Fault Diagnosis of a High Voltage Transmission Line Using Waveform Matching Approach

International Journal on Soft Computing (IJSC) Vol.4, No.4, November 2013
DOI: 10.5121/ijsc.2013.4403 33
FAULT DIAGNOSIS OF A HIGH VOLTAGE
TRANSMISSION LINE USING WAVEFORM
MATCHING APPROACH
Ripunjoy Phukan1
, Rishab K Gupta2
, Sandeep Dadga3
and Ananthanaryan
Rathinam4
1,3,4
EEE Dept., SRM University, Kattankulathur, Chennai
2
ECE Dept., UT Dallas, Texas, USA
ABSTRACT
This paper is based on the problem of accurate fault diagnosis by incorporating a waveform matching
technique. Fault isolation and detection of a double circuit high voltage power transmission line is of
immense importance from point of view of Energy Management services. Power System Fault types namely
single line to ground faults, line to line faults, double line to ground faults etc. are responsible for
transients in current and voltage waveforms in Power Systems. Waveform matching deals with the
approximate superimposition of such waveforms in discretized versions obtained from recording devices
and Software respectively. The analogy derived from these waveforms is obtained as an error function of
voltage and current, from the considered metering devices. This assists in modelling the fault identification
as an optimization problem of minimizing the error between these sets of waveforms. In other words, it
utilizes the benefit of software discrepancies between these two waveforms. Analysis has been done using
the Bare Bones Particle Swarm Optimizer on an IEEE 2 bus, 6 bus and 14 bus system. The performance of
the algorithm has been compared with an analogous meta-heuristic algorithm called BAT optimization on
a 2 bus level. The primary focus of this paper is to demonstrate the efficiency of such methods and state the
common peculiarities in measurements, and the possible remedies for such distortions.
KEYWORDS
ATP, BBPSO, BAT, COMTRADE, Waveform Matching
1. INTRODUCTION
Distribution Systems use several intelligent electronic devices (IEDs) such as digital protection
devices, modern metering devices and Remote Terminal Units. These devices are generally
equipped with super-processors, which are capable of advanced computations within a shorter
time scale. With such advanced metrics and computation, faster and improved fault detection and
identification techniques can be investigated, for a possible implementation in real time scenario.
Several methods in the past detect a fault in the distribution system through conventional
approaches. For instance, a temporary interruption in power supply is dictated in terms of a power
system fault. Several, Maintenance personals are deployed all over the affected area, for
identification. Meanwhile, for an underground cable system, such facilities are rendered useless
and so switching operations were widely practiced to identify the faulted section. Thus, the
locating process is time consuming and the system becomes bulky. Due to these complications, a
number of automated fault location methods have been introduced for the process of fault
location. The fault methods for distribution networks are categorized as Impedance based

