Applications of Artificial Intelligence Techniques in …...Applications of Artificial Intelligence Techniques in Power Engineering Applications of Artificial Intelligence • Philosophy

By

Dr. Akash SaxenaPh.D., MNIT, Jaipur

SMUACEE,MIE (I), MIACSIT, LMISTE, MIAENG

Applications of Artificial Intelligence

in Power System Engineering

Dr. Akash Saxena, SKIT,Jaipur 1

SMUACEE,MIE (I), MIACSIT, LMISTE, MIAENG

Associate Professor, Department of Electrical Engineering

Swami Keshvanand Institute of Technology, Management & Gramothan

Jaipur, Rajasthan ,India

Tel :+91-9672424999

[email protected], [email protected]

https://drakashsaxena.wordpress.com/

Applications of Artificial Intelligence Techniques in Power Engineering

Contents

�Introduction to Artificial Intelligence

�Application Area 1 Short Term Load Forecasting

�Application Area 2 Short Term Price forecasting

Dr. Akash Saxena, SKIT, Jaipur 2

�Application Area 2 Short Term Price forecasting

�Application Area 3 Power System Contingency Ranking

�Application Area 4 Power System Dynamics

�Application Area 5 Power Quality Event Classification


Artificial Intelligence

Intelligence:

“the capacity to learn and solve problems” (Websters dictionary)

in particular,

the ability to solve novel problems

the ability to act rationally


the ability to act rationally

the ability to act like humans

Artificial Intelligence

build and understand intelligent entities or agents

2 main approaches: “engineering” versus “cognitive modeling”


Introduction to Artificial Intelligence

• What is artificial intelligence?

It is the science and engineering of making intelligent machines, especiallyintelligent computer programs. It is related to the similar task of usingcomputers to understand human intelligence, but AI does not have to confineitself to methods that are biologically observable.


• Yes, but what is intelligence?

Intelligence is the computational part of the ability to achieve goals in the world.Varying kinds and degrees of intelligence occur in people, many animals andsome machines.

• Isn't there a solid definition of intelligence that doesn't depend on relating it to human intelligence?

Not yet. The problem is that we cannot yet characterize in general what kinds ofcomputational procedures we want to call intelligent. We understand some ofthe mechanisms of intelligence and not others.


Applications of Artificial Intelligence

• Philosophy Logic, methods of reasoning, mind as physical system, foundations of learning, language,rationality.

• Mathematics Formal representation and proof, algorithms,computation, (un)decidability, (in)tractability

• Probability/Statistics modeling uncertainty, learning from data

• Economics utility, decision theory, rational economic agents


• Economics utility, decision theory, rational economic agents

• Neuroscience neurons as information processing units.

• Psychology/ how do people behave, perceive, process cognitive

Cognitive Science information, represent knowledge.

• Computer building fast computers engineering

• Control theory design systems that maximize an objectivefunction over time

• Linguistics knowledge representation, grammars


Attributes of Artificial Intelligence


Application Area 1 : Short Term Load Forecasting

Dr.Akash Saxena,SKIT,Jaipur 7


�DefinitionLoad forecasting is about estimating future consumptions

based on various data and information available as per

consumer behavior.

Load Forecasting mean forecasting average load in kW or total load

Application Area 1: Short Term Load Forecasting

Load Forecasting mean forecasting average load in kW or total load

in kWh for blocks of 15’, 30’, hour, day, week, month or year for a

daily forecast, weekly forecast, monthly forecast, yearly or multi-

year forecast.


Influencing factors•Historical Load Data (on hourly basis)

•Weather variables (Temperature, humidity, wind, rain)

•Time of the year, the day of the week & the hour of the

day

•Holidays

Mostaccurate

8

Application Area 1: Load Forecasting Accuracy


Acc

ura

cy

Hours Days Weeks Months

Short-term Medium-term Long-term

•Holidays

•Festivals & events

•Economic growth

•Tariff structure

•New load growth

Leastaccurate

Years


cy

Influencing factors

• Weather• Growth Rate• New Customers

Influencing factors

• Weather• Growth Rate• New Customers• LifestyleChange

Influencing factors

•Weather•Events, Holidays, festivals, TV programs

9

Application Area 1: Forecast periods and accuracy level


Acc

ura

cy

Hours/d

aysMonths Years Years

MediumTerm

Benefits

• Network Planning• Supply /Demand Matching• Power Procurement• Rate Case Development

