Power trasmission line energy saving through Machine Learning

Feature Selection Techniques for Advanced Power System Fault
Analysis
By
Dr. Debani Prasad Mishra
ASST. PROFESSOR
(EE Department)
IIIT BHUBANESWAR
1
ONLINE FACULTY DEVELOPMENT PROGRAMME
(FDP) ON
Feature Engineering: The Backbone of Effective AI and
ML Solution Applications
(12th
– 23th
May 2025)
Organized by
Electronics & ICT Academy, NIT Warangal
In Association With
Department of CSE and EE,
International Institute of Information Technology (IIIT)
Bhubaneswar
Sponsored
by Ministry of Electronics and Information Technology
(MeitY), GoI

TABLE OF CONTENT
Section- 1 Introduction
1)General Overview
2) Importance of the work in the present scenario
3) Solution of the key problem
4) Objective of the proposed work
5) Literature Review
6) Entire protection scheme for fault classification and location
7) Detail structure for fault classification methods
8) Ground detection for fault classification methods
9) Performance criteria for fault classification and location method
10) Flow chat for fault location method
Section -II Combined signal processing and Machine learning based technique
1) Signal processing technique (DWT/ WPT / S Transform)
2) Feature selection technique (GA/ PSO/ FFS)
3) Artificial intelligence technique ( ANN/SVM /ELM)
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TABLE OF CONTENT
Section -III Fault classification and location of different configuration of the Transmission line
a) Fault classification and location of the Transmission line
1) System under study
2) Proposed Hybrid Technique
3) Results and Discussion
4) Comparison with other researcher
5) Conclusion
b) Fault classification and location of the Transmission line with TCSC
1) System under study
2) Proposed Hybrid Technique
3) Results and Discussion
4) Comparison with other researcher
5) Conclusion
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INTRODUCTION
 Electric power systems becoming complex and exposed to failure of their components.
 Restore the supply, faulty element disconnected
 Prolonged line outage and severe economic losses
 Early repair to prevent recurrence and major damage
 Restoration is done after the repair of the damage caused by the fault
 Whole line has to be inspected to find the damaged place
 Saving money and time for the inspection and repair
 Better service due to faster restoration
 Proper rectification, equipment replacement and re-evaluation of control strategies.
 Continuous and uninterrupted power supply, fault classification and location is important
SECTION : I
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Transmission lines:765/ 400/220/132 kV
EHT Customer:
400/220/132kV
Generating
Station
Generating
Step Up
Transformer
Substation
Step Down
Transformer
Sub-transmission
Customer 33kV
LT Customer
400V and 220V
SCHEMATIC DIAGRAM OF ELECTRICAL GRID

INTRODUCTION
 Classification and location of the fault is increased in application of a distribution line due to
the operation of deregulated environment and its tendency to compete in the power sector for
maximum availability of the power supply.
Protection engineers find challenges in certain applications like:-
Classification and Location of fault in a transmission and distribution line
 Additional transients during fault which is not easily visible on inspection
 Classification and location of fault in underground cable
 repair requires large time and labour, cost is large, accuracy required is very high
 To overcome the mentioned problems, following steps are taken:-
 Necessary to develop an algorithm which can easily classify and locate the fault in a
transmission and distribution line. [1-3]
SECTION : I
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IMPORTANCE OF THE WORK IN THE PRESENT SCENARIO
 Fault must be located properly, otherwise the whole line has to be inspected by the maintenance
and operation crew to find the exact location of the fault.
 Proper classification and location identifies the part of the transmission line that has been
faulted
 The patrolling vehicle of the transmission line operation and maintenance agency can reach the
spot at which the fault has occurred
 Take up repair/correction activates without wasting any further time.
 Tripping of lines in an important transmission corridor can lead to reduced levels of power
from one port of the country to the other (from a power surplus area to power starved area)
 If important transmission line trip hunting of the system,
 Collapse whole of the grid is also possible.
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 Outage time is minimized/ Restoration of power supply becomes fast
 Time and energy of the maintenance crew is reduced
 Economic losses are reduced
 Better Power Delivery
 Reduces the computational complexity of learning and prediction
 Unaffected by noise
 Fault classification and location problem in series compensated transmission line is
eliminated.
 Minimizes the fault classification and location errors
SOLUTION OF KEY PROBLEMS
SECTION -I
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OBJECTIVE OF THE PROPOSED WORK
 The key objective of the is to develop a fast and correct fault classification and location method
for different configurations of transmission and distribution system.
 To achieve the main goal, the following sub-objectives have to be met:
 Effect of the shunt capacitance has to be eliminated
 A Single cycle of post fault current and voltage signal has to be acquired for investigation
purpose.
 Reduce the computation time and complexity of the the faulted data by using feature selection
technique.
 Redundant features have to be removed and optimum features have to be chosen for the overall
feature set to enhance the prediction accuracy.
 The simulation period required by the fault analyzer has to be reduced.
 A robust fault analysis scheme has to be implemented which should be insensitive to parameter
changes.
 To meet the above objective, an enhanced hybrid method is established to analysis the fault in
the transmission network.
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LITERATURE REVIEW
 Various techniques to estimate accurate fault location in a Transmission line
Impedance measurement based method
 Single ended Impedance based method use line terminal voltage and current before and
during the fault
 Test system (400 kV, 300 km)
 Fault location error less than 1%.
 The main drawback is that they have poor accuracy for high impedance fault.
SECTION : I
[Capar, A.; Arsoy, A. B. (2015) A performance oriented impedance based fault location
algorithm for series compensated transmission lines,” Electrical Power and Energy Systems 71
pp. 209–214 ]
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For Transmission line

