194 0.0379 0.1392 474.7967 927.6129 0.0991

Symlet Mother Wavelet

Sym3

7 0.0411 0.0821 0.2026 1005 2010 0.2020

5 0.0467 0.0936 0.2160 1143 2293 0.2431

3 0.0529 0.1045 0.2300 1296 2560 0.2851

Sym6

7 0.225 0.0437 0.1499 550.3252 1071 0.0977

5 0.0347 0.069 0.1864 851.3628 1690 0.1737

3 0.0454 0.0909 0.2131 1112 2226 0.2511

Coiflet Mother Wavelet

Coif4

7 0.100 0.0178 0.1000 245.1873 435.4167 0.0431

5 0.0250 0.0502 0.1580 611.5490 1231 0.1317

3 0.0391 0.0811 0.1978 958.5230 1986 0.2243

Results of Classifier Engine for Case-1 Radial Basis Exact Fit Neural Network[RBEFNN]


EFF [%] MSE MAE RMSE SSE SAE CON.EFF [%]

71.3 5.7164e-16 7.6122e-09 2.3909e-08 1.4005e-11 1.8650e-04 0 100

73.3 6.7317e-08 1.0773e-04 2.5946e-04 0.0016 2.6394 0100

64.1 0.0361 0.0929 0.1901 885.2115 2275 0.180681.9

77.5 1.1916e-13 1.0773e-04 2.5946e-04 0.0016 2.6394 0100

75.5 0.0020 0.0103 0.0445 48.4681 252.2826 0.004999.5

78.1 0.0219 0.0654 0.1480 536.9342 1601 0.091190.9

77.7 7.1615e-10 9.6314e-06 2.6746e-05 1.7546e-05 0.2360 0100

30

80.7 0.0037 0.0174 0.0608 90.4221 427.2403 0.010099.0

90.1 0.0167 0.0447 0.1292 408.9541 1094 0.082991.7

79.8 3.8666e-15 2.7024e-08 6.2182e-08 9.4731e-11 6.6208e-04 0 100

75.7 0.0022 0.0106 0.0467 53.3503 259.3596 0.006099.4

71.5 0.0396 0.0966 0.1990 970.1390 2366 0.202079.8

90.2 4.5257e-11 2.1696e-06 6.7273e-06 1.1088e-06 0.0532 0100

82.6 0.0049 0.0202 0.0700 120.0506 495.9160 0.016098.4

74.9 0.0381 0.0921 0.1951 932.9348 2257 0.200080.0

95.7 4.6891e-07 3.6747e-05 6.8477e-04 0.0115 0.9003 0100

86.8 0.0082 0.0294 0.0906 200.9955 720.5365 0.033496.7

77.6 0.0353 0.0837 0.1878 864.1601 2049 0.192980.7

ContdNetwork type → Layer Recurrent Neural Network [LRNN]



MSE MAE RMSE SSE SAE

HAAR

7 0.0577 0.1174 0.2403 1414 2877 0.2974

5 0.0551 0.1122 0.2347 1350 2747 0.2803

3 0.0641 0.1275 0.2531 1569 3123 0.3563


DB4

7 0.0469 0.0927 0.2166 1149 2271 0.2209

5 0.0499 0.0789 0.2234 1222 1933 0.2766

3 0.0341 0.0663 0.1845 834.2541 1625 0.1683

7 0.0449 0.0887 0.2119 1100 2172 0.2160

6/12/2017

Wavelet

Db8 5 0.0348 0.0694 0.1866 852.8114 1701 0.1740

3 0.0192 0.0387 0.1386 470.5908 948.2243 0.0997


Sym3

7 0.0431 0.0859 0.2077 1057 2105 0.2126

5 0.0456 0.0929 0.2156 1139 2275 0.2386

3 0.0493 0.0953 0.22119 1206 2335 0.2683

Sym6

7 0.0244 0.0496 0.1562 597.8954 1214 0.1083

5 0.0340 0.0679 0.1845 834.0320 1664 0.1711

3 0.0486 0.0983 0.2206 1191 2407 0.2717


Coif4

7 0.0101 0.0187 0.1003 246.2914 457.3996 0.0420

5 0.0254 0.510 0.1594 622.472 1250 0.1334

3 0.0591 0.0983 0.2431 1447 2408 0.3634

Contd… Layer Recurrent Neural Network [LRNN] Elman Back Propagation Neural Network [EBPNN]


CON. EFF [%] MSE MAE RMSE SSE SAE CON.EFF [%]