LongTerm

Benefits

• Capacity / Investment Planning• Fuel Mix Decision

ShortTerm

Benefits

• Network Planning• Supply /Demand Matching• Spot Power Procurement• Load Shedding Strategy• Interaction with SLDC 10

Continuous

15

Application Area 1: Portfolio Management Process

Continuous process

Customer trends

Dr. Akash Saxena, SKIT,Jaipur11

Collect Historical Weather Data

Prepare Unconstrained Load Data

Collect Historical Event Data

31

Collect Historical Load and Load Shedding Data

Application Area 1: Load forecasting Model Development


Analyse the DataPrepare Model Input

and TestData

Select a Model/Methodology

Fit the Data and Tunethe Model

Select the Best Model and Implement

Run and Refine the ModelDr. Akash Saxena, SKIT, Jaipur

Application of Artificial Intelligence Techniques in Power Engineering

Application Area 1: Neural Network Design

Input Features

• Dry blub measurements

• Dew point measurements

• Wet bulb measurements• Wet bulb measurements

• Humidity

• Electricity Price


Application Area : Results of Different Topologies

0 50 100 150 200 250 300 350 400 450 5000.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Load

FFNN Model

Actual Load


Sample

0 50 100 150 200 250 300 350 400 450 500-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

Samples

Err

or o

f FF

NN

Error in prediction

• Error Indices are defined as

the difference between actual

and predicted load by ANN.

• Root Mean Square of Error

(RMSE)

Application Area 1 : Evaluation Criterion

( ) ( )( )iy

iyiye

act

preact ][ −=

( ) ( )( )2

1

1∑

=

−=N

ipreact iyiy

NRMSE

(11)

(RMSE)

• Mean Absolute Error (MAE)

• Mean Absolute Percentage

Error (MAPE)


1=i

( ) ( )∑=

−=N

ipreact iyiy

NMAE

1

1

( ) ( )( ) %100

][1 ×−

=iy

iyiy

NMAPE

act

preact

Application Area : Comparison of Different Topologies

0.2

0.25


0

0.05

0.1

0.15

MAE RMSE MAPE

FFNN

LRNN


Efficient Load Forecast by Principal Component Analysis

Principal

componentsEigen values

Contribution

rates %

Dry bulb 2.2576 0.4515

• Principal Component Analysis isinvestigated to reduce the dimensionsof the input features. PCA performs anorthogonal linear transformation to thebasis of correlation eigenvectors andDry bulb 2.2576 0.4515

Dew point 1.7505 0.3501

Wetbulb 0.9566 0.1913

Humidity 0.0011 2.25E-04

Electricity Price 0.0342 0.0068

basis of correlation eigenvectors andprojects onto the subspace spanned bythose eigenvectors corresponding to thelargest eigen values

• Three RBF models are constituted withthe PCA -RBF-1 (dry blub),RBF2 (DewPoint, Electricity Price) and RBF3 (Drybulb, Dew point, Wetbulb and ElectricityPrice)


15

Application Area 1: Response Curves for Three different Models

0.2

0.4

0.6

0.8

1

1.2

1.4

Load

actualload

1-RBF model

0 100 200 300 400 5000

0.2

0.4

0.6

0.8

1

1.2

1.4

Load

actual load

2-RBF model

Dr. Akash Saxena, SKIT,Jaipur

0 100 200 300 400 5000

Samples

0 100 200 300 400 5000

Sample

0 100 200 300 400 5000

0.2

0.4

0.6

0.8

1

Sample

Load

actual load

3-RBF model

System Model MAE RMSE

1-RBF MODEL 0.1378 0.1634

2-RBF MODEL 0.1438 0.1682

3-RBF MODEL 0.1446 0.1711


•A data set of 1500 observations is taken from the

Australian Electricity market on day basis. The time

interval chosen for the analysis is (2006-2010).

