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Fault Location Algorithms for
Electrical Power Transmission
Lines
Methodology, Design and Testing
Master of Science Thesis
Shreya Parmar
Intelligent Electrical Power Grids - EWI
Fault Location Algorithms for Electrical
Power Transmission Lines
Methodology, Design and Testing
Master of Science Thesis
For the degree of Master of Science in Electrical Power
Engineering at Delft University of Technology
Shreya Parmar
July 8th, 2015
Faculty of Electrical Engineering, Mathematics and Computer Science (EWI) · Delft
University of Technology
Delft University of Technology
Department of
Electrical Engineering
The undersigned hereby certify that they have read and recommend to the Faculty of
Electrical Engineering, Mathematics and Computer Science (EWI) for acceptance of a
thesis entitled
Fault Location Algorithms for Electrical Power Transmission Lines
by
Shreya Parmar
in partial fulfillment of the requirements for the degree of Master of Science
Electrical Power Engineering
Dated: July 8th, 2015
Thesis Commitee :
Dr. ir Marjan Popov
Dr. ir A. Rodrigo Mor
Dr. ir D. Jeltsema
Dr. ir Gert Rietveld
c Electrical Engineering
Copyright All rights reserved.
Abstract
Electric power transmission lines are the veins which pump life into the modern day world,
delivering electricity to consumers at their homes, offices and industries. It is important
to ensure a smooth operation of transmission lines to deliver a minimally interrupted
power supply making necessary for reliable operation of electrical power lines. This need
has given rise to Fault Location detection techniques so that the economic impact of the
fault situations can be mitigated and their correction can be rendered simpler and precise.
Over the years, much research has been done on the various techniques for accurate fault
location detection. Many methods use line data from one or more terminals for determining
the fault locations. Providing a certain degree of accuracy, these methods lose out on
the ease of detection as they may use line parameters extensively which are sometimes
difficult to assess. To counter these problems, some techniques of fault location detection
were developed on the basis of the available voltage and current measurements across the
terminals of the faulted lines. These methods minimize the effects of parameters due to
varying load and weather conditions.
This report presents a comparison between two different approaches towards fault location
detection with and without applying transmission line parameters. Firstly, an impedance
based parameter dependent algorithm, derived by using the Modal Transformation theory
and Discrete Fourier Transform is presented. The methodology has the ability to locate
the fault whether it is on an overhead line or an underground power cable. The second
algorithm is an improved parameter-independent fault location method that only uses time
synchronized measurements. The unknown fault location will be determined from voltage
and current phasors, measured at both line end terminals. This approach of fault detection
renders the prior knowledge of line parameters as obsolete, which is of great assistance to
grid technicians and engineers.
The thesis report presents the results of the algorithms tested through the use of EMTP/ATPDraw and RTDS/RSCAD v2.025. The results of the line parameter-independent
algorithm will be compared with those provided by the parameter-dependent algorithm.
Master of Science Thesis
Shreya Parmar
ii
Both algorithms are tested for the most common to occur asymmetrical faults with varying
fault resistances. The results of the tests are then related to gauge the effectiveness and
accuracy of these algorithms.
Shreya Parmar
Master of Science Thesis
Table of Contents
Preface
ix
Acknowledgments
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1 Introduction
1-1 Importance of Fault Location . . . . . . . . . . . . . . . . . . . . . . . . . .
1-2 Thesis Overview and Objectives . . . . . . . . . . . . . . . . . . . . . . . . .
2 Fault Location Techniques
2-1 Fault Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2-2 Fault locators and Relays . . . . . . . . . . . . . . . . . . . . . . .
2-2-1 Fault Locators . . . . . . . . . . . . . . . . . . . . . . . . .
2-2-2 Fault Locators vs Relays . . . . . . . . . . . . . . . . . . . .
2-2-3 Distance Relays . . . . . . . . . . . . . . . . . . . . . . . .
2-3 Fault Location Algorithms . . . . . . . . . . . . . . . . . . . . . . .
2-3-1 Impedance based Algorithms . . . . . . . . . . . . . . . . .
2-3-2 Travelling Wave based Fault Location Algorithms . . . . . . .
2-3-3 Knowledge based Fault Location Algorithms . . . . . . . . .
2-3-4 High Frequency component based Fault Location Algorithms .
2-3-5 Synchronous Sampling in Fault Location Algorithms . . . . .
2-3-6 Time and Frequency Domain Analysis . . . . . . . . . . . . .