0.2974 70.3 0.0542 0.1062 0.2329 1328 2602 0.2763 72.4

0.2803 72.0 0.0521 0.1012 0.2282 1275 2479 0.2580 74.2

0.3563 64.4 0.0649 0.1313 0.2547 1589 3217 0.3563 64.4

0.2209 77.9 0.0481 0.0959 0.2193 1178 2348 0.2391 76.1

0.2766 72.3 0.0396 0.0779 0.1990 970.0427 1909 0.2043 79.6

0.1683 83.2 0.0428 0.0824 0.2070 1049 2018 0.2203 78.0

0.2160 78.4 0.0450 0.0903 0.2122 1103 2211 0.2200 78.0

31

0.1740 82.6 0.0349 0.0688 0.1868 855.1099 1684 0.1694 83.1

0.0997 90.0 0.0191 0.0376 0.1381 467.5673 921.8981 0.0989 90.1

0.2126 80.4 0.0395 0.0759 0.1987 967.7836 1860 0.1960 80.4

0.2386 76.1 0.0457 0.0886 0.2139 1120 2170 0.2349 76.5

0.2683 73.2 0.0538 0.1074 0.2319 1317 2631 0.2877 71.2

0.1083 89.2 0.0219 0.0414 0.1481 537.1563 1013 0.0980 90.2

0.1711 82.9 0.0345 0.0665 0.1856 844.4013 1628 0.1723 82.8

0.2717 72.8 0.0483 0.0970 0.2197 1183 2376 0.2609 73.9

0.0420 95.8 0.0104 0.0199 0.1020 255.0814 488.6255 0.0440 95.6

0.1334 86.7 0.0247 0.0495 0.1570 604.1191 1213 0.1274 82.3

0.3634 63.7 0.0393 0.0814 0.1982 962.0993 1994 0.2266 77.3

Contd

0

20

40

60

80

100

haar db4 db8 sym3 sym6 coif4

Eff

icie

ncy

Mother wavelets

6/12/2017

BPNN RBEFNN LRNN EBPNN

Comparative Analysis of Classification Efficiency

86889092949698

100

MRA-7 MRA

100DB8

Eff

icie

ncy

MRA Levels

Comparison between MRA levels

Contd…

90

91

92

93

94

95

96

97

HAAR DB4 DB8 SYM3 SYM6 COIF4

93.966

96.8 96.9

93.066 92.892.466

RBEFNN

Eff

icie

ncy

32

Mother Wavelet type

MRA-5 MRA-3

99

91.7

DB8

MRA Levels

Comparison between Mother Wavelets

Comparison between MRA levels





HAAR

7 0.0665 0.1329 0.2578 1628 3255 0.3786

5 0.0587 0.1146 0.2423 1438 2808 0.3226

3 0.0653 0.1325 0.2555 1599 3245 0.3729


DB4

7 0.0584 0.1152 0.2417 1431 2822 0.3246

5 0.0525 0.1067 0.2291 1286 2615 0.2994

3 0.0618 0.1254 0.2486 1513 3072 0.3597

7 0.0636 0.1306 0.2522 1558 3199 0.3514

6/12/2017

Db8 5 0.0480 0.0969 0.2191 1175 2373 0.2874

3 0.0592 0.1205 0.2432 1449 2951 0.3537


Sym3

7 0.0536 0.1077 0.2316 1314 2638 0.3063

5 0.0560 0.1154 0.2365 1370 2827 0.3243

3 0.0634 0.1271 0.2518 1553 3113 0.3831

Sym6

7 0.0396 0.0793 0.1989 969.2099 1943 0.2303

5 0.0460 0.0928 0.2146 1128 2272 0.2720

3 0.0598 0.1232 0.2445 1464 3018 0.3634


Coif4

7 0.0289 0.0578 0.1700 708.2233 1415 0.1763

5 0.0453 0.0935 0.2129 1110 2290 0.2620

3 0.0547 0.1124 0.2338 1339 2752 0.3289

Results of Classifier Engine for Case-2 Radial Basis Exact Fit Neural Network[RBEFNN]



62.1 1.1175e-15 1.6928e-08 3.3429e-08 2.737e-11 4.1474e-04 0 100

67.7 3.8509e-05 6.7329e-04 0.0062 0.9435 16.4955 0.006099.4

62.7 0.0505 0.1182 0.2248 1237 2896 0.270972.9

67.5 5.2700e-12 8.5912e-07 2.2956e-06 1.2911e-07 0.0210 0100

70.1 0.0031 0.016 0.0553 74.9788 409.6397 0.007799.2

64.0 0.0498 0.1143 0.2231 1219 2799 0.267773.2

64.9 4.3898e-08 8.8707e-05 2.0952e-04 0.0011 2.1733 0100

33

71.3 0.0147 0.0484 0.1211 359.3125 1185 0.063493.7

64.6 0.0522 0.1165 0.2285 1279 2855 0.305469.5

69.4 4.6147e-10 7.7178e-06 2.1482e-05 1.1306e-05 0.1891 0100

67.6 0.0097 0.0346 0.0983 236.9719 847.1716 0.042095.8

61.7 0.0558 0.1253 0.2362 1367 3069 0.324667.5

77.0 3.9327e-04 0.0013 0.0198 9.6351 31.8926 0.001499.9

72.8 0.0152 0.0494 0.1232 371.6664 1209 0.063193.7

63.7 0.0533 0.1181 0.2309 1305 2893 0.313468.7

82.4 0.0023 0.0080 0.0475 55.3184 195.2733 0.008999.1

73.8 0.0191 0.0595 0.1380 466.8156 1456 0.138091.6

67.1 0.0509 0.1103 0.2257 1248 2702 0.304669.5





HAAR

7 0.0652 0.1297 0.2554 1597 3177 0.3623

5 0.0604 0.1186 0.2459 1481 2905 0.3303

3 0.0730 0.1245 0.2702 1788 3050 0.4017


DB4

7 0.0584 0.1149 0.2416 1429 2815 0.3220

5 0.0636 0.1078 0.2523 1559 2641 0.3389

3 0.0631 0.1299 0.2513 1546 3182 0.3694

7 0.0592 0.1172 0.2432 1449 2870 0.3220

6/12/2017

Db8 5 0.0465 0.0932 0.2156 1139 2283 0.2769

3 0.0592 0.1206 0.2433 1450 2954 0.3529


Sym3

7 0.0555 0.1123 0.2357 1360 2750 0.3106

5 0.0545 0.1090 0.2333 1334 2670 0.3229

3 0.0635 0.1263 0.2519 1554 3095 0.3806

Sym6

7 0.0396 0.0800 0.1990 970.4234 1960 0.2291

5 0.0459 0.0919 0.2141 1123 2251 0.2729

3 0.0591 0.1196 0.2431 1447 2930 0.3549


Coif4

7 0.0284 0.0578 0.1684 695.0077 1416 0.1751

5 0.0442 0.0906 0.2103 1083 2219 0.2626

3 0.0750 0.1332 0.2740 1838 3262 0.4703

Contd….Layer Recurrent Neural Network [LRNN] Elman Back Propagation Neural Network [EBPNN]