LITERATURE REVIEW
 Two-ended impedance based technique is applied to localize the fault to eradicate the single
end impedance methods in the Transmission line.
 The drawback of this technique is a high calculation burden due to measurement of current
and voltage signal at both ends of the TL.
 However increase the accurateness to localize the fault
SECTION : I
[Dabbagh, M. A.; Kapuduwage, S. K. (2005) Using instantaneous values for estimating fault
locations on series compensated transmission lines, Electric Power Systems Research, vol. 76,
issues. 1-3 , pp. 25-32.]
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LITERATURE REVIEW
 Impedance based technique is applied to classify the fault to eradicate the single end
impedance methods in the Transmission line.
 The Advantage of this method is its effectiveness in case of several typical cases
 The drawback of this technique is failed particularly in case of high impedance faults and
also at other typical cases
SECTION : I
[Prasada, C. D.; Srinivasua, N. (2015) Fault Detection in Transmission Lines using Instantaneous
Power with ED based Fault Index, Procedia Technology , 21, pp. 132 – 138.]
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LITERATURE REVIEW
SECTION : I
[Hasheminejad, S.; Seifossadat, S. G.; Joorabian, M. M. (2016) Traveling-wave-based protection of
parallel transmission lines using Teager energy operator and fuzzy systems, IET Gener. Transm.
Distrib., Vol. 10, Iss. 4, pp. 1067–1074]
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 Intelligent traveling-wave (TW) based method is applied in transmission line for the location and
classification of faults in parallel Transmission line.
 To extracts the TWs from the power signal Teager energy operator (TEO) is implemented.
 The time difference between the first two TWs and the TWs’ propagation speed is applied to
analyze the faults
 The effect of Current transformer (CT) saturation is not considered in the algorithms
 This technique has less error to localize the faults in high resistance faults path.
 But the main drawbacks are
 Calculation burden and Costly
 High sampling frequency is used, which is a challenging task for real time use

LITERATURE REVIEW
 Classification and location of the faults in the Transmission line by DWT in combination with
SVM and ELM is represented .
 In this scheme, SVM is used for fault classification and ELM for faulty position.
 It is observed that it requires a large amount of time to adjust optimal parameters of SVM.
 Besides this, the error reported for fault location is relatively large in this method.
SECTION : I
[Malathi, V.; Marimuthu, N. S.; Baskar, S. (2010) Intelligent approaches using support vector
machine and extreme learning machine for transmission line protection, Journal of
Neurocomputing, vol. 73, pp. 2160-2167.
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LITERATURE SURVEY
 Wavelet packet based technique for fault location in a series compensated transmission line .
 Single ended measurement, half cycle of post fault voltage
 Wavelet packet decomposition, Support vector machine are implemented
 Feature extraction, energy, more features
 Noise eliminated by low pass filter
 Large value of fault resistance taken
 Performance evaluation by absolute error and mean square error
 Error reported is large
SECTION:I
[Yusuff, A. A.; Fei, C. A.; Jimoh A.; Munda, J. L. (2011) Fault location in a series compensated
transmission line based on wavelet packet decomposition and support vector regression, Electric
Power Systems Research, vol. 81, Issue 7, pp. 1258-1265.]
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LITERATURE REVIEW
 In order to select the best features for better performance, a feature selection algorithm is
proposed.
 His algorithm involved a feature-weighted version of the k-nearest-neighbor which is able to
capture complex dependency of the target function on its input and makes use of the leave-
one-out error as a natural regularization.
 The new algorithm for feature selection provided improvement in prediction quality and
presented a novel way of exploring neural data.
SECTION : I
[Amir Navot, Lavi shpigelman, Naftali tishby, Eilon vaadia, “Nearest neighbor based feature
selection for regression and its application to neural activity,” in Proc.2006. Advances in neural
information processing systems, Vol.18, pp. 995-1002.]
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CONCLUSION OF LITERATURE SURVEY
SECTION:I
Methods Strength Weakness
ANN
technique
1) ANN is quite successful in
determining the correct fault type.
2) It is easy to use, with a few
parameters to adjust
3) Easy to implement
4) Application of wide range of
problems in real life
5) ANN learns and
reprogramming is not needed.
1) For high dimension problem training
process is complex.
2) Gradient based Back propagation
method gives a local optimum solution for
nonlinear separable pattern classification
problem.
3) Slow convergent in BP algorithm.
4) Convergent depends on the choice of
initial value of weight parameters connects
to the network.
TABLE 1 GENERALISED STRENGTH AND WEAKNESS OF THE TECHNIQUES
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SECTION:I
PNN technique 1) No learning process is required
2) No need to set the initial weights
of the network
3) No relationship between
learning processed and recalling
processes.
4) It is guaranteed to converge in
Bayesian classifier.
5) PNN is fast learning time and is
insensitive to outlier.
1) Required high processing time if the
network is large
2) Difficult to know how many neurons
and layers are required.
3) Learning can be slow
4) Required large memory space to store
the model
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TABLE-1 CONCLUSION OF LITERATURE SURVEY

SECTION:I
ANFIS
technique
1) Hybrid learning rule tunes the
parameters properly
2) Converges much faster
3) Reduce the dimension of the search
space
4) Smoothness and adaptability
1) Computational and complexity is
very high.
ELM technique 1) Only one optimize hidden layer
2) There is no requirement of tuning of
the hidden layer
3) Weight and bias value adjust is not
required in ELM
1) Local minima issue
2) Easy overfitting.
3) Difficult to find the optimal
solution.
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TABLE 1 CONCLUSION OF LITERATURE SURVEY