•Log entropies of the demand curves are obtained and

Application Area 1 : Entropy Based Methodology

•Log entropies of the demand curves are obtained and

the values of those entropies are incorporated as input

features to the neural network in the proposed approach.

predicted load is at output.

•Normalization of the data is processed between 0.1 to

0.9 for better matching and regression. Out of the data

set 70% data are considered for training purpose

remaining data are used for testing and validation.19

15

Application Area 1: Response Curves and observations


MODELS RMSE MAE MAPE

(i) WITH ENTROPY

FFNN 0.1087 0.0848 0.6160LRNN 0.1114 0.0864 0.6560

ELMAN 0.1140 0.08760.6879

NARX 0.1123 0.08780.6740

(ii) WITHOUT ENTROPY

FFNN 0.1738 0.13951.3960

LRNN 0.1131 0.08740.6980

ELMAN 0.1167 0.09100.7306

NARX 0.1213 0.0984 0.7079

15

Application Area 1: Prediction Error by Different Topologies

0

0.1

0.2

0.3

ER

RO

R


0 5 10 15 20 25 30 35 40 45-0.4

-0.3

-0.2

-0.1

Hour of the day (Half Hourly )

ER

RO

R

FFNN

NARX

ELMAN

LRNN

Application Area 2: Short Term Price Forecasting


8

Application Area 2: Short Term Price Forecasting


• Decisions for optimal scheduling of the generators, setting the

reserve and planning maintenance require the knowledge of

load and price for the survival in the competitive electricity

market.

• Usually commodity prices are compelled by supply and• Usually commodity prices are compelled by supply and

demand balance but in case of electricity this compilation

doesn’t hold good. Electricity as a commodity can’t be stock

piled and constrained by the system demand and generation

capacity.

• Price forecasting is considered as a basic decision making

issue for power companies.


15

Application Area 2: Consumer Behavior

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

Electricity

Price

(Euro)2005

2006

2007

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

Electricity

Price (Euro)

2004

2005

2006

2007

Dr. Akash Saxena, SKIT, Jaipur

0.00

1 3 5 7 9 11 13 15 17 19 21 23

Hours

Working day

1 3 5 7 9 11 13 15 17 19 21 23

Hours

Good Friday

0.00

20.00

40.00

60.00

80.00

100.00

1 3 5 7 9 11 13 15 17 19 21 23

Electricity

Price (Euro)

Hours

2004

2005

2006

2007

Christmas

0.00

20.00

40.00

60.00

80.00

100.00

1 4 7 10 13 16 19 22

Electricity

Price (Euro)