0.3623 63.8 0.0661 0.1325 0.2570 1618 3247 0.3691 63.1

0.3303 67.0 0.0583 0.1154 0.2414 1427 2827 0.3251 67.5

0.4017 59.8 0.0670 0.1342 0.2587 1640 3288 0.3906 60.9

0.3220 67.8 0.0603 0.1209 0.2455 1476 2961 0.3311 66.9

0.3389 66.1 0.0520 0.1042 0.2280 1274 2553 0.2954 70.5

0.3694 63.1 0.0648 0.1351 0.2546 1588 3310 0.3814 61.9

0.3220 67.8 0.0602 0.1173 0.2454 1475 2874 0.3309 66.9

34

0.2769 72.3 0.0482 0.0976 0.2195 1180 2392 0.2874 71.3

0.3529 64.7 0.0579 0.1185 0.2406 1418 2902 0.3406 65.9

0.3106 68.9 0.0644 0.1106 0.2537 1576 2710 0.3191 68.1

0.3229 67.7 0.0539 0.1077 0.2321 1320 2637 0.3183 68.2

0.3806 61.9 0.0644 0.1308 0.2538 1578 3204 0.3823 61.8

0.2291 77.1 0.0412 0.0832 0.2029 1008 2039 0.2380 76.2

0.2729 72.7 0.0461 0.0932 0.2148 1130 2284 0.2729 72.7

0.3549 64.5 0.0585 0.1177 0.2418 1433 2884 0.3463 65.4

0.1751 82.5 0.0285 0.0573 0.1690 699.3419 1404 0.1731 82.7

0.2626 73.7 0.0436 0.0882 0.2088 1067 2161 0.2603 74.0

0.4703 53.0 0.0539 0.1084 0.2321 1320 2655 0.3229 67.7

Contd

0

20

40

60

80

100



Eff

icie

ncy

Mother Wavelet

6/12/2017


0

50

100

MRA-7 MRA-5

100

Eff

icie

ncy

MRA Levels

HAARComparative Analysis of Classification Efficiency

Comparison between MRA Levels

Contd….

84

85

86

87

88

89

90

91


90.96 90.8

87.733 87.766 87.43386.733

RBEFNN

Mother wavelet

Eff

icie

ncy

35

Mother wavelet

5 MRA-3

99.4

72.9

MRA Levels

HAARComparisons between

Mother Wavelets






HAAR

7 0.0668 0.1358 0.2585 1637 3326 0.3377

5 0.0598 0.1185 0.2444 1463 2903 0.3377

3 0.0690 0.1389 0.2627 1690 3403 0.4166


DB4

7 0.0611 0.1215 0.2472 1497 2975 0.3394

5 0.0496 0.0991 0.2228 1215 2427 0.2871

3 0.0624 0.1261 0.2498 1529 3090 0.3814

7 0.0578 0.1157 0.2405 1417 2834 0.3217

6/12/2017

Wavelet

Db8 5 0.0264 0.0536 0.1626 647.8909 1312 0.1374

3 0.0583 0.1189 0.2415 1428 2912 0.3369


Sym3

7 0.0555 0.1116 0.2355 1359 2734 0.3129

5 0.0553 0.1097 0.2353 1356 2686 0.3206

3 0.0638 0.1289 0.2526 1563 3158 0.3951

Sym6

7 0.0370 0.0742 0.1924 907.1304 1818 0.2169

5 0.0461 0.0937 0.2148 1130 2295 0.2663

3 0.0595 0.1204 0.2439 1457 2949 0.3620


Coif4

7 0.0274 0.0553 0.1655 670.8530 1354 0.1694

5 0.0443 0.0909 0.2105 1086 2225 0.2663

3 0.0567 0.1197 0.2382 1389 2933 0.3440

Results of Classifier Engine for Case-3Radial Basis Exact Fit Neural Network[RBEFNN]