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3 Transmission Line Fault Location Algorithms
3-1 Line Parameter Dependent Algorithm . . . . . . . . . . . . . . . . . . . . . .
3-2 Line Parameter Independent or Parameterless Algorithm . . . . . . . . . . . .
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Table of Contents
4 Modelling Methodologies
4-1 Network Modelling . . . . .
4-2 Source Modelling . . . . . .
4-3 Transmission Line Modelling
4-4 Fault Control Modelling . .
4-5 FLA Block . . . . . . . . .
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5 Simulation Results for different fault types
5-1 EMTP Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5-2 RTDS Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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6 Conclusions
6-1 Result Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6-2 Future Work and suggestions . . . . . . . . . . . . . . . . . . . . . . . . . .
6-3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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A Simulation softwares used
A-1 MATLAB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A-2 EMTP/ATPDraw . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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A-3 RTDS/RSCAD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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B Symmetrical components and Sequence Networks
B-1 Conversion Between the Phase and Symmetrical Component Domains . . . . .
B-2 Sequence Networks for Faults . . . . . . . . . . . . . . . . . . . . . . . . . .
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C Phasor Measurement Units
C-1 Basics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
C-2 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
C-3 Placement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Glossary
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Master of Science Thesis
List of Figures
1-1 Basic layout of power transmission lines. Courtesy: Institute for Energy Research
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2-1
2-2
2-3
2-4
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2-6
Classification of fault types in electrical power systems. . . . . . . .
Single line to ground Fault . . . . . . . . . . . . . . . . . . . . . .
Line to Line Fault . . . . . . . . . . . . . . . . . . . . . . . . . .
Double line to ground Fault . . . . . . . . . . . . . . . . . . . . .
An example of protections zones offered by a typical distance relay.
Types of Fault Location Algorithms . . . . . . . . . . . . . . . . .
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3-1
3-2
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Three phase representation of a faulted line . . . . . . . . . . .
Symmetrical Components Analysis for unbalanced faults . . . .
Sequence circuits of the three phase faulted line respectively. . .
Flowchart depicting approach towards fault location algorithms
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4-1
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EMTP-ATPDraw model used for simulation tests. . . . . . . . . . . . . . .
RTDS-RSCAD model used for simulation tests. . . . . . . . . . . . . . . .
EMTP-ATPDraw and RTDS-RSCAD source model used for simulation tests.
EMTP-ATPDraw and RTDS-RSCAD line model used for simulation tests. .
EMTP fault fault control switch used for simulation tests. . . . . . . . . .
RTDS-RSCAD fault control block used for simulation tests. . . . . . . . . .
EMTP algorithm MODEL used for FLA implementation. . . . . . . . . . .
RSCAD CBuilder used for FLA implementation. . . . . . . . . . . . . . . .
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5-1 Single line diagram for FLA simulation tests. . . . . . . . . . . . . . . . . . .
5-2 Simulation waveforms for SLG fault. . . . . . . . . . . . . . . . . . . . . . .
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List of Figures
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A-1 ATPDraw User Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A-2 RSCAD Power system user library . . . . . . . . . . . . . . . . . . . . . . .
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B-1 Asymmetrical fault sequence network. . . . . . . . . . . . . . . . . . . . . .
B-2 Three phase fault sequence network. . . . . . . . . . . . . . . . . . . . . . .
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C-1 PMU placement in power systems
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Simulation
Simulation
Simulation
Simulation
Simulation
Simulation
Simulation
Simulation
Shreya Parmar
results for SLG fault in EMTP
waveforms for LL fault. . . .
results for LL fault in EMTP
waveforms for LLG fault. . .
results for LLG fault in EMTP
results for SLG fault in RTDS
results for LL fault in RTDS .
results Parameter independent
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for LLG fault.
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Master of Science Thesis
List of Tables
2-1 Statistics for fault types on transmission lines as per the Nordic grid Report 2013. 6
2-2 Artificial Intelligence used in Power Systems . . . . . . . . . . . . . . . . . . 14
2-3 Comparison of Frequency and Time domain Analysis . . . . . . . . . . . . . 16
5-1 Network Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5-2 Line parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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5-3 Simulation result for SLG Fault - EMTP/ATP 60 kms . . . . . . . . . . . . .
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5-4 Simulation result for SLG Fault - EMTP/ATP 100kms . . . . . . . . . . . . .