SECTION:I
SVM
technique
1) High accuracy
2) Work well, even if data is not
linearly separable in the base
feature space
3) Misclassification possibilities
are less.
4) Maximize the margin to
minimize the error bound
5) The dimension of space is not
affected the upper bound
generalize error
1) Speed and size requirement both in
training and testing is more
2) High complexity and extensive memory
requirements for classification in many
cases.
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SECTION:I
Impedance
based
Methods
1) Easy and simple method for
understanding
1)At high fault resistance this method
gives more error.
2)At high impedance fault resistance and
load tap systems the accuracy of the
technique is deteriorated.
Travelling
wave based
technique
1) It is implemented for long
lines.
2) It is not affected by high fault
resistance
1) It is required high speed
communication with a wide bandwidth.
2) During data measurement this
technique is affected by noise
3) Sampling frequency is not applicable
for practical use.
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Fig. 2. Suggested fault classification technique
SECTION:II
DETAIL STRUCTURE FOR FAULT CLASSIFICATION
Ya, Yb, Yc, Yg = Output of phase a,b,c and ground; NN = Neural network
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Signal Processing
SECTION : II
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 The Fourier Transform means finding the frequency content of the
stationary signal.
 No time information is availed in the Fourier Transform.
 In a stationary signal, it is not required to know what time the
frequency component exists
 The STFT window is of finite length. So perfect frequency
resolution.
 If we use a narrow window, the better the time resolution, the
poorer the frequency resolution.
 Wide window, good frequency resolution, the poor time resolution.
 Continuous wavelet transform to overcome the STFT resolution
problem.
 Wavelet transform gives a time-frequency representation of the
non-stationary signal.

 It is the process of choosing a small subset from all the features that is sufficient to predict the target properly.
 It helps in reducing the computational complexity of learning and prediction algorithms and enhances the
prediction accuracy.[21]
SECTION:II
FEATURE SELECTION
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Fig. 6. Feature selection algorithm

FEATURE SELECTION METHOD
SECTION:II
FORWARD FEATURE SELECTION (FFS) METHOD
 Features are iteratively added into a growing subset of inputs and in each step, feature showing
the highest score is added and the rest is discarded.[22]
 An evaluation function to assign scores to features.
 Evaluation function used is
 leave one out (LOO)
 Mean square error (MSE) of the k-nearest-neighbor (KNN) estimator.[23]
 KNN estimator is the weighted average of nearest neighbor.
 Evaluation function is negative (halved) MSE of the weighted KNN estimator.
 It helps to search a locally optimal weight vector by giving scores to weight vector over the
features.
 Thereafter each feature is provided with a rank by the resulting weight which is applied further
to make a subset of optimal features.
 Search algorithm to search for a subset with a high score
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PARTICLE SWARM OPTIMIZATION BASED FEATURE SELECTION
SECTION:II
 Stochastic optimization based technique [25]
 Particle interacts among them to find global optimal solution
 Each particle has its own Position and velocity
 Position of each particle is given in binary form representing the energy feature.
 Fitness function of each particle
 Updated by pbest and gbest
 Best solution achieved in every step of the iteration process so far
 Best solution obtained so far by any particle in the population
 Update new position and velocity
 Choice of particle based on the fitness function of the new updated particle.
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FLOW CHART OF PARTICLE SWARM OPTIMIZATION BASED FEATURE SELECTION
SECTION:II
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Fig. 7. PSO technique

40
FLOW CHART FOR SELECTION OF SUPPORT VECTOR MACHINE PARAMETER BY
PARTICLE SWARM OPTIMIZATION
SECTION:III
Fig . 8. Flow chart to select the optimal parameter of SVM by using PSO
Where c1 & c2 are the acceleration constants or weighting factor, w is the inertia weight or weighting function,
generally w (0,1) wmax and wmin are the final and initial values of weighting coefficient
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GENETIC ALGORITHM BASED FEATURE SELECTION
SECTION:II
 Stochastic search method , explores the search space to attain an optimal solution.
 Operates with a set of population of chromosome represented by a string of
 binary digits.
 Selected chromosome for the next generation on the basis of a fitness function.
 Each coefficient of DWT/WPT decomposition is represented by binary digit .
 One of the feature is selected from the available six features for each coefficient.[27]
• Reproduction,
• Cross-over
• Mutation.
 Reproduction :
 Entire set of chromosomes gets a rank based on the fitness function and
 The selection of chromosomes is done based on the highest ranking.
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SECTION:II
 Crossover :
• To produce a child chromosome
• More than one parent chromosome is considered.
• A uniform crossover is used with a 0.5 mixing ratio between the two parents
• The child chromosome gets approximately half of the genes from one parent and another
half from the other with the crossover point(mask) randomly chosen.
 Mutation:
• Mutation operation is then performed which randomly alters the bit of the chromosome
string with a probability of 0.001.
 Advantage
• It works well with large feature set and has less chance to converge into local optimal
solution.
• Minimum error
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SECTION:II
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Fig. 9. Example of the GA

ARTIFICIAL NEURAL NETWORK
 Computational model, simulates structure & functional aspects of biological neural network
 Multilayer feedforward neural network with gradient descent backpropagation training
algorithm[28]
SECTION:II
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Fig. 10. ANN structure

 Adaptive computational learning algorithm based on statistical learning theory in which the
original input vectors are non-linearly mapped into a high dimensional feature space and the
optimal hyper plane is determined to maximize the generalization ability. Global & Unique
solution, Does not converge into local minima, Prone to Overfitting [32]

SECTION:II
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SUPPORT VECTOR MACHINE (SVM)
Fig. 13. SVM Structure

PARAMETERS OF SUPPORT VECTOR MACHINE (SVM)
 Radial basis function is used as kernel function which made the hyper plane optimal by
maximizing the gap between the two categories
 Integrated software LIBSVM is used for SVM parameters
 The two parameters are soft parameter (c ) and gamma parameter (g)
 Soft parameter performs trade off between allowing train error and forcing rigid margin
 Gamma parameter is the radius of RBF and controls the shape of the separating hyperplane.
SECTION:II
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FAULT CLASSIFICATION & LOCATION IN A TRANSMISSION LINE
SECTION:III
 400 kV, 300 km long transmission line
 Fault is made to occur after every 1 km starting from 1 km of the relaying end up to 300
km.
 Sampling frequency is 30 kHz.
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Fig. 15 System under study

FAULTY CURRENT AND VOLTAGE SIGNAL
SECTION : III
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Fig 17 Shows a-g, and a-b fault having one cycle pre fault and post fault voltage signal
Fig 16 Shows a-g, and a-b fault having one cycle pre fault and post fault current signal