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methods and Other Fundamental Frequency Methods, High Frequency Components and
Travelling Wave Based Methods, Knowledge-Based Methods, Artificial Neural Networks,
Matching Approach, Hybrid methods, Wavelet transform and Magnetic field sensing coils. Quick
fault detection can help protect equipment through faster disconnection of faulted lines before any
significant cascaded damage is done. The reason behind a strategy for accurate fault location is to
assist in removing potential sites for persistent faults and locate areas where faults could regularly
occur, thus reducing the frequency and length of power outages. Hence, while many fault
diagnosis schemes have been developed in the past, a variety of algorithms continue to be
developed solely to perform this task more accurately and more effectively. Most faults in an
Electrical system occur within a network of overhead lines as they are highly susceptible to
vagaries of nature. More than 70% of the fault types belong to the genre of single-phase to ground
faults caused due to lightning induced transient high voltage or from falling trees. In the overhead
lines, tree contact caused by wind is a major cause for such faults along with double line to
ground faults.
Several papers have reported surveys on evolutionary algorithms (EAs) and their applications in
power systems [l]. Nevertheless, very few methods have been employed to solve the fault
diagnosis problem till date. They include, Expert Systems based Computational intelligence
techniques (Scientific Computation) such as, artificial neural networks (ANNs) [2] and genetic
algorithms (GA) [3]. As the objective function is usually a second-order polynomial, Genetic
Algorithm method has been employed to deal with such a problem [3]. Evolutionary
Programming excludes crossover operations and hence have a shorter run time when compared to
GA [3]. Faulted-section determination has been determined using model based reasoning in [4].
This calls for larger investments into protection models and knowledge engineering. Further
scientific review defines the solution for fault location using Artificial Neural Nets. Many
research groups have applied ANN [2], by using data from any one power line terminal, thus
reducing the amount of required information. Reference [5] uses Bayes Theorem and applies a
probabilistic model to the solution of a complex communication system.
A continuous escalation in the complexity, size, and reliability of modern industrial systems
necessitates an advanced development of the control and fault diagnosis theory and practice.
These requirements extend beyond normally accepted critical systems of the existing power
stations/grid. As it is obvious, the controlled system is the main part of the scheme, and it is
composed of actuators, process dynamics and sensors. Each of these parts is affected by several
unknown inputs/attenuation that can be perceived as process or measurement noise as well as
external disturbances acting on the system. When model-based control and diagnosis is utilized,
then the unknown input can also be extended by model uncertainty via Gaussian/random
operators, i.e., the mismatch between a model and the system being considered. The system could
also be affected by faults, which can be divided into three primary groups, i.e., actuator faults,
component (or process) faults, and sensor based faults, redefining the problem out of scope of this
paper. The role of the fault diagnosis portion is to conditionally monitor the system behaviour
and to provide all possible information regarding the abnormal functioning of its components. As
a result, the overall task of fault diagnosis consists of three subtasks: fault detection, isolation and
systemic updating. In the field of power system fault diagnosis both hybrid and conventional
methods are being used. In our work, waveform matching technique is used to identify the fault
type and fault location. Recent work on this method involves harmony search [14]. Advanced
metrics involve the use of Fuzzy ART Maps [17], FIRANN [18], Unsynchronized and non-
contact magnetic field measurements [19, 20].

35
2. PROBLEM FORMULATION
This paper utilizes the concept from Waveform matching and Evolutionary algorithms, and
creates an interactive approach to optimize the fault location. The software discrepancies further
add strength to the problem formulation. The subsequent section discusses the methods adopted.
2.1. Waveform Matching Technique
In general, Digital Fault Recorders (DFRs) or Smart Metering Devices can locate the fault site in
a SMART Grid. Nevertheless it is evident that such analysis is not always precise due to natural
disturbances in signals on account of attenuation in practical circuitry. This deviation from ideal
state can occur in the form of cross inductances of OH lines, Overhead capacitances (line to
ground or line to line), corona effects in co-axial cables, radio interference in the form of
frequency broadcasts, surge impedance loading and so on and so forth. This calls for optimizing
the fault location using Evolutionary Algorithms. In this paper Fault Diagnosis of a high-voltage
transmission line (HVTL) considers three major tasks, namely, fault-type identification, fault
distance location and fault resistance estimation. The diagnosis problem is formulated as a single
objective 3 dimensional optimization problem. The optimization decision variables involved in a
basic fault diagnosis problem, are fault length from the reference bus, ground resistance between
faulted line and earth, and fault nature. The expected variables pertaining to the original
waveform, are obtained from the DFRs placed at the receiving/sending end substation in a real
time scenario. Another waveform set is obtained from MATLAB simulations or the ISPEN tool
in real time simulations. The optimized variables are automatically readjusted as per the presented
algorithm, according to the condition that both these waveforms extracted simultaneously from
DFR/ATP and MATLAB simulations coincide i.e. superimposition of both waveforms. Hence,
the term Waveform Matching. The paper has been formulated using this relatively simple idea as
shown in figure 1 below.
Figure 1. Paper Idea organization