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5-5 Simulation result for LL Fault - EMTP/ATP 60kms . . . . . . . . . . . . . .
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5-6 Simulation result for LL Fault - EMTP/ATP 100kms . . . . . . . . . . . . . .
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5-7 Simulation result for LLG Fault - EMTP/ATP 60kms . . . . . . . . . . . . . .
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5-8 Simulation result for LLG Fault - EMTP/ATP 100kms . . . . . . . . . . . . .
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5-9 Simulation result for SLG Fault - RTDS 100kms . . . . . . . . . . . . . . . .
5-10 Simulation result for LL Fault - RTDS 100kms . . . . . . . . . . . . . . . . .
5-11 Simulation result for LLG Fault - RTDS 100kms . . . . . . . . . . . . . . . .
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viii
Shreya Parmar
List of Tables
Master of Science Thesis
Preface
The purpose of this thesis is to examine the application of impedance-based fault location
methods for transmission lines using phasor measurements and test them in well known
transient analysis softwares, so that their effectiveness can be assured. Different types of
faults are applied to a transmission line model to analyze the accuracy of the two algorithms
used for fault location detection. The first algorithm is based on the Telegrapher’s equation.
This algorithm is dependent on the line parameters used and hence, the thesis project
explores a second fault location algorithm which is line parameter independent and has
the capability to locate faults on transmission lines, without requiring any line parameters
and just the voltage and current measurements at the line end terminals.
The report begins with a discussion on the importance of fault location algorithms in its
introduction in Chapter 1. Then Chapter 2 reviews the basics of fault analysis and deliberates over the commonly used approaches to fault location detection. This chapter revises
the popularly used relay protection schemes and how they can be deficient in accurate and
quick fault location detection. A short description on the basics of the different types of
fault location methodologies is provided to acquaint the readers with the developments
and research in the vast topic of fault location algorithms.
Chapter 3 develops two impedance based algorithms used in this project and gives a detailed derivation for both. The first algorithm uses line impedances and critical measurements at the line end terminals, where as the second algorithm uses only line terminal
measurements. The simulation modelling and methodologies are elaborated in Chapter 4
citing the investigation established with practical use of the EMTP, RTDS and MATLAB
simulation platforms.
A transmission line model in a two source network is simulated on EMTP-ATP and RTDS
and confirmed with MATLAB to validate the effectiveness of fault location estimations in
Chapter 5. The results are included for a line length of 60 and 100 kilometers respectively
for the most common asymmetrical fault types, accounting for effects of fault resistances.
Lastly, Chapter 6 gives a conclusion to the report, as the analysis and summary are identified here. This chapter also discusses the future research to utilize the results from this
thesis project.
Master of Science Thesis
Shreya Parmar
x
Shreya Parmar
Preface
Master of Science Thesis
Acknowledgments
I would like to extend my sincere thanks to my advisor, Dr. Dipl.-Ing. M. Popov, for his
assistance, guidance and encouragement throughout my research.
I take this opportunity to express gratitude to all of the members of my committee, Dr.
ir A. Rodrigo Mor, Dr. ir Gert Rietveld from VSL and Dr. ir D. Jeltsema for their
recommendations.
This thesis is dedicated to my parents and all my friends whom I had the immense pleasure
of knowing during my graduate study at TU Delft. Special thanks goes out to Anuj Shah,
who was my rock throughout my Master study at Delft.
Delft, University of Technology
July 8th, 2015
Master of Science Thesis
Shreya Parmar
Shreya Parmar
xii
Shreya Parmar
Acknowledgments
Master of Science Thesis
“What is a soul? It’s like electricity - we don’t really know what it is, but it’s
a force that can light a room. ”
— Ray Charles
Chapter 1
Introduction
Overhead transmission lines are the most convenient means for transportation of electrical
energy from sources of generation to load centers so as to result in energy use and consumption. A pictorial representation of their use is depicted in Figure 1-1. Deregulation
of the electricity market, economic and environmental requirements have pushed electrical
utilities to operate transmission lines close to their maximum limits. Smooth operation of
electric power transmission lines is essential to deliver minimally interrupted power supply to consumers, who have become more and more sensitive to power outages with the
growth in worldwide technology. This necessitates reliable operation of power equipment
and satisfaction of consumers. Engineers are hence pushed to design transmission networks that formulate power system protection schemes to reliably detect and isolate faults
compromising the security of the system.