62.2 7.0582e-10 8.2515e-07 2.6567e-06 1.7293e-07 0.0202 0100

66.2 8.3763e-04 0.0040 0.0289 20.5220 96.8776 0.003499.7

58.3 0.0525 0.1216 0.2292 1287 2978 0.296370.4

66.1 1.4583e-09 1.1027e-05 3.8188e-05 3.5728e-05 0.2702 0100

71.3 0.0070 0.0299 0.0837 171.8107 733.3487 0.023497.7

61.9 0.0518 0.1177 0.2276 1268 2884 0.301469.9

67.8 1.7015e-04 0.0011 0.0130 4.1688 25.80938.5714e-04

99.9

36

e-04

86.3 0.0043 0.0180 0.0655 104.9718 441.1936 0.014698.5

66.3 0.0521 0.1172 0.2282 1276 2872 0.298370.2

68.7 3.4415e-10 5.7301e-06 1.8551e-05 8.4316e-06 0.1404 0100

67.9 0.0074 0.0306 0.0806 181.1989 750.4740 0.023197.7

60.5 0.0564 0.1258 0.2375 1382 3082 0.330367.0

78.3 4.9564e-08 4.3207e-05 2.2263e-04 0.0012 1.0586 0100

73.4 0.0134 0.0479 0.1160 329.5050 1174 0.053194.7

63.8 0.0543 0.1186 0.2331 1331 2904 0.325767.4

83.1 0.0013 0.0048 0.0363 32.3640 117.2492 0.006699.3

73.4 0.0173 0.0565 0.1317 424.9046 1384 0.074392.6

65.6 0.0524 0.1140 0.2289 1283 2793 0.318068.2





HAAR

7 0.0656 0.1303 0.2562 1607 3192 0.3817

5 0.0675 0.1320 0.2598 1653 3234 0.3800

3 0.0690 0.1408 0.2627 1691 3449 0.4183


DB4

7 0.0662 0.1115 0.2573 1621 2732 0.3209

5 0.0498 0.1005 0.2232 1220 2461 0.2874

3 0.0618 0.1259 0.2487 1514 3085 0.3634

7 0.0582 0.1157 0.2412 1425 2834 0.3223

6/12/2017

Wavelet

Db8 5 0.0249 0.0492 0.1577 609.0015 1206 0.1303

3 0.0590 0.1205 0.2428 1444 2952 0.3480


Sym3

7 0.0525 0.1046 0.2291 1286 2562 0.2963

5 0.0550 0.1113 0.2346 1348 2726 0.3146

3 0.0624 0.1255 0.2499 1529 3075 0.3803

Sym6

7 0.0369 0.0736 0.1922 905.2173 1802 0.2146

5 0.0657 0.1112 0.2564 1610 2723 0.4109

3 0.0594 0.1211 0.2438 1456 2967 0.3611


Coif4

7 0.0275 0.0566 0.1659 674.4066 1387 0.1711

5 0.0449 0.0912 0.2119 1100 2235 0.2717

3 0.0566 0.1181 0.2378 1385 2893 0.3420

Contd….Layer Recurrent Neural Network [LRNN] Elman Back Propagation Neural Network [EBPNN]



0.3817 61.8 0.0630 0.1244 0.2510 1543 3047 0.3580 64.2

0.3800 62.0 0.0583 0.1152 0.2415 1429 2821 0.3320 66.8

0.4183 58.2 0.0681 0.1388 0.2610 1669 3400 0.4106 58.9

0.3209 67.9 0.0599 0.1219 0.2447 1466 2987 0.3300 67.0

0.2874 71.3 0.0510 0.1057 0.2259 1250 2590 0.2983 70.2

0.3634 63.7 0.0600 0.1186 0.2450 1470 2906 0.3583 64.2

0.3223 67.8 0.0602 0.1211 0.2453 1474 2966 0.3369 66.3

37

0.1303 87.0 0.0260 0.0510 0.1612 636.9590 1250 0.1311 86.9

0.3480 65.2 0.0569 0.1138 0.2385 1393 2788 0.3343 66.6

0.2963 70.4 0.0539 0.1105 0.2322 1320 2707 0.3091 69.1

0.3146 68.5 0.0549 0.1099 0.2342 1344 2693 0.3174 68.3

0.3803 62.0 0.0621 0.1227 0.2491 1520 3005 0.3746 62.5

0.2146 78.5 0.0368 0.0757 0.1919 902.3676 1854 0.2169 78.3

0.4109 58.9 0.0455 0.0920 0.2133 1115 2253 0.2657 73.4

0.3611 63.9 0.0588 0.1190 0.2424 1439 2914 0.3603 64.0

0.1711 82.9 0.0275 0.056 0.1659 674.4338 1377 0.1689 83.1

0.2717 72.8 0.0444 0.0905 0.2108 1088 2218 0.2674 73.3

0.3420 65.8 0.0549 0.1100 0.2343 1345 2693 0.3397 66.0

Contd

0

50

100


BPNN RBEFNN LRNN EBPNNMother wavelets

6/12/2017

0

20

40

60

80

100

MRA-7 MRA-

100

Eff

icie

ncy

MRA levels

Comparative Analysis of Classification Efficiency


Contd….