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2.2. Objective Function
The waveform matching is done between the actual waveforms derived from the DFR and the
expected waveforms produced from parallel simulation studies in MATLAB. A total of „M‟
points in the actual and the expected waveforms in the range [ts, tbr] are sampled, respectively. An
objective function (Error Function) that reflects the discrepancy between the actual and the
expected waveforms is given as follows.
Error function = = (1)
where i = A,B or C represents phase A, B or C, respectively;
j is the jth
sampling point ( j [ {1, 2, . . . , M}); Vi, j and Ii, j are the sample points of the during-
fault voltages and currents in three phases, respectively, which are obtained from DFR in an
actual fault scenario; Vj(X) I j(X), are the sample points of the during-fault voltage and current
waveforms. These are produced by simulation studies corresponding to a given fault hypothesis X
where X is the decision matrix to be optimized. Under Practical considerations, waveform
matching is possible through actual waveforms derived from an alternate measuring device. An
important point in fault diagnosis is taking into account the purpose and types of fault recording
devices (characteristic features). The objective function in (1) is designated as a minimization
function. The minimum value of f(x) is analogous to matched waveforms, hence the term
waveform matching.
3. ALTERNATE TRANSIENT PROGRAM (ATP)
Waveform matching is carried out by concurrently adjusting the waveforms from the recording
devices and the simulation. But, in our analysis, it is impossible to adopt a DFR due to cost
constraints and inflexibility in tuning the device. Instead of a DFR, ATP (Alternate Transient
Program) has been employed as a substitute. ATP is used purposefully create a fault in a bus
feeder and the waveforms of fault current and voltage are obtained with respect to time. These are
the actual waveforms that represent the DFR recordings replaced with a software approach. Since
ATP program is studies transient phenomenon through state space modelling, it is expected to
mimic the station recorders. ATP contains a feature called COMTRADE which is basically used
to transfer data sets between two Softwares. Expected waveforms are obtained from MATLAB
and waveform matching between the waveform sets is done via. COMTRADE file format
imported from ATP.
3.1. COMTRADE Format
COMTRADE (Common Format for Transient Data Exchange) is a file format created especially
for transient simulations. It comprises library files and data files that store instantaneous values
pertaining to a waveform. It is read and generated using GTPPLOT program. GTPPLOT is a
plotting program for processing .PL4 output files of ATP-DRAW simulation results and
converting their formats. It can be considered as a SMART platform to capture all data points
from voltage and current probes placed in the circuit. It is compiled in GNU FORTRAN
language, and makes use of the graphical package DISLIN. The ATP simulated data can be
exported as PL4, COMTRADE, Matlab, Math Cad and Mathematical files. Furthermore, the
program is used in calculations involving numerous Power Quality indices. Examples include
Fourier series representation, estimation of Generator turbine shaft loss or life etc. The captured
view of this program is as under.

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Figure 2. GTPPLOT Window
4. EA BASED OPTIMIZATION TECHNIQUES
In artificial intelligence, an evolutionary algorithm (EA) is a genre of evolutionary computation, a
generic population-oriented meta-heuristic optimization algorithm. An EA uses mechanisms
inspired by biological evolution/processes, such as reproduction, mutation, recombination,
and selection. Candidate solutions of the optimization problem play the role of individuals in an
interacting population, and the fitness function determines the environment/topography within
which the solutions "live". Evolution of the population then takes place after the repeated
application of the operators defined by mathematical interpretations. Evolutionary algorithms
often perform well, thereby approximating solutions of all types of problems based on the
underlying fitness landscape; this generality is shown by success in multitudes of fields such as
robotics, operations research, management, power systems, supply chain management, political
sciences, prediction, etc.
A large number of methods in meta-heuristics are constructed based on analogy of natural
phenomena, such as biological evolution (Genetic Algorithm: GA), bird flocking or fish
schooling (Particle Swarm Optimization: PSO) and behaviour of ants seeking a path (Ant Colony
Optimization: ACO). A vast majority of heuristic and meta-heuristic algorithms have been
derived from the behaviour of biological and/or physical systems in nature. For example, particle
swarm optimization was developed based on the flocking swarm behaviour of birds and fish,
while simulated annealing was based on the slow annealing process in metal fabrication. New
algorithms have also emerged recently, like the harmony search, firefly algorithm et al. The
former is inspired by the improvising process of composing a piece of music from strings, while
the latter was formulated based on the flashing behaviour of fireflies of South American
rainforest. Each of these algorithms have their own trade-offs. For example, simulating annealing
will guarantee to find the exact optimal solution if the cooling process is slow enough and the
simulation times are delayed.; however, the fine adjustment in certain constant parameters does
affect the convergence rate of the optimization process. A feature of meta-heuristics is to have
wide application range because information used in a search is only evaluation values. These
methods are expected as convenient and powerful framework for practical problems from
background of improvement of computer power nowadays.