FAULTY CURRENT AND VOLTAGE SIGNAL
SECTION : III
Fig. 19 Shows ab-g, and a-b-c fault having one cycle pre fault and post fault voltage signal
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Fig. 18 Shows ab-g, and a-b-c fault having one cycle pre fault and post fault current signal

DISCRETE WAVELET TRANSFORM DECOMPOSED COEFFICIENTS OF THE
CURRENT SIGNAL
SECTION:III
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Fig . 20. DWT Decomposed coefficients of the current signal

WPT DECOMPOSED COEFFICIENTS OF THE CURRENT SIGNAL
SECTION:III
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Fig . 21. WPT Decomposed coefficients of the current signal

FEATURE EXTRACTION
SECTION : III
 Sampling frequency considered is 30 KHz (600 samples per cycle), so DWT decomposed signal
up to 8th
level and wavelet packet transform decomposition is done up to 4th
level.
 After decomposition process, reconstructed detail coefficient and approximate coefficient of the
current signal is obtained from which 6 statistical features are extracted for DWT and 2 statistical
features are extracted for WPT.
 Feature extraction is a technique to reduce the dimension of large data set by converting it into
set of features.
 The 6 statistical features are:- Energy , Standard Deviation , Mean , Kurtosis , Skewness ,
Entropy
 set of 48 features (6 statistical features x 8 WPT coefficients) is generated in DWT
 The two statistical features are:-
 Energy
 Entropy
 set of 32 features (2 statistical features x 16 WPT coefficients) is generated in WPT
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OPTIMAL FEATURE SET OBTAINED WITH FORWARD FEATURE SELECTION AND
PARTICLE SWARM OPTIMIZATION BASED METHOD
SECTION : III
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TABLE 2 BEST FEATURE BY FFS /PSO METHOD
Signal
Feature selected by
FFS using DWT
coefficients (04
optimal features out
of 48 total features)
Feature selected by
FFS using WPT
coefficients (2
optimal features out
of 32 total features)
Feature selected
by PSO using
DWT coefficients
(04 optimal
features out of 48
total features)
Feature selected
by PSO using
WPT coefficients
(2 optimal
features out of 32
total features)
Current
Standard Deviation
(D2), Mean (D3, D4),
Entropy( D2)
Energy (ADAD4)
Entropy (DDDD4)
Energy (D1),
Standard
Deviation (D7),
Mean(D3, D4)
Energy(DDDD4),
Entropy
(ADDA4)

OPTIMAL & NON-OPTIMAL FEATURE PLOT USING DWT
SECTION: III
Fig 22 (a) Optimal feature plot of
coefficient standard deviation
[D2] of energy of current signal
using FFS method
Fig .22 (b) Optimal feature plot of
coefficient standard deviation [D7]
of current signal using PSO method
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Fig . 22(c). Non-optimal feature
plot of coefficient standard
deviation [D1] of current signal
 Pattern of optimal feature is easy to predict whereas non-optimal feature gave unpredictable and erratic
pattern
 So, concluded that optimal feature plot gives a distinct path for each value of fault distance whereas non-
optimal feature plot shows a random path which makes the prediction of fault classification and location
quite difficult.

OPTIMAL & NON-OPTIMAL FEATURE PLOT USING WPT
SECTION: III
Fig 23 (a) Optimal feature plot of
coefficient Energy [ADAD4] of
current signal by using FFS
Fig .23 (b) Optimal feature plot
of coefficient Energy[DDDD4]
of current signal by using PSO
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Fig . 23(c). Non-optimal feature
plot of coefficient DDAA4
energy of current signal
 Pattern of optimal feature is easy to predict whereas non-optimal feature gave unpredictable and erratic
pattern
 So, concluded that optimal feature plot gives a distinct path for each value of fault distance whereas non-
optimal feature plot shows a random path which makes the prediction of fault classification and location
quite difficult.

OPTIMAL PARAMETER OF SUPPORT VECTOR MACHINE BY USING PARTICLE
SWARM OPTIMIZATION TECHNIQUE
SECTION:III
SVM parameters For fault
classification
For fault distance estimation
For fault
detection of the
ground
For phase fault
detection
Kernel type Radial basis
function
Radial basis
function
Radial basis
function
Gamma (g) 0.4 0.52 0.63
Cost (c) Not used Not used 12.4
Nu (nu) 0.5 0.45 0.15
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TABLE 3 BEST VALUE OF SVM BY PSO

PARAMETER SETTING FOR GENERATING TRAIN AND TEST DATA SET
 Ten type of fault (ag, bg, cg, ab, bc, ca, abg, bcg, cag, abc)
 300 fault location
 8 fault inception angle , 10 type of fault resistance
 Total train data set consists of 240,000 data samples (10 types of fault resistance x 8 types of
fault inception angle x 300 fault distances x 10 short-circuit fault)
 Similarly test data matrix consists of 168, 000 data samples (8 types of fault resistance x 7
types of fault inception angle x 300 fault distances x 10 types of fault).
 test data set is taken as 70% of the train data set
SECTION:III
Data-set Fault resistance
(Rf) (in )
Fault inception angle ()
(in degree)
Train data 0, 1, 5, 10, 20, 40, 50, 70,
100, 150
10°, 20°, 30° , 40°, 50° ,
60°,70°, 80°
Test data 2,9,25,45,65,85, 110, 140 5°,11°,17°,24°,45°, 65°,
90°
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TABLE 4 TRAIN AND TEST DATA PARAMETER

RESULTS AND DISCUSSION
SECTION:III
 It can be noticed that Daubechies with feature selection gives highest accuracy in
fault classification and location than others.
 So, Daubechies is adopted for further analysis.
Different Types of
mother wavelet
For fault classification For fault distance estimation
Fault Classification
Accuracy (%)
Maximum absolute
error (%)
Mean
error
(%)
Biorthogonal (Bior3.1) 88.2 1.2 0.65
Coiflets (coif1) 91.7 0.9 0.45
Symlets(sym2) 94.3 0.75 0.4
Haar 95.3 0.9 0.35
Daubechies (db4) 99.21 0.20 0.10
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TABLE 5 COMPARISON OF DIFFERENT TYPES OF MOTHER WAVELET
TEST RESULT