Hours

2004

2005

2006

2007

Labor Day

15

Application Area 2: Historical Designs

Design1 (D-1,M,Y,H)A (D,M-1,Y,H)B (D,M,Y-1,H)C

Design2 (D-1,M,Y,H-1)D (D,M-1,Y,H-1)E (D,M,Y-1,H-1)F

Design3 (D-1,M,Y,H+1)G (D,M-1,Y,H+1)H (D,M,Y-1,H+1)I

Design4 (D-1,M,Y,H)A (D-1,M,Y,H-1)D (D,M-1,Y,H-1)E

Design5 (D,M,Y-1,H-1)F (D,M-1,Y,H-1)E (D-1,M,Y,H)A

Design6 (D,M-1,Y,H) B (D-1,M,Y,H-1)D (D,M,Y-1,H-1)F

Design7 (D-1,M,Y,H-1)D (D,M-1,Y,H)B (D,M-1,Y,H-1)E

Design8 (D,M,Y-1,H)C (D,M-1,Y,H-1)E (D-1,M,Y,H-1)D

Design9 (D,M,Y-1,H-1)F (D,M,Y-1,H)C (D,M-1,Y,H-1)E

Design10 (D-1,M,Y,H+1)G (D-1,M,Y,H) A (D,M,Y-1,H+1) I

Design11 (D,M-1,Y,H)B (D,M-1,Y,H+1)H (D-1,M,Y,H+1 )G

Design12 (D,M,Y-1,H)C (D-1,M,Y,H+1)G (D,M-1,Y,H+1)H


Design12 (D,M,Y-1,H)C (D-1,M,Y,H+1)G (D,M-1,Y,H+1)H

Design13 (D-1,M,Y,H-1)D (D,M-1,Y,H)B (D,M,Y-1,H) C

Design14 (D-1,M,Y,H)A (D,M-1,Y,H-1) E (D,M-1,Y,H)B

Design15 (D,M,Y-1,H-1) F (D,M-1,Y,H)B (D,M,Y-1,H)C

Design16 (D,M-1,Y,H-1)E (D-1,M,Y,H+1)G (D,M,Y-1,H+1) I

Design17 (D,M,Y-1,H+1)I (D,M,Y-1,H-1)F (D,M-1,Y,H+1)H

Design18 (D-1,M,Y,H+1)G (D-1,M,Y,H)A (D,M-1,Y,H)B

Design19 (D,M,Y-1,H)C (D-1,M,Y,H+1)G (D-1,M,Y,H )A

Design20 (D-1,M,Y,H)A (D,M-1,Y,H)B (D-1,M,Y,H-1)D

Design21 (D,M-1,Y,H+1)H (D,M,Y-1,H)C (D-1,M,Y,H )A

Design22 (D-1,M,Y,H-1)D (D,M-1,Y,H+1)H (D,M,Y-1,H-1)F

Design23 (D,M-1,Y,H-1)E (D,M,Y-1,H-1)F (D,M-1,Y,H+1)H

Design24 (D,M,Y-1,H-1) F (D,M,Y-1,H+1)I (D,M,Y-1,H)C

Design25 (D,M,Y-1,H+1)I (D-1,M,Y,H-1)D (D,M-1,Y,H-1) E

Design26 (D-1,M,Y,H-1)D (D,M-1,Y,H)C (D,M,Y-1,H+1) I

Design27 (D,M,Y-1,H-1)F (D,M-1,Y,H+1)H (D,M,Y-1,H)C

15

Application Area 2: Factorial Design


15

Application Area 2: Prediction Errors

250

300

350

400

450

M


0

50

100

150

200

250

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

S

E

Factorial Designs

15

Application Area 2: Response Curves and Observations

-0.3

-0.2

-0.1

0

0.1

0.2

Err

ors

for

SV

M

0 100 200 300 400 500 600 700-0.4

-0.2

0

0.2

0.4

0.6

Err

or f

or N

AR

X

Dr. Akash Saxena, SKIT, Jaipur

0 100 200 300 400 500 600 700-0.3

Hours

0 100 200 300 400 500 600 700-0.4

Hours

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

FFNN NARX Probabilistic SVM

RMSE

0 100 200 300 400 500 600 700-0.4

-0.2

0

0.2

0.4

Hours

Err

or f

or F

FN

N

Application Area 3: Power System Contingency Ranking



Contingency Ranking

� Contingency: Any operating condition can presents a threat tothe system’s stability known as Contingency.

� Contingency ranking is a pioneer study done with the offlinedatabase of different operating conditions. This ranking isbased on the calculations of the standard indices based onbased on the calculations of the standard indices based onvoltage and reactive of the lines.

� Contingencies can consist of several actions or elements –Simple Example: outage of a single transmission line –Complex: outage of a several lines, a number ofgenerators, and the closure of a normally open transmissionline



Objective of Supervised Learning Model

� To develop a supervised learning based model which can

predict the performance indices for a large interconnected

standard IEEE 39 bus test system(10 generator,46 lines,12

transformers) under dynamic operating scenarios.transformers) under dynamic operating scenarios.

� To develop a classifier which can screen the contingencies of

the power system into three states namely not critical, critical

and most critical.

� To present the comparative analysis of the reported

approaches with the proposed approach based on accuracy in

prediction of the PI.


15

Application Area 3: System Description

38

G1

G9

G6

G8

30

2

37

2526

28

28

118

17

27 24

35

G10

G7

G5

G3

G2

G4

39

9

318

17

16

21

22

23

36

4

15

8

5

14

12

19

6

13

11

10

20

33

32

31

7

34


Line MVA Performance Index (PIMVA )

� = the post contingency MVA flow of line,

S max = the MVA rating of the line I

postiS

MNl

i i

postiLi

MVAS

S

M

WPI ∑

=

=1

max

Simax = the MVA rating of the line I

NL = the number of lines in the system. (46 for NE sys.)