85

86

87

88

89

90

91


90.033

89.2 89.533

88.233

87.36686.7

RBEFNN

Mother wavelet type

Eff

icie

ncy

38

-5 MRA-3

99.7

70.4

HAAR

MRA levels

Comparisons between Mother Wavelets


Performance of Classifier Engine by Wavelet Cases

RBEFNN

Case 1

RBEFNN

1 2 3 4 5 6

1

2

3

4

5

6

7

50014.3%

00.0%

00.0%

00.0%

00.0%

00.0%

00.0%

100%0.0%

00.0%

50014.3%

00.0%

00.0%

00.0%

00.0%

00.0%

100%0.0%

00.0%

00.0%

50014.3%

00.0%

00.0%

00.0%

00.0%

100%0.0%

00.0%

00.0%

00.0%

50014.3%

00.0%

00.0%

00.0%

100%0.0%

00.0%

00.0%

00.0%

00.0%

50014.3%

00.0%

00.0%

100%0.0%

00.0%

00.0%

00.0%

00.0%

00.0%

50014.3%

00.0%

100%0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

50014.3%

100%0.0%

Target Class

Output C

lass

Confusion Matrix

1

2

3

50014.3%

00.0%

00.0%

00.0%

50014.3%

00.0%

00.0%

00.0%

50014.3%

00.0%

00.0%

00.0%

00.0%

00.0%

00.0%

00.0%

00.0%

00.0%

Confusion Matrix

6/12/2017

HAAR Wavelet Case 2

RBEFNN

Case 3

1 2 3 4 5 6

4

5

6

7

0.0%

00.0%

00.0%

00.0%

00.0%

100%0.0%

0.0%

00.0%

00.0%

00.0%

00.0%

100%0.0%

14.3%

00.0%

00.0%

00.0%

00.0%

100%0.0%

0.0%

50014.3%

00.0%

00.0%

00.0%

100%0.0%

0.0%

00.0%

50014.3%

00.0%

00.0%

100%0.0%

0.0%

00.0%

00.0%

50014.3%

00.0%

100%0.0%

14.3%

100%

Target Class

Output C

lass

1 2 3 4 5 6

1

2

3

4

5

6

7

50014.3%

00.0%

00.0%

00.0%

00.0%

00.0%

00.0%

100%0.0%

00.0%

50014.3%

00.0%

00.0%

00.0%

00.0%

00.0%

100%0.0%

00.0%

00.0%

50014.3%

00.0%

00.0%

00.0%

00.0%

100%0.0%

00.0%

00.0%

00.0%

50014.3%

00.0%

00.0%

00.0%

100%0.0%

00.0%

00.0%

00.0%

00.0%

50014.3%

00.0%

00.0%

100%0.0%

00.0%

00.0%

00.0%

00.0%

00.0%

50014.3%

00.0%

100%0.0%

14.3%

Target Class

Output C

lass

Confusion Matrix

Performance of Classifier Engine by Confusion MatrixNeural Topologies

BPNN

LRNN

7

00.0%

00.0%

00.0%

00.0%

00.0%

00.0%

50014.3%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

1 2 3 4 5 6 7

1

2

3

4

5

6

7

2888.2%

00.0%

10.0%

50.1%

00.0%

1805.1%

260.7%

57.6%42.4%

00.0%

50014.3%

00.0%

00.0%

00.0%

00.0%

00.0%

100%0.0%

00.0%

20.1%

2316.6%

451.3%

2065.9%

00.0%

160.5%

46.2%53.8%

00.0%

00.0%

762.2%

2657.6%

210.6%

20.1%

1363.9%

53.0%47.0%

00.0%

40.1%

1283.7%

130.4%

3429.8%

00.0%

130.4%

68.4%31.6%

972.8%

00.0%

00.0%

200.6%

10.0%

3459.9%

371.1%

69.0%31.0%

20.1%

00.0%

401.1%

1564.5%

190.5%

120.3%

2717.7%

54.2%45.8%

74.4%25.6%

98.8%1.2%

48.5%51.5%

52.6%47.4%

58.1%41.9%

64.0%36.0%

54.3%45.7%

64.1%35.9%

Target Class

Output C

lass

Confusion Matrix

00.0%

00.0%

00.0%

100%0.0%

100%0.0%

100%0.0%

1

2

3

2637.5%

00.0%

441.3%

00.0%

50014.3%

00.0%

30.1%

00.0%

2627.5%

1333.8%

00.0%

732.1%

40.1%

10.0%

1394.0%

210.6%

00.0%

00.0%

2436.9%

00.0%

461.3%

39.4%60.6%

99.8%0.2%

46.5%53.5%

Confusion Matrix

39

LRNN

7

0.0%

00.0%

00.0%

00.0%

50014.3%

100%0.0%

0.0%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

1 2 3 4 5 6 7

4

5

6

7

1.3%

1474.2%

280.8%

180.5%

00.0%

52.6%47.4%

0.0%

00.0%

00.0%

00.0%

00.0%

100%0.0%

7.5%

561.6%

1795.1%

00.0%

00.0%

52.4%47.6%

2.1%

2717.7%

210.6%

20.1%

00.0%

54.2%45.8%

4.0%

190.5%

3379.6%

00.0%

00.0%

67.4%32.6%

0.0%

180.5%

00.0%

46113.2%

00.0%

92.2%7.8%

1.3%

1704.9%

220.6%

190.5%

00.0%

0.0%100%

53.5%

39.8%60.2%

57.4%42.6%

92.2%7.8%

NaN%NaN%

59.8%40.2%

Target Class

Output C

lass

7

00.0%

00.0%

00.0%

00.0%

00.0%

00.0%

50014.3%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

1 2 3 4 5 6 7

1

2

3

4

5

6

7

1815.2%

10.0%

401.1%

1223.5%

220.6%

160.5%

1183.4%

36.2%63.8%

10.0%

49814.2%

00.0%

00.0%

10.0%

00.0%

00.0%

99.6%0.4%

20.1%

10.0%

2156.1%

551.6%

2186.2%

00.0%

90.3%

43.0%57.0%

1103.1%

00.0%

671.9%

2296.5%

230.7%

30.1%

681.9%

45.8%54.2%

00.0%

30.1%

1313.7%

90.3%

3459.9%

00.0%

120.3%

69.0%31.0%

210.6%

10.0%

50.1%

130.4%

00.0%

45012.9%

100.3%

90.0%10.0%

1815.2%

10.0%

401.1%

1233.5%

210.6%

160.5%

1183.4%

23.6%76.4%

36.5%63.5%

98.6%1.4%

43.2%56.8%

41.6%58.4%

54.8%45.2%

92.8%7.2%