SECTION:III
 It can be noticed that dB4 with feature selection gives highest accuracy in fault location and
classification than others.
 So, dB4 is adopted for further analysis. 61/93
TABLE 26 COMPARISON OF MOTHER WAVELET TEST RESULT USING
DWT/WPT
Different
order of
Daubechies
mother
wavelet
For fault classification
Accuracy (%)
For fault distance estimation
Maximum absolute error
(%)
Mean error (%)
DWT WPT DWT WPT DWT WPT
dB1 91.2 92.7 1.3 1.02 0.5 0.34
dB2 93 95.2 0.84 0.65 0.35 0.25
dB3 92 94.3 1.0 0.88 0.4 0.30
dB4 97.1 99.21 0.27 0.2 0.2 0.10

SECTION:III
TABLE 7 TEST RESULTS WITH DISSIMILAR SAMPLING FREQUENCIES
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Sampling frequency (kHz) Fault Classification
Fault Location error (%)
Classification accuracy (%)
0.1 93.5 Greater than 0.9
0.3 90.7 Greater than 1.0
50 92.4 Greater than 1.5
100 80.5 Greater than 2.7
Proposed Method (30 kHz) 99.21 Less than 0.21
 Best result for classification and location with 30 kHz sampling frequency

SECTION:III
TABLE 8 TEST RESULT WITH OR WITHOUT OPTIMAL PARAMETER OF SVM
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For fault classification For fault distance estimation
Accuracy (%)
Maximum
absolute error (%)
Mean
error (%)
With Optimized
parameter of SVM with
PSO
99.21 0.20 0.10
Without Optimized
parameter of SVM
95.01 0.32 0.22

SECTION:III
TABLE 9 TEST RESULT OF FAULT CLASSIFICATION DWT-SVM
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Fault type
No of test
samples
True
fault
classificati
on
No. of
misclassifi
cation
Classifi
cation
accuracy
(%)
LG (AG, BG, CG) 64,800 62,300 2500 96.14
LL (AB, BC, CA) 64,800 63,500 1300 97.99
LLG (ABG, BCG, CAG) 64,800 61,550 3250 94.98
LLL (ABC) 21,600 21,090 510 97.63
Total 216,000 208,440 7,560 96.5

SECTION:III
TABLE 10 TEST RESULT OF FAULT CLASSIFICATION WPT-SVM
Fault type
No. of test
data
samples
No. of test
samples
classified
correctly
No. of test
samples
misclassified
Classific
ation
accuracy
(%)
LG (a-g, b-g, c-g) 50,400 49,855 545 98.91
LL (a-b, b-c, c-a) 50,400 50,100 300 99.40
LLG (ab-g, bc-g, ca-g) 50,400 49,970 430 99.14
LLL (abc) 16,800 16,750 50 99.70
Total 168,000 166,675 1,325 99.21
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SECTION: III
TEST RESULTS FOR LOCATION OF ALL FAULT CASES
Fig. 24 Box plot of fault location error with DWT-SVM based method
 Proposed method gives maximum fault location error of less than 0.28%. And mean error 0.15%
66/93

TEST RESULT OF FAULT LOCATION METHOD
SECTION:III
TABLE-11 TEST RESULTS OF FAULT LOCATION METHOD USING DWT-SVM
67/93
 The observation made from Fig 24 are shown in Table 11
Type of fault No. of test
samples
Minimum
error (%)
Maximum
error (%)
Mean error
(%)
Range of the
box (%)
LG (a-g,b-g,c-g) 64800 0.0015 0.27 0.18 0.12-0.23
LL (ab,bc,ca) 64800 0.0011 0.25 0.15 0.05-0.21
LLG (ab-g,bc-
g,ca-g)
64800 0 0.30 0.20 0.13-0.26
LLL (abc) 21600 0.01 0.20 0.15 0.12-0.19

SECTION: III
TEST RESULTS FOR LOCATION OF ALL FAULT CASES
Fig. 25 Box plot of fault location error with WPT-SVM based method
 Proposed method gives maximum fault location error of less than 0.21% and mean error 0.1%
68/93

TEST RESULT OF FAULT LOCATION METHOD
SECTION:III
TABLE-12 TEST RESULTS OF FAULT LOCATION METHOD USING WPT-SVM
Fault type No. of
samples
Minimum
absolute error
(%)
Maximum
absolute error
(%)
Mean fault
distance
error (%)
Range of the
box (error
range)
a-g 16,800 0.00052 0.18 0.10 0.013-0.148
b-g 16,800 0.0027 0.17 0.08 0.022-0.17
c-g 16,800 0.002 0.19 0.10 0.02-0.18
a-b 16,800 0.00021 0.15 0.08 0.014-0.13
b-c 16,800 0.02 0.15 0.07 0.028-0.11
c-a 16,800 0.007 0.14 0.07 0.02-0.12
ab-g 16,800 0.001 0.20 0.10 0.006-0.19
bc-g 16,800 0.006 0.20 0.10 0.041-0.18
ca-g 16,800 0.0012 0.19 0.10 0.013-0.17
abc 16,800 0.0048 0.12 0.04 0.02-0.10
69/93
 The observation made from Fig 25 are shown in Table 12

70
SECTION:III
FOR A SPECIAL CASE FAULT INCEPTION ANGLE= 65˚, Fault Resistance = 45,
Fig . 26 (a) Actual versus predicted distance plot
for AG fault using WPT-SVM
Fig . 26 (b) Actual versus predicted distance
plot for AB fault using WPT-SVM
Maximum fault location error is 0.18 (20-19.82) Maximum fault location error is 0.18 (60-59.82)