WLi =the weighting factor (=1)

M (=2n) the order of the exponent of penalty function.

To avoid missranking high value of exponential order (n=4) is

chosen in this work.



Line Voltage Reactive Performance Index (PIVQ)

MN

i i

iGi

MNB

iLim

i

spiiVi

VQ

G

Q

Q

M

W

V

VV

M

WPI ∑∑

==

+

∆−

=1

max1

maxmaxiiii

Limi VVforVVV >−=∆ minmin

iiiLim

i VVforVVV <−=∆


iiiii iiii VVforVVV <−=∆

Si = the post contingency Voltage at the ith bus,

Sipost= the specified (base case) Voltage at the ith bus

= the maximum limit of voltage at the ith bus,

= the minimum limit of voltage at the ith bus,

NB = the number of buses in the system,

the real non-negative weighting factor (=1), M( =2n) is the order of theexponent for penalty function.

maxiVmin

iV

15

Application Area 3: Flow of Algorithm

10 Generator 39 busPower System Test Case

(Base Case Data)

Run OPF

Set Value of PG, QG

Random Load variation (100% to 160% of base case)

i=1 to p

Contingency selectionContingency selectionSingle Line outage

(j= 1 to 46)

Run Newton PF for each jth contingency

Calculate and save value of Performance Indices

(PIMVA & PIVQ)

Is j = 46 ?

YES

NO

Is i = p ?

YES

NO

END


Simulated Results

•14,000 patterns are generated, which includes the 46

line outages and different loading patterns (300).

•Out of these 200 patterns are those where Newton

Raphson (NR) method failed to converge.


Raphson (NR) method failed to converge.

Class A (Non-Critical) B (Critical) C (Most Critical)

PI Range <0.2 0.2-0.8 >0.8

Performance Index for classification


Comparative Performance of Different Neural Networks

0.2

0.4

0.6

0.8

1x 10

-3

Mea

n Sq

uare

Err

or (

MSE

)

PIMVA

PIVQ

37

Elman Backprop Cascaded FNN FFNN FFDTDNN Layer Recurrent NARX LSSVM0

0.2

Different Methods

Mea

n Sq

uare

Err

or (

MSE

)

Elman Backprop Cascaded FNN FFNN FFDTDNN Layer Recurrent NARX LSSVM96

96.5

97

97.5

98

98.5

99

99.5

100

Different Regression Agent

Val

ue o

f R

-squ

are

(%)

PIMVA

PIVQ


Sample Results of PI calculations and Contingency Analysis

Outage No. 1345 7811 9014 587 2984

Line No. 6-7 8-9 9-39 25-26 20-34

PIMVA

NR 0.8683 0.5438 0.4971 0.4094 0.1102

Elman Backprop 0.7483 0.7250 0.4635 0.3744 0.1565

Cascaded FBNN 0.7884 0.5030 0.3654 0.4649 0.1557

FFNN 0.8236 0.4457 0.3972 0.3107 0.1270

FFDTD 0.7785 0.5447 0.4482 0.3174 0.1567

Layer Recurrent 0.8330 0.7349 0.3381 0.2192 0.1150

NARX 0.8177 0.3869 0.2877 0.3399 0.1465

38

NARX 0.8177 0.3869 0.2877 0.3399 0.1465

LS-SVM 0.8588 0.5432 0.4865 0.4014 0.1098

PIVQ

NR 0.8421 0.5846 0.3876 0.4232 0.1201

Elman Backprop 0.8143 0.6510 0.4231 0.3647 0.1345

Cascaded FBNN 0.8001 0.4322 0.3870 0.4515 0.1141

FFNN 0.8436 0.5561 0.4015 0.3484 0.1220

FFDTD 0.8015 0.5334 0.4312 0.3486 0.1546

Layer Recurrent 0.8451 0.5457 0.3342 0.2247 0.1340

NARX 0.8245 0.3475 0.3015 0.3846 0.1426

LS-SVM 0.8425 0.5901 0.3870 0.4231 0.1210

ClassLS-SVM C B B B A

NR C B B B A


Key Points

� It is observed that neural nets of different topologiesexhibit their quality to act as a regression agent.However, the best regression results are based on MSEand R are exhibited by LS-SVM.