35.2%64.8%

58.2%41.8%

Target Class

Output C

lass

Confusion Matrix

Contd RBEFNN

Case 1

RBEFNN

1 2 3 4 5 6

1

2

3

4

5

6

7

50014.3%

00.0%

00.0%

00.0%

00.0%

00.0%

00.0%

100%0.0%

00.0%

50014.3%

00.0%

00.0%

00.0%

00.0%

00.0%

100%0.0%

00.0%

00.0%

50014.3%

00.0%

00.0%

00.0%

00.0%

100%0.0%

00.0%

00.0%

00.0%

50014.3%

00.0%

00.0%

00.0%

100%0.0%

00.0%

00.0%

00.0%

00.0%

50014.3%

00.0%

00.0%

100%0.0%

00.0%

00.0%

00.0%

00.0%

00.0%

50014.3%

00.0%

100%0.0%

Target Class

Output C

lass

Confusion Matrix

1

2

3

50014.3%

00.0%

00.0%

00.0%

50014.3%

00.0%

00.0%

00.0%

50014.3%

00.0%

00.0%

00.0%

00.0%

00.0%

00.0%

00.0%

00.0%

00.0%

0.0%

0.0%

0.0%

Confusion Matrix

6/12/2017

DB8 Wavelet Case 2

RBEFNN

Case 3

1 2 3 4 5 6

3

4

5

6

7

0.0%

00.0%

00.0%

00.0%

00.0%

100%0.0%

0.0%

00.0%

00.0%

00.0%

00.0%

100%0.0%

14.3%

00.0%

00.0%

00.0%

00.0%

100%0.0%

0.0%

50014.3%

00.0%

00.0%

00.0%

100%0.0%

0.0%

00.0%

50014.3%

00.0%

00.0%

100%0.0%

0.0%

00.0%

00.0%

50014.3%

00.0%

100%0.0%

0.0%

0.0%

0.0%

0.0%

14.3%

100%0.0%

Target Class

Output C

lass

1 2 3 4 5 6

1

2

3

4

5

6

7

49814.2%

00.0%

00.0%

00.0%

00.0%

00.0%

20.1%

99.6%0.4%

00.0%

50014.3%

00.0%

00.0%

00.0%

00.0%

00.0%

100%0.0%

00.0%

00.0%

50014.3%

00.0%

00.0%

00.0%

00.0%

100%0.0%

00.0%

00.0%

00.0%

50014.3%

00.0%

00.0%

00.0%

100%0.0%

00.0%

00.0%

00.0%

00.0%

50014.3%

00.0%

00.0%

100%0.0%

00.0%

00.0%

00.0%

00.0%

00.0%

50014.3%

00.0%

100%0.0%

14.3%

99.8%

Target Class

Output C

lass

Confusion Matrix

Contd….BPNN

BPNN

7

00.0%

00.0%

00.0%

00.0%

00.0%

00.0%

50014.3%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

1 2 3 4 5 6 7

1

2

3

4

5

6

7

43112.3%

00.0%

00.0%

90.3%

00.0%

451.3%

150.4%

86.2%13.8%

00.0%

49814.2%

20.1%

00.0%

00.0%

00.0%

00.0%

99.6%0.4%

00.0%

00.0%

38210.9%

932.7%

220.6%

20.1%

10.0%

76.4%23.6%

00.0%

00.0%

1233.5%

3269.3%

270.8%

130.4%

110.3%

65.2%34.8%

30.1%

00.0%

391.1%

230.7%

35210.1%

110.3%

722.1%

70.4%29.6%

220.6%

00.0%

10.0%

190.5%

40.1%

43012.3%

240.7%

86.0%14.0%

170.5%

00.0%

80.2%

351.0%

1022.9%

371.1%

3018.6%

60.2%39.8%

91.1%8.9%

100%0.0%

68.8%31.2%

64.6%35.4%

69.4%30.6%

79.9%20.1%

71.0%29.0%

77.7%22.3%

Target Class

Output C

lass

Confusion Matrix

00.0%

00.0%

00.0%

100%0.0%

100%0.0%

100%0.0%

1

2

3

1253.6%

00.0%

00.0%

00.0%

50014.3%

00.0%

00.0%

00.0%

3038.7%

170.5%

00.0%

20.1%

00.0%

00.0%

2196.3%

180.5%

00.0%

00.0%

1243.5%

00.0%

00.0%

44.0%56.0%

100%0.0%

57.8%42.2%

Confusion Matrix

40

LRNN

7

0.0%

00.0%

00.0%

00.0%

50014.3%

100%0.0%

0.0%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

1 2 3 4 5 6 7

3

4

5

6

7

0.0%

1484.2%

90.3%

140.4%

2045.8%

25.0%75.0%

0.0%

00.0%

00.0%

00.0%

00.0%

100%0.0%

8.7%

50.1%

1925.5%

00.0%

00.0%

60.6%39.4%

0.1%

38911.1%

80.2%

20.1%

822.3%

77.8%22.2%

6.3%

120.3%

2667.6%

10.0%

20.1%

53.2%46.8%

0.0%

00.0%

00.0%

47213.5%

100.3%

94.4%5.6%

0.0%

1474.2%

80.2%

140.4%

2075.9%

41.4%58.6%

42.2%

55.5%44.5%

55.1%44.9%

93.8%6.2%

41.0%59.0%

64.6%35.4%

Target Class

Output C

lass

7

10.0%

00.0%

00.0%

00.0%

00.0%

00.0%

49914.3%

99.8%0.2%

99.8%0.2%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

100%0.0%

99.6%0.4%

99.9%0.1%

1 2 3 4 5 6 7

1

2

3

4

5

6

7

501.4%

00.0%

00.0%

1484.2%

20.1%

210.6%

2798.0%

10.0%90.0%

00.0%

50014.3%

00.0%

00.0%

00.0%

00.0%

00.0%

100%0.0%

00.0%

00.0%

3008.6%

60.2%

1945.5%

00.0%

00.0%

60.0%40.0%

260.7%

00.0%

40.1%

39911.4%

60.2%

20.1%

631.8%

79.8%20.2%

00.0%

00.0%

2055.9%

170.5%

2767.9%

10.0%

10.0%

55.2%44.8%

00.0%

00.0%

00.0%

00.0%

00.0%

47413.5%

260.7%

94.8%5.2%

461.3%

00.0%

00.0%

1494.3%

20.1%

200.6%

2838.1%

56.6%43.4%

41.0%59.0%

100%0.0%

58.9%41.1%

55.5%44.5%

57.5%42.5%

91.5%8.5%

43.4%56.6%

65.2%34.8%

Target Class

Output C

lass

Confusion Matrix

Result & Discussion

� Performance of the Classifier: It is observed

classification under noisy environmenthowever,

system is noise free.