Figure 1-1: Basic layout of power transmission lines. Courtesy: Institute for Energy Research
Transmission and distribution lines experience faults that are caused by nature such as
storms, lightning, snow, rain, insulation breakdown and short circuit faults caused by
birds, tree branches and other external objects. In most cases, electrical faults manifest
themselves as mechanical damage, which must be repaired before returning the line to
Master of Science Thesis
Shreya Parmar
2
Introduction
service. Any fault, if not detected and isolated quickly will thus, grow into a system wide
disturbance causing widespread outages and even subsequent blackouts.
When a fault occurs on a power system, financial losses can be reduced and the line service
can be maintained if the location of the fault can be accurately determined, especially when
generation, transmission and distribution occurs over a longer distance or area, thereby
improving the security and quality of the energy supply.
Electric utility services can thus be maintained if the location of the fault on a line can be
accurately determined. These deductions and suitability factors make fault locators and
their algorithms, a critical tool in maintaining the competence of smart grids as is also
mentioned by Kezunovic in [1].
1-1
Importance of Fault Location
Transmission and distribution lines experience both temporary and permanent faults. Temporary faults are mostly self cleared and permanent faults can be detected and mitigated
with the help of traditionally available protective relay equipments. When a line is taken
out of service because of faults, the connected loads are not supplied and sometimes, other
lines are forced to supply the loads of the faulted line. It is also possible that a series of
cascading trips of a protective relay occurs, faulting successively larger parts of the system,
resulting in large power system blackouts. An example of such a situation is the recent
blackout in The Netherlands’ northern grid, leading to no power in almost 1 million Dutch
households [2].
Restoration of power supply after permanent faults can be done only after the responsible
maintenance team finishes the repair of the damage caused by the fault. For this purpose,
the fault position has to be known, otherwise the whole line has to be inspected to find
the damage origin. This task becomes even more tedious, if high voltage transmission
lines, running upto hundreds of kilometers are considered. Underground lines and cables
have to be uncovered from under the ground, requiring more manpower and machines,
and in populated areas, roads and passageways have to be blocked and dug. Thus, it is
important that the location of a fault is either known or can be estimated with a reasonable
accuracy. This allows saving of both money and time for the inspection and repair work,
and aids towards better service by utilities due to the possibility of faster restoration of
power supply.
1-2
Thesis Overview and Objectives
In conjunction with the topics discussed in the preceding sections, fault location algorithms
(FLAs) for transmission line faults form the backbone of this thesis project. The motivation
of this project is to detect and determine the location of various types of faults on a
Shreya Parmar
Master of Science Thesis
1-2 Thesis Overview and Objectives
3
transmission line model, while considering both accuracy and speed. The main aim of the
thesis is to implement the algorithms in EMTP and real time simulator RTDS and test for
compelling results.
There are various methods of fault detection, and this report focuses on impedance based
fault location techniques. The thesis report aims to first familiarize the reader with the
basics of fault analysis for electric power transmission lines, using available research from
the past years. The successive chapters present a review on the most commonly and
widely used fault location techniques for line protection. Thereafter, the report focuses
on two fault location algorithms which use measurements made from line end terminals
to locate fault distances . A detailed design, methodology and simulation tests for the
algorithms are presented. Lastly, a comparison is made based on the simulation results
of the two algorithms. The simulation studies are based on the EMTP/ATPDraw [3] and
RTDS [4] platforms. Conclusions based on the results are discussed, along with the factors
affecting the accuracy of the algorithms. The author also suggests future work for the
research of fault location techniques and their simple integration into Phasor Measurement
Units (PMU) and Synchronized Measurement Technology (SMT) technologies to make the
electric utilities and grid system smarter and more reliable.
Master of Science Thesis
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Shreya Parmar
Introduction
Master of Science Thesis
Chapter 2
Fault Location Techniques
This chapter presents the basics of fault analysis and fault locators. Distance relays are
analyzed with respect to their effectiveness in fault location detection and a comparison
is made with conventional fault locators using numerical algorithms. A comprehensive
review of all of the different methods of fault location on power systems is presented in this
chapter. The advantages and disadvantages of each method of fault location detection and
techniques for deriving them is discussed, as is the need for special measures to ensure the
accuracy of the results obtained from the fault locators under certain network topologies
or fault conditions.
2-1
Fault Analysis
Figure 2-1: Classification of fault types in electrical power systems.