�A binary classifier is obtained with three binary classes

39

�A binary classifier is obtained with three binary classesbased on the values of Performance Indices. Theperformance of the SVM as a classifier is exhibitedthrough the comparison of the results with NR method.

� It is concluded that SVM shows a satisfactory responseto classify the contingencies.

Dr.Akash Saxena,SKIT,Jaipur

Application Area 4: Power System Dynamics



Preface: Coherency Detection

�Power system dynamics is the study of behaviour of thegenerator’s swing after the power network is subjected to adisturbance.

� Preventive Control Strategies are initiated and applied on thegenerators which are coherent.

41

� Preventive Control Strategies are initiated and applied on thegenerators which are coherent.

� After a disturbance, generator rotors exhibits swing to obtainnew operating equilibrium. The machines which show similarbehaviour (power angle variation) are known as CoherentMachines.

�Determination of coherency is important aspect for decisionmaking of load shedding and generator rescheduling. The Task isperformed through Artificial Intelligence Techniques



Application Area 4: Classification Definition

Group Range of at FCT+48 cycles

1 <75’(Least advanced generator)

2 75’-150’

iδ∆ o75< oo 15075 − oo 200150 −o200> iδ∆ o75< oo 15075 − oo 200150 −o200>

42

Classification Criterion for Generator Coherency Detection

3 150-200’

4 >200’ (Most advanced generator)



Case 1: A three phase fault of 12 cycles on bus 28 (100 Samples)

Case 2: A three phase fault of 12 cycles on line 21-22 (100 Samples)

Case 3: A three phase fault of 12 cycles on line 2-25 (100 Samples)

Application Area 4: Case Study

50

60

Rel

ativ

e R

otor

Ang

le (

rad)

200

43

0 1 2 3 4 5-10

0

10

20

30

40

Time (s)

Rel

ativ

e R

otor

Ang

le (

rad)

Generator 9

0 .5 1 1.5 2 2.5-50

0

50

100

150

Time(s)

Rel

ativ

e R

otor

Ang

le (

rad)

Generator 9

Generator 5



Application Area 4: Classification Results

Operating Loading Generator Number

scenarios Sample G-1 G-2 G-3 G-4 G-5 G-6 G-7 G-8 G-9

Case1

1 1 2 2 2 3 2 2 2 4

2 1 2 2 3 3 3 3 2 4

3 1 2 2 3 4 3 3 2 4

4 1 2 2 2 3 2 2 2 4

5 1 2 2 2 3 2 2 2 4

6 1 2 2 2 3 2 2 2 4

7 1 2 2 2 3 2 2 2 4

8 1 2 2 2 3 2 2 2 4

9 1 2 2 2 2 2 2 2 4

1 1 2 2 3 3 2 2 3 3

2 2 2 2 3 4 3 3 3 3

44

case 2

2 2 2 2 3 4 3 3 3 3

3 1 2 2 3 4 3 3 4 4

4 2 3 3 4 4 4 4 4 4

5 1 1 2 2 2 2 2 2 2

6 2 3 3 4 4 4 4 4 4

7 1 2 2 4 4 3 3 4 4

8 2 3 3 4 4 4 4 4 4

9 1 2 2 3 3 2 3 2 3

Case 3

1 2 3 4 4 4 4 4 4 4

2 2 2 3 4 4 4 4 2 3

3 2 3 3 4 4 4 4 3 4

4 2 3 3 4 4 4 4 3 3

5 2 3 4 4 4 4 4 3 4

6 2 3 4 4 4 4 4 4 4

7 2 3 4 4 4 4 4 3 4

8 2 3 4 4 4 4 4 3 3

9 2 3 3 4 4 4 4 3 3


Application Area 4: Errors in Predictions

0 100 200 300-2

0

2x 10

-14

Samples for generator 1

Error

s

0 100 200 300-5

0

5x 10

-14

Samples for generator 20 100 200 300

-5

0

5x 10

-14


5x 10

-14

2x 10

-13

5x 10

-14

45Dr.Akash Saxena,SKIT,Jaipur

0 100 200 300-5

0


Error

s

0 100 200 300-2

0


-5

0


0 100 200 300-4

-2

0

2

4x 10

-14


Error

s

0 100 200 300-1

0

1x 10

-13


-2

-1

0

1

2x 10

-14



•Offline time domain simulation studies are performed with three criticalcontingencies and the deviation of the rotor angles of the generators areobserved with reference to swing generator.