� Performance of the Neural Topologies: From

6/12/2017

the most Favorable topologies under allthree

poor results.

� MRA Levels : It is observed that 7th decomposition

all three cases

Result & Discussion

observedthat Haar mother Wavelet is suitable for fault

however,DB 8 wavelet gives best result when the

From the result, it can be observed that RBEFNN is

41

threecases for classification however, BPNN gives

decompositionMRA levels is best for classification under

Part 4: Comparison with Statistical Tests

6/12/2017

Part 4: Comparison with Statistical Tests

42

Statistical Analysis

• Statistical technique that assess potential difference between 2 or more scale level dependent variable.

• One way analysis of variance.

ANOVA Test

FISHER Test

6/12/2017

• Statistical significance test used in the analysis of contingency tables.• Used to examine significance of the contingency between the two kind of

classification.

• Single step multiple comparison process.• Used on raw data with an ANOVA to find means that are significantly

different from each other.

TUKEY Test

Statistical Analysis

Statistical technique that assess potential difference between 2 or more scale level dependent variable.

43

Statistical significance test used in the analysis of contingency tables.Used to examine significance of the contingency between the two kind of

Single step multiple comparison process.Used on raw data with an ANOVA to find means that are significantly

Results of Statistical Test [

Wavelet No. Mean

Coif 4 12 86.57Db4 12 82.38

6/12/2017

Db4 12 82.38Db8 11 85.58Haar 11 73.48Sym3 12 80.33Sym6 12 84.33

Results of Statistical Test [ANOVA]

Standard Deviation

95% Confidence Interval for MeanLower Bound

Upper Bound

10.77 81.15 92.009.31 76.95 87.81

44

9.31 76.95 87.816.98 79.91 91.2510.23 67.81 79.159.62 74.90 85.768.99 79.40 90.25

Results of Statistical Test [Wavelet N

Coif4 12Db8 11

Sym6 12Db4 12

Sym3 12Haar 11

Difference of levels

Difference of Means

Db4 - Coif4 -4.19Db8 - Coif4 -0.99

Haar - Coif4 -13.09Sym3 - Coif4 -6.24Sym6 - Coif4 -1.75

Db8 - Db4 3.20

Fisher Pair wise Comparisons

Fisher Individual Tests for Differences of Means

6/12/2017

Db8 - Db4 3.20Haar - Db4 -8.90Sym3 - Db4 -2.05Sym6 - Db4 2.44Haar - Db8 -12.10Sym3 - Db8 -5.25Sym6 - Db8 -0.76Sym3 - Haar 6.85Sym6 - Haar 11.34Sym6 - Sym3 4.49Difference

of levelsDifference of Means

Coif4 - Db8 0.99Db4 - Coif4 -4.19Db8 - Coif4 -0.99Haar - Coif4 -13.09Sym3 - Coif4 -6.24Sym6 - Coif4 -1.75

HSU Multiple Comparisons with the Best (MCB)

Results of Statistical Test [FISHER]Mean Grouping Feedback86.57 A

1. Means that do not share a letter aresignificantly different.

2. Grouping Information Using the FisherLSD Method and 95% Confidence

85.58 A84.83 A82.38 A80.33 AB73.48 B

SE of Difference95% Cl

Adjusted T-Value

Adjusted P-Value

Feedback

3.84 (-15.47, 7.09) -1.09 0.279

• Simultaneous

3.93 ( -8.84, 6.86) -0.25 0.8013.93 (-20.94, -5.24) -3.33 0.0013.84 (-13.92, 1.44) -1.62 0.1093.84 (-9.43, 5.93) -0.46 0.6503.93 (-4.65, 11.05) 0.81 0.419

45

• Simultaneous confidence level = 64.49%

• Fisher Individual 95% CIs

3.93 (-4.65, 11.05) 0.81 0.4193.93 (-16.75, -1.05) -2.27 0.0273.84 (-9.73, 5.63) -0.53 0.596

3.84 ( -5.24, 10.12) 0.64 0.5274.01 (-20.12, -4.08) -3.01 0.0043.93 (-13.10, 2.60) -1.34 0.1863.93 (-8.61, 7.09) -0.19 0.848

3.93 ( -1.00, 14.70) 1.74 0.0863.93 ( -3.49, 19.19) 2.89 0.0053.84 ( -3.19, 12.17) 1.17 0.247SE of Difference

95% ClAdjusted T-Value

Adjusted P-Value

Feedback

3.93 (-7.98, 9.97) 0.25 0.751

Individual confidence level = 97.43%

3.84 (-12.97, 4.58) -1.09 0.3783.93 (-9.97, 7.98) -0.25 0.7513.93 (-22.07, 0.00) -3.33 0.0033.84 (-15.02, 2.53) -1.62 0.1793.84 (-10.53, 7.03) -0.46 0.669