Master of Science Thesis
Shreya Parmar
6
Fault Location Techniques
A fault is an interruption to the normal flow of current in a circuit. Large currents flow
across the lines in a faulted condition, which results not only in financial losses to the
suppliers and inconvenience to the customers, but also in severe cases, a complete shutdown of the grid supply.
According to the Nordic Grid report of the year 2013 and the data presented in IEEE
journal [5], the most common to occur type of fault in electrical transmission lines remains
to be the line to ground fault, but the most severe faults are still of three phase nature.
The Table 2-1 summarizes the same:
Table 2-1: Statistics for fault types on transmission lines as per the Nordic grid Report 2013.
Type of fault
Nature
Percentage occurrence
Single Line-to-ground–SLG
unbalanced
85%
Line-to-Line–LL
unbalanced
8%
Double Line-to-ground–LLG
unbalanced
5%
Triple Line–LLL
balanced
2%
There are broadly two types of faults in transmission lines – transient and permanent faults,
as is shown in Figure 2-1. A transient fault is no longer present if power is disconnected
for a short time and then restored. Many faults in overhead power lines are transient in
nature and power system protection devices operate to isolate the area of the fault, clear
the fault and then the power-line can be returned to service. Typical examples of transient
faults include:
• momentary tree, bird or animal contact
• lightning strike
• conductor clashes
A permanent fault can cause lasting damage to the transmission lines. To counter a
permanent fault, the line first has to be isolated and then correction has to be made to the
line. Some examples of the fault of permanent nature are:
• direct lightning stroke on line
• man-made damage
• mechanical damage due to environment and age
Shreya Parmar
Master of Science Thesis
2-1 Fault Analysis
7
A symmetrical or balanced fault on a line affects each of its three phases equally. In
transmission line faults, roughly 3-5% are symmetric in nature as seen in Table 2-1. This
is in contrast to an asymmetrical or unbalanced fault, where the three phases are not
affected equally. Common types of asymmetric faults, and their causes are:
• line-to-ground fault (Figure 2-2) - a short circuit between one line and ground, often
caused by physical contact, for example due to lightning or other storm damage.
Figure 2-2: Single line to ground Fault
• line-to-line fault (Figure 2-3) - a short circuit between lines, caused by ionization of
air, or when lines come into physical contact, for example due to a broken insulator.
Figure 2-3: Line to Line Fault
• double line-to-ground fault (Figure 2-4) - two lines come into contact with the
ground and each other, commonly due to storm damage.
Master of Science Thesis
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Fault Location Techniques
Figure 2-4: Double line to ground Fault
Shreya Parmar
Master of Science Thesis
2-2 Fault locators and Relays
2-2
2-2-1
9
Fault locators and Relays
Fault Locators
A fault locator is a system designed for locating a fault with the highest possibly accuracy.
It mainly augments the protection equipment, which applies the fault-location algorithms
for estimating the distance to a fault and can be used for fault type identification and fault
impedance calculations as well. Their implementation can be as stand-alone devices or as
a part of a microprocessor relay, or in a offline fault analysis programs. The analysis and
computation segments of the fault locators form the basis of its fault location and detection
technique. This objective is achieved by analyzing the critical information provided to the
fault locator with the help of mathematical manipulation and deriving an accurate result
based on a plethora of fundamental techniques suitable to the system and resources being
used. Thus, fault location algorithms are employed in the fault locators, which use various
techniques to arrive at the single goal of locating a fault on the line from a reference, with
consistent accuracy and ease.
Fault locators when used with conventional protection schemes usually use the line data
from typical recording equipments like digital relays and Intelligent Electronic Devices.
Fault analysis is usually established with the bus phase and line voltage and current data,
but it can also utilize information on circuit breaker status.
2-2-2
Fault Locators vs Relays
Formulated to provide protection from faults in the transmission lines, there exists some
important differences between fault locators using algorithms and the conventional fault
location by relays. The points highlighted below are vital to the growth of fault location
techniques and fault locators, as they highlight the insufficient nature of the conventional
line protection devices and relays.
• Accuracy – Protective relays usually define a general area known as a protective
zone where the fault might have occurred. Fault locators pinpoint these areas with
a certain percentage of error, if any.
• Speed – Protective relays require very high speed operations to mitigate the spreading of faulted current to other parts of the power network, making use of circuit
breakers and high speed communication devices, sometimes, sacrificing the relay system security and selectivity. Fault locators on the other hand use algorithms that
calculate fault locations in several seconds, if not in minutes.