•It is observed that with the change in the operating conditions the

Application Area 4: Key Points

46

•It is observed that with the change in the operating conditions theranking of the generator swings from one state to another state, hence itis concluded that the effect of operating condition on generator swing isempirical.

•Network errors for the determination of the state of generator’s areplotted. Low values of errors indicate the efficacy of the SVM to deal withclassification task.


Application Area 5: Power Quality Events Classification



Application Area 5: Preface -Power Quality Events

�Various events affect the quality of power at distribution end.

Detection of these events is major thrust area since last decade.

48

�In PQ problems the deviation of voltage and current is

observed from the ideal waveforms.

�Improvement of the quality refers to three common

approaches namely identification, determination and removal

of the above said deviations from the voltage/ current signals.

Dr.Akash Saxena, SKIT, Jaipur


Application Area 5: Definitions of Events

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4-1

0

1Normal

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4-1

0

1Voltage sag

Voltage swell

Categories DurationVoltage

Magnitude

Normal -Fundamental

values

Short term


0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4-2

0

2Voltage swell

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4-1

0

1Harmonics

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4-2

0

2Transient

Time(s)

Voltage(p.u.)

SagShort term

(up to 1 min)0.1-0.9 p.u.

SwellShort term

(up to 1 min)1.1-1.4 p.u.

HarmonicSteady state

THD>5

Transients <50 ns 0-4 p.u.


Application Area 5: Signal Processing

•Hilbert transform is a mathematical tool for generation of an

analytical signal from real signal. It is obtained by convolving the

real signal g(t) with the function(1/πt).

gH(t)=g(t)(1/πt)=1/π


�A wavelet series is a representation of a square integrable

complex valued function by a certain ortho-normal series

generated by a Wavelet. Nowadays, wavelet transformation is

one of the most popular of the time-frequency-transformations.

�Critical issues are choice of MRA level , Mother wavelets and its

vulnerability towards noise.


Application Area 5: Proposed Methodology

�Two signal processing techniques namely Hilbert and Wavelet

Transforms (Haar,7) are applied to extract potential features from the

voltage signals of the system.

�Different statistical attributes like maximum, minimum, standard

deviation and norm values are obtained from these transform


deviation and norm values are obtained from these transform

�These values are considered as input features of ANN. Binary class of

different events are assigned.

�Construction of three different classification engine on the basis of

Principal Component Analysis is carried out and the errors of

prediction are compared with the confusion matrix.


Application Area 5: Design of Neural Network

Features Contribution Rates in (%)