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4.1. Bare Bones Particle Swarm
The Bare Bones swarm is conceptually the simplest of all variants of the PSO. Particle dynamics
are replaced with sampling in a predefined probability distribution (normal distribution). The
centre and dispersion of such a randomized allocation are set by the informers (local best
particles) rather than global best. The Bare Bones Particle Swarm Optimizer is selected out for
implementation in this paper because of its efficiency in search space exploitation. The
conventional PSO adaptively follows the global best and local best solutions in the trajectory
equation to define the solution space. Since it was later found that Gbest particles could lead to
premature convergence; here the local best particle dynamics is considered equally useful. The
algorithm is stated below,
• For each variable i=1……N do
o ; is the mean of the decision variable set
o ; is the standard deviation of the associated set
• For each dimension d=1…D do (Here D=3)
o ; normally distribute the new informers based on obtained
dimensional mean and standard deviation
• End for
• ; derive local best particle for that particular iteration and then repeat
4.2. BAT Algorithm
This is a meta-heuristic algorithm relying on echolocation by bats. The algorithm is designed
based on frequency of pulse emission, loudness and rate. In simulations, we use virtual bats
naturally. In order to define the rules how their positions „xi‟ and velocities „vi‟ in a d-dimensional
search space is updated, the following equations are used in a given time step, „t‟.
(2)
(3)
(4)
Where [0, 1] is a random number drawn from a uniform distribution. Here is the current
global best location (solution: gbest) which is identified after comparing all the solutions through
successive substitutions among all the „n‟ possible bats in the system. As the product of this
algorithm is a velocity increment, we it to adjust the velocity change while fixing the other factor,
depending on the type of the problem of interest. In this study, we will use fmin = 0 and fmax = 100,
i.e. the rate of emission of ultrasonic pulses, depending on the problem scope. Initially at the start
of the iterative procedure, each bat is randomly assigned a frequency which is drawn uniformly
from a random distribution of [fmin, fmax].
For the local search part, once a solution is selected from among the current best solutions, a new
solution for each bat is generated locally using random walk simulations goven by xnew = xold +
, where [−1, 1] is a random number, while At =< At and I >1, is the average loudness of all
the bats at this time step „t‟. The velocity and positional update of bats have some similarity to the
procedure in the standard particle swarm optimization as it essentially controls the pace and range
of the movement of the particles in a swarm.

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As the loudness of pulse emission usually decreases once a bat has found its prey, while the rate
of pulse emission increases, the loudness can be chosen as any value as per convenience. This
ensures the adaptive feature of PSO where the population rate of convergence slows down upon
detection of possible prey. For example, we can use A0 = 100 and Amin = 1. For simplicity, we can
also use A0 = 1 and Amin = 0, assuming Amin = 0 means that a bat has just found the prey and has
temporarily rendered UF sound = 0 decibels. Now we have the expression,
, (5)
where, α and are constants. In fact, „α‟ is similar to the cooling factor of a condensing schedule
in a simulated annealing process. For any 0 < α < 1 and > 0, we have
(6)
We can use = , and we have used = = 0.9 in our simulations.
5. METHODOLOGY
Figure 3. Schematic view of the proposed method
Actual (real time) waveforms are obtained from ATP-EMTP (Electromagnetic Transient
Program) software and simulated waveforms are obtained using MATLAB (R2008b version).
Parameters such as Voltage for PV buses, Load for PQ buses, transmission line parameters etc.
are same for both simulations. Simulation in ATP is done using ATP-DRAW software developed
by Bourneville Corporation. The circuitry is shown below for an IEEE 2 bus system between two
generators (U).

