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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
Applications of Artificial Intelligence Techniques in Power Engineering
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
Dr. Akash Saxena, SKIT, Jaipur 3
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”
Applications of Artificial Intelligence Techniques in Power Engineering
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.
Dr. Akash Saxena, SKIT, Jaipur 4
• 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 Techniques in Power Engineering
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
Dr. Akash Saxena, SKIT, Jaipur 5
• 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
Applications of Artificial Intelligence Techniques in Power Engineering
Attributes of Artificial Intelligence
Dr. Akash Saxena, SKIT, Jaipur 6
Application Area 1 : Short Term Load Forecasting
Dr.Akash Saxena,SKIT,Jaipur 7
Applications of Artificial Intelligence Techniques in Power Engineering
�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.
Dr. Akash Saxena, SKIT,Jaipur 8
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
Applications of Artificial Intelligence Techniques in Power Engineering
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
Dr.Akash Saxena,SKIT,Jaipur 9
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
Applications of Artificial Intelligence Techniques in Power Engineering
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
Applications of Artificial Intelligence Techniques in Power Engineering
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
Dr. Akash Saxena, SKIT,Jaipur 13
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
Dr.Akash Saxena,SKIT,Jaipur 14
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)
Dr.Akash Saxena,SKIT,Jaipur 15
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
Dr.Akash Saxena,SKIT,Jaipur 16
0
0.05
0.1
0.15
MAE RMSE MAPE
FFNN
LRNN
Application of Artificial Intelligence Techniques in Power Engineering
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)
Dr. Akash Saxena, SKIT,Jaipur 17
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
Application of Artificial Intelligence Techniques in Power Engineering
•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
Dr. Akash Saxena, SKIT,Jaipur
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
Dr. Akash Saxena, SKIT,Jaipur
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
Dr.Akash Saxena,SKIT,Jaipur 22
8
Application Area 2: Short Term Price Forecasting
Application of Artificial Intelligence Techniques in Power Engineering
• 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.
Dr.Akash Saxena,SKIT,Jaipur 23
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
Dr. Akash Saxena, SKIT,Jaipur
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
Dr. Akash Saxena, SKIT,Jaipur
15
Application Area 2: Prediction Errors
250
300
350
400
450
M
Dr. Akash Saxena, SKIT,Jaipur
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
Dr.Akash Saxena,SKIT,Jaipur 29
Application of Artificial Intelligence Techniques in Power Engineering
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
Dr. Akash Saxena, SKIT,Jaipur 30
Application of Artificial Intelligence Techniques in Power Engineering
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.
Dr. Akash Saxena, SKIT,Jaipur 31
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
Application of Artificial Intelligence Techniques in Power Engineering
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.
Dr. Akash Saxena, SKIT,Jaipur 33
Application of Artificial Intelligence Techniques in Power Engineering
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 <−=∆
Dr. Akash Saxena, SKIT,Jaipur 34
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
Application of Artificial Intelligence Techniques in Power Engineering
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.
Dr. Akash Saxena, SKIT,Jaipur 36
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
Application of Artificial Intelligence Techniques in Power Engineering
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
Application of Artificial Intelligence Techniques in Power Engineering
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
Application of Artificial Intelligence Techniques in Power Engineering
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
Dr.Akash Saxena,SKIT,Jaipur 40
Application of Artificial Intelligence Techniques in Power Engineering
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
Dr.Akash Saxena,SKIT,Jaipur
Application of Artificial Intelligence Techniques in Power Engineering
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)
Dr.Akash Saxena,SKIT,Jaipur
Application of Artificial Intelligence Techniques in Power Engineering
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
Dr.Akash Saxena,SKIT,Jaipur
Application of Artificial Intelligence Techniques in Power Engineering
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 of Artificial Intelligence Techniques in Power Engineering
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
Samples for generator 3
5x 10
-14
2x 10
-13
5x 10
-14
45Dr.Akash Saxena,SKIT,Jaipur
0 100 200 300-5
0
Samples for generator 4
Error
s
0 100 200 300-2
0
Samples for generator 50 100 200 300
-5
0
Samples for generator 6
0 100 200 300-4
-2
0
2
4x 10
-14
Samples for generator 7
Error
s
0 100 200 300-1
0
1x 10
-13
Samples for generator 80 100 200 300
-2
-1
0
1
2x 10
-14
Samples for generator 9
Application of Artificial Intelligence Techniques in Power Engineering
•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.
Dr.Akash Saxena,SKIT,Jaipur
Application Area 5: Power Quality Events Classification
Dr.Akash Saxena,SKIT,Jaipur 47
Application of Artificial Intelligence Techniques in Power Engineering
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 of Artificial Intelligence Techniques in Power Engineering
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
49Dr.Akash Saxena,SKIT,Jaipur
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 of Artificial Intelligence Techniques in Power Engineering
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
50Dr.Akash Saxena,SKIT,Jaipur
�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 of Artificial Intelligence Techniques in Power Engineering
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
51Dr.Akash Saxena,SKIT,Jaipur
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 of Artificial Intelligence Techniques in Power Engineering
Application Area 5: Design of Neural Network
Features Contribution Rates in (%)
0.4
0.5
0.6
52Dr.Akash Saxena,SKIT,Jaipur
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 of Artificial Intelligence Techniques in Power Engineering
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
53Dr.Akash Saxena,SKIT,Jaipur
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 of Artificial Intelligence Techniques in Power Engineering
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
54Dr.Akash Saxena,SKIT,Jaipur
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 of Artificial Intelligence Techniques in Power Engineering
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
�Standard deviation
55Dr.Akash Saxena,SKIT,Jaipur
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
�Standard deviation
� Minimum
�Maximum
�Norm values
Wavelet Transform of Voltage signals
�Minimum Values
�Standard Deviations
Application of Artificial Intelligence Techniques in Power Engineering
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).
56Dr.Akash Saxena,SKIT,Jaipur
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.
Application of Artificial Intelligence Techniques in Power Engineering
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
57Dr.Akash Saxena,SKIT,Jaipur
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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Dr.Akash Saxena,SKIT,Jaipur 58
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