SECTION:III
71/93
Fig. 26 (c) Actual versus predicted distance plot
for ABG fault using WPT-SVM
Fig . 26 (d)Actual versus predicted distance plot
for ABC fault using WPT-SVM
It is noticed from Fig. that 0.21% of maximum absolute fault distance error
 LG, LL and LLG gives more error than LLL Fault
 Max Error occurs generally at the source end.
Maximum fault location error is 0.2(20-19.8) Maximum fault location error is 0.12(20-19.88)

SECTION : III
TABLE 13 FAULT LOCATION TEST RESULTS FOR DISTANCES VERY
NEAR TO SOURCE END OF TRANSMISSION LINE USING WPT-SVM
72/93
Actual fault
location (km)
Absolute error (%)
AG Fault AB Fault ABG Fault ABC Fault
2 0.19 0.15 0.20 0.12
4 0.18 0.14 0.18 0.11
6 0.17 0.11 0.17 0.10
8 0.15 0.10 0.16 0.09
294 0.17 0.13 0.18 0.09
296 0.18 0.14 0.19 0.10
298 0.19 0.14 0.20 0.11

SECTION:III
TABLE 14 COMPARISON OF DIFFERENT FAULT CLASSIFIERS TECHNIQUE
73/93
Fault Classifier Classification Accuracy (%)
ANN 96
PNN 97
ANFIS 89
Proposed one (SVC) 99.21

SECTION:III
TABLE 15 COMPARISON WITH OTHER RESEARCHER
74/93
Schemes
Fault Location
error (%)
No. of test samples Classification
accuracy (%)
Method in [34] - - Greater than 0.30
Method in [35] 28,800 99.11 Greater than 0.45
Method in [36] 200 97.2 -
Method in [39] - - More than 2.0
Proposed Method
(WPT-SVM)
168,000 99.21 Less than 0.21

FAULT CLASSIFICATION & LOCATION OF A SERIES COMPENSATED TRANSMISSION LINE
SECTION:III
 400 kV, 300 km long transmission line
 Fault is made to occur after every 1 km starting from 1 km of the relaying end up to 300
km.
 TCSC is placed at the middle of the transmission line
Fig 27. Transmission line with thyristor controlled series capacitor (TCSC)
73/93

FACTS DEVICE APPLICATION
SECTION:III
 Increasing the Power transmission capacity of the existing line.
 Improving the steady state and dynamic stability stability
 Improving damping of different types of power oscillations
 Improving voltage stability
 Reducing the problem of Sub synchronous resonance
 Improving HVDC link performance
74/93

THYRISTOR CONTROLLED SERIES CAPACITOR
SECTION:III
 Capacitor , series combination of reactor and antiparallel connection of thyristor
 Capacitor is protected from overvoltage by a metal oxide varistor (MOV) and an
air gap arrangement connected in parallel to it.
 The MOV protection voltage level depends on the voltage across the capacitor.
Fig. 28 Basic TCSC arrangement
74/93

OPTIMAL FEATURE BY FORWARD FEATURE SELECTION METHOD
SECTION:III
Signal
type
Feature (2) Best coefficient
Current Energy AAAA4, ADAD4, AADA4
Entropy ADDA4, AADD4, DADA4,
DDDA4
78/93
TABLE 16 BEST FEATURE IN CASE OF THYRISTOR CONTROLLED SERIES CAPACITOR
BASED TRANSMISSION SYSTEM BY FORWARD FEATURE SELECTION METHOD

OPTIMAL & NON-OPTIMAL FEATURE PLOT BY FFS
SECTION:III
Fig 29 (a) Optimal feature plot of coefficient
ADAD4 of energy of current signal.
Fig 29 (b) Non Optimal feature plot of
coefficient DDDD4 entropy of current signal
 Pattern of optimal feature is easy to predict whereas non-optimal feature gave unpredictable
and erratic pattern
 So, concluded that optimal feature plot gives a distinct path for each value of fault distance
whereas non-optimal feature plot shows a random path which makes the prediction of fault
location quite difficult. 79/93

SECTION:III
80/93
Fault type
No. of
test data
samples
No. of test
samples classified
correctly
No. of test
samples
misclassified
Classification
accuracy (%)
LG (a-g, b-g,
c-g)
50,400 49,392 1008 98.00
LL (a-b, b-c,
c-a)
50,400 49,745 655 98.70
LLG (ab-g,
bc-g, ca-g)
50,400 49,443 957 98.10
LLL (abc) 16,800 16,673 127 99.24
Total 168,000 165,253 2,747 98.36
TABLE 17 TEST RESULTS OF FAULT CLASSIFICATION FOR TCSC BASED TRANSMISSION
LINE

SECTION:III
TABLE 18 COMPARISON OF DIFFERENT FAULT CLASSIFIERS TECHNIQUE
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Fault Classifier Classification Accuracy (%)
ANN 95
PNN 95.5
ANFIS 88
Proposed one (WPT-SVC) 98.36

SECTION:III
TEST RESULTS WITH ALL FAULT CASES
Fig. 30 Box plot of fault location error with WPT-SVR based method
 Proposed method gives maximum fault location error of less than 0.28%.
82/93

SECTION:III
TABLE 20 COMPARISON WITH OTHER RESEARCHER
83/93
Schemes
Fault Location
error (%)
No. of test samples Classification accuracy
(%)
Method in [40] 25200 Average accuracy
93.92%
-
Method in [41] 200 More than 95.09% -
Method in [42] 25600 More than 97.2% -
Method in [43] - - Maximum error
5.28%
5%
3%
Proposed Method 168000 98.36% Less than 0.28

CONCLUSION
SECTION:III
 Support vector machine with combined WPT based method estimate the type of fault and
distance scheme in a long transmission line is proposed.
 The data window is reduced as it uses one cycle of post fault current signal from the sending end
of the transmission line to classify and determine the fault location.
 The uniqueness of the proposed technique is that it uses transient data to analyze the fault, a
large number of features are collected by wavelet packet transform,
 The method is robust to parameter variation as it uses a wide range of operating conditions.
 FFS/PSO feature selection method is applied to remove redundant features, where FFS methods
is enhancing the prediction accuracy as compared to PSO.
 The simulation result shows for transmission line, maximum fault classification accuracy
(99.21%), maximum fault position error (less than 0.21%) and maximum mean error 0.1% using
WPT-SVM.
 It is noticed that for transmission line with TCSC, fault classification accuracy for all test cases
is 98.36% , the fault location error less than 0.28% and mean error less than 0.15% using WPT-
SVM . 84/93