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Figure 4. A 2 bus Fault Circuit Diagram in ATP
The ATP circuit is then decisively simulated with an integration time of 0.00002 seconds and
maximum simulation time of 2 seconds. The fault triggering switches are then triggered under
dynamic conditions i.e. when the circuit is under operational conditions and steady state values
have seeped in. The switching interval timing is decided between 20 to 30 milliseconds i.e. the
switch ON time is 20msec and the switch OFF time is 30 ms. The current/default sampling rate in
ATP creates around 1668 data points (instantaneous values) between this time interval. This
represents the discretized version of the fault waveforms. The six waveforms needed for our
analysis are Va, Vb, Vc, (actual Voltages) Ia, Ib and Ic (actual Currents). COMTRADE data format
for these waveforms (Common Method of Transient Data Exchange) is obtained from
GTTPLOT, containing the individual data points in a structured format, along with other relevant
details. Now, a similar circuit is created in MATLAB Simulink R2008b version as shown in the
figure below, with similar parameters as its ATP counterpart.
Figure 5. An equivalent 2 bus Fault Circuit Diagram in MATLAB
The above circuit is then simulated with few modified configuration parameters.
The following modifications are made in Simulink structure;
 output times in the range of [0:.00006:.35]
 ode45 (Dormand-Prince) solver
This simulation is called parallel from the main program via. the inbuilt function, „set_param‟
function.

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Figure 6. Three Phase Fault Block View
The ground resistance set to a default constant value of 0.008 ohm, and the type parameters are
indicated by the fault type classification given in Table 5.1.
For 60 such cases simulated waveforms are obtained as shown in the Scope block.
Figure 7. Fault Current and Voltage Waveforms
Now the optimization algorithm is run in the form of an iterative process for 40 iterations. Here
the iteration count does not matter as the main aim of this analysis is to validate the effectiveness
of a software as a substitute. Initially we devote around 12-15 iterations to precisely indicate the
fault type and then the remaining 25-30 iterations to solve for the fault location and fault
resistance, with the obtained fault type. This allows for reduced memory requirements and also
sectionalizing the algorithm in two stages. The 3 dimensional co-ordinates then follow the search
motion meeting the Optimization rules (explained earlier) according to this best fitness function.
This updated matrix after the first iteration is now used to solve for the next iteration and so on.
The procedure follows suite and is based on successive substitutions. The algorithm performance
is escalated after dissecting the process into two sub-processes. This is because, after 15
iterations, all the elements of the third column meet at a point, i.e. the fault type. This is then used
to determine the nature of the fault occurring in the system. It can also be used in special cases

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where only the fault type is desired to be known and hence reliability analysis needs to be done
further on. The same algorithm is now run for a 2 dimensional problem as discussed above.
6. RESULTS AND DISCUSSION
It has been found that despite random initializations in the algorithm, the BBPSO gives near to an
exact location of the fault. Slight distortions are found in raw fault signals from ATP software due
to which, accurate match/superimposition is a problem for some of the waveforms. These
problems can be curtailed using Wavelet based de-noising algorithms applied to ATP waveforms.
6.1. Circuit Diagrams and Tabulation
Figure 8. A 6 bus Fault Circuit in ATP
Figure 9. A 6 bus Fault Circuit in Matlab
The above circuit diagrams represent equivalent 6 bus systems in ATP and MATLAB software
respectively. A line to line fault has been initiated in both the circuits. Each LCC block represents
a transmission line setting in ATP-DRAW.

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Table 1. Parameter setting for 2 bus circuit
Parameters Sending
End
Receiving End
Set Voltage 504 kV 502 kV
Set Source Resistance (0.5-1)Ω (0.5-1)Ω
Set Source Inductance 0.1-100 mH 0.1-100 mH
Set Positive Sequence Impedance 0.108+j0.293 0.108+j0.293
Set Zero Sequence Impedance 0.998+j1.295 0.998+j1.295
Set Positive Sequence Capacitance 0.0121µF/km 0.0129µF/km
Set Zero Sequence Capacitance 0.005 µF/km 0.005 µF/km
Table 2. Parameter setting for a 6 bus system
Parameters Generator Bus
Set Voltage 110 kV
Set Source Inductance 0.1 mH to 150 mH
Set Source Resistance 0.5 mΩ
Set Phase Angle -20 to 20
Parameters Load Bus
Set Real Power 20 Kw
Set Reactive Power 100 VAR
Table 3. Variable Declaration
Fault Type Variable
Single Line to Ground (A-Ground) 1
Single Line to Ground (B-Ground) 2
Single Line to Ground (C-Ground) 3
Line to Line (A-B) 4
Line to Line (B-C) 5
Line to Line (A-C) 6
Double Line to Ground (A-B-Ground) 7
Double Line to Ground (B-C-Ground) 8
Double Line to Ground (A-C-Ground) 9
Three phase Line to Line (A-B-C) 10
Three phase to Ground (A-B-C-Ground) 11
Table 4. Results for 2 bus system using BBPSO and BAT algorithm
Using BBPSO
Actual Values Diagnostic Values
Fault Type Length Fault Resistance Fault Type Length Fault Resistance
5 80 1.5 5 80 1.4245
7 30 1.4 7 29.2 1.37
Using BAT-Algorithm
6 80 1.3 6 80.65 1.45
2 40 1.5 2 39.96 1.0