Results of Statistical Test [Wavelet N

Coif4 12Db8 11

Sym6 12Db4 12

Sym3 12Haar 11

Difference of levels

Difference of Means

Db4 - Coif4 -4.19Db8 - Coif4 -0.99

Haar - Coif4 -13.09Sym3 - Coif4 -6.24Sym6 - Coif4 -1.75

Tukey Simultaneous Tests for Differences of Means

Tukey Pair wise Comparisons

6/12/2017

Db8 - Db4 3.20Haar - Db4 -8.90Sym3 - Db4 -2.05Sym6 - Db4 2.44Haar - Db8 -12.10Sym3 - Db8 -5.25Sym6 - Db8 -0.76Sym3 - Haar 6.85Sym6 - Haar 11.34Sym6 - Sym3 4.49Difference

of levelsCoif4 - Db8 Db4 - Coif4 Db8 - Coif4 Haar - Coif4 Sym3 - Coif4 Sym6 - Coif4

Hsu Multiple Comparisons with the Best (MCB)

Differences of Means

Results of Statistical Test [TUKEY]Mean Grouping Feedback86.57 A

1. Means that do not share a letter aresignificantly different.

2. Grouping Information Using theTukey Method and 95% Confidence

85.58 A84.83 AB82.38 AB80.33 AB73.48 B

SE of Difference95% Cl

Adjusted T-Value

Adjusted P-Value

Feedback

3.84 (-15.47, 7.09) -1.09 0.883

• Individual

3.93 (-12.52, 10.54) -0.25 1.0003.93 (-24.62, -1.56) -3.33 0.0173.93 (-17.52,5.04) -1.62 0.5863.84 (-13.03, 9.53) -0.46 0.997

46

• Individual confidence level = 99.54%

• Tukey Simultaneous 95% Cis

3.93 (-8.33,14.73) 0.81 0.9643.93 (-20.43,2.63) -2.27 0.2233.84 (-13.33, 9.23) -0.53 0.9953.84 ( -8.84, 13.72) 0.64 0.9884.01 (-23.88, -0.32) -3.01 0.0413.93 (-16.78, 6.28) -1.34 0.7643.93 (-12.29, 10.77) -0.19 1.0003.93 ( -4.68, 18.38) 1.74 0.5093.93 ( -0.19, 22.87) 2.89 0.0573.84 ( -6.79, 15.77) 1.17 0.850Difference

of MeansSE of Difference

95% ClAdjusted T-Value

Adjusted P-Value

Feedback

0.99 3.93 ( -7.98, 9.97) 0.25 0.751

Individual confidence level = 97.43%

-4.19 3.84 (-12.97,4.58) -1.09 0.378-0.99 3.93 ( -9.97, 7.98) -0.25 0.751-13.09 3.93 (-22.07,0.00) -3.33 0.003-6.24 3.84 (-15.02,2.53) -1.62 0.179-1.75 3.84 (-10.53,7.03) -0.46 0.669

Conclusion & Future Scope

� The fault locations are predicted through different topologies

been employed to train and test the network. It has beenobserved

� The details of the classification engine design have beenincorporated,

decision boundaries are underlined. It has been observedthat

under different operating conditions have an impact on theefficiency

� The comparative study of the neural classifiers is presented with

is observedthat RBFNN is the most suitabletopology for designing

6/12/2017

is observedthat RBFNN is the most suitabletopology for designing

suitable for this application.

� Statistical tests are conducted to validate the conclusions derived

that RBEFNN along with Haar wavelet is suitable fordesigning

The design of the same classifier for a system withrenewable

Conclusion & Future Scope

of neural networks. The sequence components of currentshave

observedthat RBFNN topology gives best results.

incorporated,and while presenting the design critical parametersfor

that MRA levels and the performance of different motherwavelets

efficiencyof the classification engine.

with the calculation of standard error indices and confusion values.It

designinga classifier. The performanceof Haar wavelet is found

47

designinga classifier. The performanceof Haar wavelet is found

derivedfrom the study. Results of ANOVA, Fisher Group testvalidate

designinga classifier.

renewableenergy sources lies in the scope of the future work.

List of Publications1] Purva Sharma, Akash Saxena, “Critical Investigations onPerformance

(Taylor & Francis),4(1), 1286730, http://doi.org/10.1080/23311916

2] Purva Sharma, Deepak Saini, Akash Saxena, “FaultDetection

and ANN”, Bulletin of Electrical Engineering andInformatics

3] Purva Sharma, Deepak Saini, Ankush Tandon and AkashSaxena

and clean technology, Manipal University, Jaipur (PosterPresentation),

4] Purva Sharma, Akash Saxena, Anjali Jain, S. L.Surana,

6/12/2017

Network”, SKIT Research Journal. (Communicated).

5] Purva Sharma, Akash Saxena, “EssentialInvestigation

Environment”,Walailak Journal of Science and Technology, 2017

6] Purva Sharma, Akash Saxena, “Intelligent Fault Classifier

Topologies”,Engineering Journal of Science and Technology,

7] Purva Sharma, Akash Saxena, “A Few Investigations onFault

(Communicated)

List of PublicationsPerformanceof ANN and wavelet Fault Classifier”,CogentEngineering

23311916.2017.1286730.

Detectionand Classification in Transmission Line using WaveletTransform

Informatics, Vol.5, no.3, pp. 284-295, September 2016.

Saxena” ANN Based Fault Detection algorithm”Workshop onsmart

Presentation),28th November, 2015.

Surana,“Detection of Fault Location by Radial Basis FunctionNeural

48

Investigation on Supervised Learning Fault Classifier underNoisy

2017. (Communicated).

Design based on Wavelet Transform and Artificial NeuralNetwork

, 2017. (Communicated).

Fault Identification and Classification”,Journal of Electrical System,

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