0.4

0.5

0.6


34.64

29.28

10.34 9.447.24

4.883.09

1.08 0.01

0

0.1

0.2

0.3

0.4

SVM-1 SVM-2 SVM-3 RBFNN

MSE

MAE

RMSE


Application Area 5: Confusion Matrix

1

2

3

29511.8%

00.0%

00.0%

00.0%

50020.0%

00.0%

60.2%

30.1%

49119.6%

30.1%

00.0%

00.0%

00.0%

00.0%

00.0%

97.0%3.0%

99.4%0.6%

100%0.0%

Ou

tpu

t Cla

ss Confusion Matrix


1 2 3 4 5

3

4

5

0.0%

2058.2%

00.0%

59.0%41.0%

0.0%

00.0%

00.0%

100%0.0%

19.6%

00.0%

00.0%

98.2%1.8%

0.0%

49719.9%

00.0%

99.4%0.6%

0.0%

00.0%

50020.0%

100%0.0%

0.0%

70.8%29.2%

100%0.0%

91.3%8.7%

Target Class

Ou

tpu

t Cla

ss

•Standard deviations

•Minimum values

Hilbert transform of voltage

Signals


Application Area 5: Confusion Matrix

1

2

3

45318.1%

00.0%

00.0%

00.0%

50020.0%

00.0%

00.0%

00.0%

50020.0%

542.2%

70.3%

00.0%

00.0%

00.0%

00.0%

89.3%10.7%

98.6%1.4%

100%0.0%

Ou

tpu

t Cla

ss

Confusion Matrix

�Standard deviation

� Minimum


1 2 3 4 5

3

4

5

0.0%

471.9%

00.0%

90.6%9.4%

0.0%

00.0%

00.0%

100%0.0%

20.0%

00.0%

00.0%

100%0.0%

0.0%

43917.6%

00.0%

87.8%12.2%

0.0%

00.0%

50020.0%

100%0.0%

0.0%

90.3%9.7%

100%0.0%

95.7%4.3%

Target Class

Ou

tpu

t Cla

ss � Minimum

�Maximum

�Norm values

Hilbert Transform of Voltage Signals


Application Area 5: Design of Neural Network

1

2

43017.2%

00.0%

00.0%

50020.0%

00.0%

30.1%

210.8%

00.0%

00.0%

00.0%

95.3%4.7%

99.4%0.6%

Confusion Matrix

Hilbert Transform of Voltage Signals



1 2 3 4 5

3

4

5

00.0%

702.8%

00.0%

86.0%14.0%

00.0%

00.0%

00.0%

100%0.0%

49719.9%

00.0%

00.0%

99.4%0.6%

00.0%

47919.2%

00.0%

95.8%4.2%

00.0%

00.0%

50020.0%

100%0.0%

100%0.0%

87.2%12.8%

100%0.0%

96.2%3.8%

Target Class

Ou

tpu

t Cla

ss


� Minimum

�Maximum

�Norm values

Wavelet Transform of Voltage signals

�Minimum Values

�Standard Deviations


Publications: Related to Artificial Intelligence Techniques

1. Bhanu Pratap Soni, Akash Saxena and Vikas Gupta,“Application of Support Vector

Machines for Fast and Accurate Contingency Ranking in Large Power System”, Third

International Conference on information system design and intelligent applications, 9th

January 2016.

2.Bhanu Pratap Soni, Akash Saxena and Vikas Gupta, ”A least square support vector

machine based approach for contingency classification and ranking in a large power

system” Cogent OA Engineering, (Taylor and Francis Group).


system” Cogent OA Engineering, (Taylor and Francis Group).

3. Esha Gupta and Akash Saxena “Grey Wolf Optimizer based Regulator Design for

Automatic Generation Control of Interconnected Power System” Cogent OA

Engineering, (Taylor and Francis) .

4. Nikita Mittal and Akash Saxena, ”Layer Recurrent Neural Network based Power System

Load Forecasting”, Telekomonika Indonesian Journal of Electrical Engineering.

TELKOMNIKA Vol. 16, No. 3, December 2015 : 423 – 430.

5.Purva Sharma, Deepak Saini, Ankush Tandon and Akash Saxena” ANN Based Fault

Detection algorithm” Workshop on smart grid and clean technology, Manipal

University, Jaipur (Poster Presentation),28th November,2015.


Publications: Related to Artificial Intelligence Techniques

6. Kuldeep Saini, Akash Saxena and S.A. Siddiqui ” ANN based Contingency ranking of a

Large Power system ”Workshop on smart grid and clean technology, Manipal

University, Jaipur (Poster Presentation),28th November,2015.

7.Bhanu Pratap Soni, Akash Saxena and Vikas Gupta, ”Supervised learning paradigm

based on least square Support Vector Machine for contingency ranking in a large power

system” International Congress on information and communication technology, 9th


system” International Congress on information and communication technology, 9th

october 2015.

8.Bhanu Pratap Soni, Akash Saxena and Vikas Gupta,”Support Vector Machine based

approach for accurate contingency ranking in power system” 12th IEEE- India

International Conference Electronics Environment Electronics communication Computer

and Control (Indicon),20th December 2015.

Other Details are at :

https://drakashsaxena.wordpress.com/research-publications/

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