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Figure 11. Fitness vs. iteration count
The convergence characteristics shown below indicate that Local Search (BBPSO) is a better
algorithm in terms of precision. Moreover, the presence of random rate of emissions, after every
iterations poses a problem as every time a random initialization produces a different fault
location. Such is not the case with Bare Bones, where the line constant to „x‟ axis indicates an
exact location. Computation times are severely affected in BAT optimization, hence, further
analysis has been focussed on BBPSO, in less time.
Table 5. Results for BBPSO
Transmission
line
Actual Values Diagnosis Values
Fault Type Length
Fault
Resistance Fault Type Length
Fault
Resistance
For 6 bus
1 7 40 1.5 7 39.53 1.4776
2 11 70 1.7 11 70.45 1.3257
3 9 10 1.2 9 9.65 1.621
4 5 95 1.7 5 93.3 1.652
5 11 15 1.9 11 15.73 1.9484
For 14 bus
1 1 40 1.5 1 40.03 1.56
2 4 60 1.2 4 59.34 1.25
The diagram for 14 bus is provided in Appendix. BBPSO tends to converge the solutions into a
better optimal condition with greater precision. Analysis showed that the length had converged
soon enough compared to fault resistance. The length had converged to the actual length specified
in ATP, i.e. 50km, in the 7th
iteration itself. It was found that initially the fault length from one of
the buses assumed as reference had converged to 56.780 km after 3rd
iteration. But the resistance
showed oscillations between 1.66 and 1.5. This conclusively states that fault resistance and length
are mutually responsible for accurate location algorithms and the process is dynamic in nature.
Waveforms have matched after gradual shifts as per iteration build up indicated in Figure 11. At
the end of the final iteration all the parameters corresponding to this matched/coinciding
waveform is the optimized fault setting that we are looking for.
Fitness
(error)
Epoch

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An unusual hindrance in our analysis was the occurrence of distortions and phase drifts due to
software make up. This problem has been clearly articulated in the set of figures. Such
phenomenon is seen due to different starting times for the two waveforms. Fluctuations in the
form of Gaussian noise in ATP simulations and overall d.c. component shift (momentary fault
currents) lead to inefficiency in analysis. This however is resolved via. trial and error setting of
source inductances, phase angle and fault resistance in matlab circuit.
Figure 12(a). Distortion in fault waveform due to high Inductance
Figure 12(b). Phase angle shifting in fault waveform
Figure 12(c). D.C Component mismatch due to momentary currents

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The range of values given in Table II and III, obtained via inspection is used in this analysis.
DFRs are accurately configured and so this problem does not arise in Real time situations. Noise
and drifts can be simply eliminated using circuit based filtering and pre-amplification. But
accurate configuration setting is needed prior to installemnt in area. Even in this analysis, the
problem in Figure 12(a) could be resolved through de-noising. But, fundamentally we are
interested in waveform matching of un-manipulated raw signals. The problem of phase shifts
occurs due to load variations and even point of origin of these waveforms. This can be assumed to
be in phase, and changes in source phase angle needs to be made in matlab, indicating sub-
transient and transient reactance drops with internal emfs. The presence of momentary currents in
Figure 12(c) cannot be circumvented, and so is the primary source of deviations in error function.
Figure 13. Accurate Waveform Matching
However, in most cases an accurate fault optimization would correspond to exact waveform
superimposition as shown in the figure above. It is under such optimized parameter settings that
the error function reduces to zero and hence, the term waveform matching.