OVERALL CONCLUSION
SECTION: III
 This work suggests an accurate hybrid technique for obtaining fast, accurate and robust fault
classification and location in transmission and distribution line.
 The percentage of error for classification of the fault and fault distance estimation is nominal
and is much smaller than the traditional methods.
 The proposed hybrid technique uses transient data for classification and location of the fault.
 The feature selection method reduces the dimension of the total feature set and increase the
prediction accuracy.
 The proposed technique is robust as it implements wide range of operating conditions to
generate the train and test data set for classification and location of the fault.
85/93

FUTURE SCOPE
SECTION: III
 Accurate fault detection, classification and location in HVDC transmission line is to be carried
out.
 Advance signal processing methods and advanced intelligent techniques are used for analysis of
the fault.
 To detention various feature and actions are relatively efficient and gives to the user to obtain the
critical information through visualization.
 Satellite spitting image or geographic pictures are provided for location of the faults where faults
are more recurrent.
 Which will helps to know the main cause of permanent faults.
 Detection of the inception faults in the underground cable can be extended further.
 So that suitable extent can avoid from tripping of the feeder and also decrease the uninteresting
voltage transients.
 The hybrid method used in this thesis can be further used for islanding detection in power
distribution network with multiple DG interference
86/93

APPENDICES
FOR TRANSMISSION LINE SYSTEM
PARAMETERS OF THE SYSTEM UNDER STUDY [20]
(i) Receiving and Sending end voltage source parameter : Positive sequence impedance (Z1): 1.31+ j16.0 Ω
Zero sequence impedance (Z0): 2.22 + j 27.6 Ω ; Frequency of the system: 50 Hz
(ii) Parameter of long transmission line :
Length: 300 km, Voltage: 400 kV; Impedance of positive sequence = 8.15 + j 94.5 
Impedance of zero sequence = 92.5 + j 308 ; Positive sequence capacitance = 14 nF/km, Zero sequence
capacitance = 7.5 nF/km
DETAILS OF TCSC PARAMETER
L = 61.9 mH, C = 21.977 μ F
 Details of the parameters of PSO based feature selection
C1= 2.05, C2= 2.05, Particle size = 60, No. of iteration = 100, Wmin= 0.4, Wmax= 0.9
Details of the parameters of ANN are given in Table 68 and optimal value of SVM parameter by PSO is given in Table
3
87/93

TABLE 68 DETAILS OF ARTIFICIALNEURAL NETWORK (ANN)
Network type Feed-forward back propagation network
Training function Levenberg-Marquardt
Size of first hidden layer 50
Size of second hidden layer 05
Size of input layer The size of the optimal feature set depends on (04 in
case of DWT and 07 in case of WPT)
Size of output layer 01
Train parameter goal 7e-9
Performance function MSE(mean squared error)
No. of Epochs 1000
88/93
Parameters of PNN and ANFIS :
Kernel function used in PNN: Radial basis function
Spread factor () = 0.025
ANFIS generates a sugeno-type fuzzy inference system (FIS) using subtractive clustering technique with a
radius of 0.5.
THE PARAMETERS DETAILS OF GA BASED FEATURE SELECTION :
Population Size = 60, Cross-over rate = 0.8, Mutation rate = 0.01. Iteration = 100
Particle swarm optimization parameters
C1= 4, C2= 4, Particle size = 50, No. of iteration = 1000
Wmin= 0.5, Wmax= 0.9