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6.2. Performance Evaluation
Figure 14. Performance of BBPSO with variation in Agents and Fault indicating parameters.
The above three dimensional representation clearly states the relationship between the Root Mean
Square Error/Fitness function with number of system fault indicating parameters. It can be seen
that parameters such as;
 Voltage readings
 Current readings
 CB switching times
 Relay impedance measured through Megger circuits
 Phase angles of the sequence components
and many more systemic parameters can be obtained from metering equipment along with the
particle count used for running the computations. Bare Bones shows better performance under the
presence of lesser such fault indicating parameters stated above and higher particle/agent count.
This cuts down on expenses in procuring such measuring devices and assists in economic fault
diagnosis. As observed, system performance deteriorates with lesser agents and higher
parameters. This is obvious as more number of informers/local particles are needed to determine
the best local solutions. A major complication to the application of this particular algorithm in
real time scenarios is memory constraints, and work is currently underway with other algorithms
to replace the same.
Figure 15. Performance of BAT with variation in Agents and Fault indicating parameters.

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BAT algorithm performs well for any agent count and lesser number of fault indicating
parameters. This is obvious as the plot increases gradually from one end of the three D space and
performance issues are egregious at the higher system parameter state and lower agent count. So,
in terms of Power System Reliability studies, BBPSO is better compared to Bat algorithm.
6. CONCLUSION
Evolutionary Optimization Algorithms can be successfully implemented for accurate fault
diagnosis in a double circuit HVTL. It has been proven that waveform matching approach is an
effective tool for identifying fault parameters such as length, fault type and fault resistance. This
is justified as captured waveforms from two variants superimpose over one another at the
optimized parameter setting conditions, when the error function is minimum. The software
proposed (Alternate Transient Program) has been used successfully to mimic the real time
behaviour of Digital Fault Recorders (DFRs) in substations. The results were analysed for an
IEEE 2 bus, 6 bus and 14 bus system using BBPSO algorithm. As far as Power System Reliability
is concerned, Bare Bones Particle Swarm Optimization can be used, as local best solutions
(informers) give a precise trajectory towards the minimal value of error, compared to global best
solutions of BAT as is evident from the convergence characteristics. As observed, waveform
distortions of Gaussian nature and phase angle drifts lead to misleading results in some of the
transmission lines (especially lines between two adjacent load buses), conditional tuning of the
source inductance and phase angle settings is found to be a pre-requisite for such systems to
counter the effects of software mismatches. This technique will be able to facilitate in the creation
a knowledge base to have the same technique of waveform matching using evolutionary
algorithms run successfully on power grids using expert systems. Furthermore, as per fault
resistance values, the accurate relay setting of impedance based relays can be tuned for fault
isolation/clearance. The underlying future scope of the project will be incorporation of this
approach in real life macro power grid systems using better metering equipments and
supercomputers. Though time consuming, it will assist in the creation of an adaptive look up table
as a reference medium for TSOs to act during crisis. Furthermore, work is underway with
application of Neigh Block thresholding schemes and Stein‟s unbiased risk estimator for
denoizing these waveforms and obtaining accurate matching. This will solve the problem of
tuning sources as per requirement.
APPENDIX

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Figure 16. ATP and MATLAB 14 bus system
ACKNOWLEDGEMENTS
The authors would like to thank SRM University and other faculty members from EEE Dept. of
SRM University for their support and encouragement. Special thanks to Head of Department, Dr.
S.S.Dash and M.Tech faculty for giving necessary inputs in this project.
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Author
Ripunjoy Phukan received his B.Tech degree in Electrical and Electronics
Engineering from SRM University, Chennai in 2013.Presently, he is pursuing a
research internship at Indian Institute of Technology, Guwahati working on PHEV
integration into Power Grids. His research interest include Power Systems
Optimization, Intelligent control of systems, Condition monitoring in insulation
systems.
A Rathinam received his Master‟s degree from Annamalai University, Chennai, in
1997. Presently he is an assistant professor at SRM University, EEE department. His
research interests include Power systems Optimization, Signals & systems
Multidimensional systems, Microprocessors, Intelligent Controllers, Soft computing
& evolutionary methods.
Rishab K Gupta received his B.Tech degree in Electrical and Electronics Engineering from SRM
University, Chennai in 2013. He is currently pursuing graduate studies in Power Systems at University of
Texas, Dallas.
Sandeep Dadga received his B.Tech degree in Electrical and Electronics Engineering from SRM
University, Chennai in 2013. His research interests revolve around IT based solutions in Engineering.

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