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LIST OF SOME SELECTED PUBLICATIONS
1. P. Ray, S. R. Arya, D. P. Mishra, “Intelligence Scheme for fault location in a combined overhead transmission line &underground cable,” International Journal of
Emerging Electric Power Systems. Vol 19, Issue 5, 2018, pp. 1-18, DOI: 10.1515/ijeeps-2017-0277 (DE GRUYTER, Scopus, ESCI, IF-1) ISSN: 1553-779X
2. D. P. Mishra and P. Ray, “Fault detection, location and classification of a transmission line,” Neural Computing and Applications, Vol. 30, 2018, No. 5,pp. 1377-
1424. DOI 10.1007/s00521-017-3295-y (Springer)(SCI, IF-6) ISSN: 09410643, 14333058
3. P. Ray and D. P. Mishra, “Support Vector Machine Based Fault Classification and Location of a Long Transmission Line”, Engineering Science and Technology, an
International Journal 19 (2016) pp.1368–1380. https://doi.org/10.1016/j.jestch.2016.04.001. (Elsevier) (SCI, Scopus, IF-5.7), Online ISSN: 2215-0986
4. P. Ray and D. P. Mishra, “Application of extreme learning machine for underground cable fault location,” International Transactions on Electrical Energy Systems,
vol. 25, Issue. 12, Dec. 2015, pp. 3227–3247.(Willy) (SCI, IF-2.3), https://doi.org/10.1002/etep.2032 Online ISSN:2050-7038 , Print ISSN:2050-7038
5. S. K. Panda, P. Ray, and D. P. Mishra, “ An Efficient Short-Term Electric Power Load Forecasting Using Hybrid Techniques," International Journal of Computer
Information Systems and Industrial Management Applications, Volume 12 , pp. 387-397 , Nov., 2020. (Scopus, , SJR: 0.16)
6. S. R. Das, D. P. Mishra, P. K. Ray, S. R. Salkuti, A. K. Sahoo, " Power Quality Improvement using Fuzzy Logic Based Compensation in a Hybrid Power System,"
International Journal of Power Electronics and Drive System (IJPEDS), Vol. 11, No. 3, Dec 2020, (Scopus, CiteScore: 1.49, SJR: 0.304)
7. A. P. Hota, S. Mishra, D. P. Mishra, S. R. Salkuti, “ Allocating active power loss with network reconfiguration in electrical power distribution systems,"
International Journal of Power Electronics and Drive System (IJPEDS), Vol. 11, No. 3, Dec 2020, (Scopus, CiteScore: 1.49, SJR: 0.304)
8. S. R. Das, P. K. Ray, D. P. Mishra, H. Das, “Performance assessment of PV integrated Model Predictive Controller based hybrid filter for Power Quality
Improvement”, International Journal of Power Electronics, 2020. (Inder science, Scopus, SJR-0.14)
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LIST OF SOME SELECTED PUBLICATIONS
1. PAPERS PUBLISHED IN INTERNATIONAL CONFERENCE
1. S. Jena, D. P. Mishra, S. R. Salkuti, (2023). Fault Detection, Classification, and Location in Underground Cables. In: Salkuti, S.R., Ray, P., Singh, A.R. (eds) Power Quality in Microgrids: Issues,
Challenges and Mitigation Techniques. Lecture Notes in Electrical Engineering, vol 1039. Springer, Singapore. https://doi.org/10.1007/978-981-99-2066-2_10, Publisher Name: Springer, Singapore, Print
ISBN:978-981-99-2065-5, Online ISBN:978-981-99-2066-2, pp 195-215.
2. Mishra, D.P., Biswal, P., Sahu, S.S., Dash, S., Giri, N.C. (2023). Radial Basis Function Neural Network with Wavelet Transform for Fault Detection in Transmission Line. In: Rani, A., Kumar, B.,
Shrivastava, V., Bansal, R.C. (eds) Signals, Machines and Automation. SIGMA 2022. Lecture Notes in Electrical Engineering, vol 1023. Springer, Singapore.
https://doi.org/10.1007/978-981-99-0969-8_9, Publisher Name: Springer, Singapore, Print ISBN 978-981-99-0968-1,Online ISBN 978-981-99-0969-8
3. S. Jena, D. P. Mishra and S. Mishra, "Detection and Classification of Permanent Fault Using Multi-Layer Perceptron Model in a Distribution Network," 2023 IEEE 3rd International Conference on Smart
Technologies for Power, Energy and Control (STPEC), Bhubaneswar, India, 2023, pp. 1-6, doi: 10.1109/STPEC59253.2023.10431048. Date: 10th-13th December 2023 Electronic ISBN:979-8-3503-
0473-2, Physical presentation
4. Panda S.K., Ray P., Mishra D.P. (2021) A Study of Machine Learning Techniques in Short Term Load Forecasting Using ANN. In: Mishra D., Buyya R., Mohapatra P., Patnaik S. (eds) Intelligent and
Cloud Computing. Smart Innovation, Systems and Technologies, vol 194. Springer, Singapore. https://doi.org/10.1007/978-981-15-5971-6_6
5. M. A. R. Tilak, U. Subudh, D. P. Mishra, “Performance Analysis of Lead Acid Batteries with the Variation of Load Current and Temperature,” Advances in Smart Grid and Renewable Energy. ETAEERE
2020. Lecture Notes in Electrical Engineering, vol 691. Springer, Singapore., March 2020, pp. 15-23 https://doi.org/10.1007/978-981-15-7511-2_2
6. S. K. Panda, P. Ray, D. P. Mishra,“ A Study of Machine Learning Techniques in Short Term Load Forecasting Using ANN”, Intelligent and Cloud Computing. Smart Innovation, Systems and
Technologies, vol 194. Springer, Singapore. https://doi.org/10.1007/978-981-15-5971-6_6, Dec, 2019.pp.49-57
7. P. Mohanty, D. P. Mishra, A.Behera, Swati Swarupa Das, “Demonstration and Simulation of Brushless Direct Current Motor,” Advances in Energy Technology Proceedings of ICAET 2020, Jan. 2020, pp
79-89. Jan. 2020 pp.1-9
8. R. Mishra, D. P. Mishra, “Comparison of neural network models for weather forecasting,” Advances in Energy Technology Proceedings of ICAET 2020, Jan. 2020, pp. 79-89.
9. Papia Ray, D. P. Mishra, “Introduction to Condition Monitoring of Wide Area Monitoring (WAM) System,” Chapter 4 of the book Titled Soft Computing In Condition Monitoring And Diagnostics Of
Electrical And Mechanical Systems. (Springer) 2020, pp.71-89.(Springer S. K. Panda, P. Ray, D. P. Mishra, “A Review on ANN In Short Term Load Forecasting Using Artificial Intelligence Techniques”,
International Conference on Intelligent and cloud computing (ICICC-2019), to be held at ITER, SOA university, from 16-17 Dec, 2019. (Springer)
10. A. P. Hota, S. K. Mishra and D. P. Mishra,” Loss allocation strategies in active power distribution networks: A review, 1st international conference on advances in electrical control & signal systems
(AECSS-2019)” to be held at ITER, SOA, from Nov 8-9, 2019, (Springer)
11. A. P. Hota, S. K. Mishra and D. P. Mishra, “A new active power loss allocation method for radial distribution networks with DGs.” 1st international conference on advances in electrical control & signal
systems (AECSS-2019)” to be held at ITER, SOA, from Nov 8-9, 2019, (Springer)
12. S. K. Panda, P. Ray, D. P. Mishra, “Effectiveness of PSO On Short Term Load Forecasting,” 1st International Conference on Application of Robotics in Industry using Advanced Mechanisms, August,
16-17, 2019, GIFT, Bhubaneswar, India,PP. (Springer)
13. P. Ray, D. P. Mishra, “Analysis of EEG Signals for Emotion Recognition using Different Computational Intelligence Techniques”, Applications of Artificial Intelligence Techniques in Engineering.
SIGMA 2018, Volume 2. Advances in Intelligent Systems and Computing, vol 697. Springer, Singapore, pp 527-536 (Springer)
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