• Data window – Relays use a fault interval between the inception of the fault to the
clearing by a breaker, which takes several fundamental frequency cycles, resulting in a
wide data window. Fault locators use the most compatible data windows to minimize
Master of Science Thesis
Shreya Parmar
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Fault Location Techniques
the scope of errors in calculations. However, this is of a considerable importance as
it may affect the results of the algorithm.
• Complexity of calculations – The high speed operation of protective relays render
the calculations to be simpler. Fault locators do not posses any limitations of complexity. They can be relatively simple (based on impedance of line, or travelling wave
methods) and may even increase in their complexity to incorporate a more versatile
operation (use of neural networks and fuzzy logic, use of PMUs etc).
2-2-3
Distance Relays
This section explains the basic working principles of a distance relay. Distance relays are
most closely related to fault locators. They work to locate faults occurring in particular
protective zones, as shown in Figure 2-5, making them the most common form of protection
of transmission lines.
Figure 2-5: An example of protections zones offered by a typical distance relay.
The distance relay operation depends upon the predetermined value of voltage to current
ratio which is impedance. The relay will only operate when this ratio becomes less than
its predetermined value or impedance. Distance relays are placed in a particular zone
and usually communicate with the main system and other relays using the pilot relaying
scheme.
As the impedance of a transmission line is directly proportional to its length, it can be concluded that a distance relay can only operate if fault has occurred within a predetermined
distance. Distance relays are most useful for reasonable line lengths (upto 20 kms or so)
because their operating characteristics are based on the line parameters which proves to
be a major flaw, owing to the capricious nature of line parameters.
Shreya Parmar
Master of Science Thesis
2-3 Fault Location Algorithms
2-3
11
Fault Location Algorithms
Traditional line fault detection used to heavily rely on visual inspections of the faulted line
parts resulting in long and tedious foot or aerial patrols. These methods were expensive
and prone to more errors. Thus, the shift to automatic fault locators was not only desired,
but also natural.
Fault location techniques can be generally classified into the following main categories:
• based on fundamental-frequency currents and voltages, mainly on impedance measurement
• based on traveling-wave phenomenon
• knowledge-based approaches
• based on high-frequency components of currents and voltages generated by faults
The most widely used FLAs can be split into two main groups: impedance based and
travelling-wave based. Impedance-based algorithms make use of the line parameters (such
as resistance, inductance and conductance per unit length, and the line length) as well as
voltage and current data from one or more line terminals to calculate the distance to the
fault from a reference point or line terminal. Travelling-wave based algorithms utilize the
theory that waves travel along a line from a fault at the speed of light to calculate the
distance to a fault from a reference point and timing wave reaching the line terminal.
More details on the types of fault location detection techniques are presented in the subsequent sections.
Figure 2-6: Types of Fault Location Algorithms
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Fault Location Techniques
2-3-1
Impedance based Algorithms
Impedance-based FLAs calculate fault distance using the per unit length impedance of the
line, voltage and current data, and circuit analysis techniques, such as Kirchhoff’s voltage
and current laws. Single-terminal and two-terminal algorithms are the two main groups of
impedance based FLAs .
Single-terminal FLA (STFLA), often cited as the first and earliest class of FLA use voltage
and current data from one end of the transmission line only. They determine the fault
distance by calculating the impedance of the transmission line as seen from one line terminal
and then using the line parameters to convert that value into a distance measurement. To
implement this, a microprocessor-based relay or other measuring device can be used to
measure the voltages and currents at the local line terminal and then produce an estimate
of the fault distance based on those measurements. The main advantage of using STFLA
is that no communication of data is needed between line terminals. They are simpler to
construct and the results require less computations making them fast.
Two-terminal FLAs (TTFLA) tend to be more accurate since they are unaffected by the
resistance of the fault or other factors that reduce the precision of STFLAs as shown in [6],
[7] and [8]. The positive-sequence components can be used as opposed to zero-sequence
components, which is advantageous as zero-sequence components can be harder to assess.
TTFLA utilize similar methods from STFLA. Some disadvantages of using TTFLAs are
as listed below:
• require a means of gathering the data from line terminals at two locations before it
can be analyzed.
• takes longer to locate the fault due to their complexity.
• implementation requires more measuring devices, communication channels and data
storage devices.
Impedance based fault location algorithms, unfortunately require very accurate line parameters and sequence impedance data. Line parameters also tend to vary with fault
conditions and this tends to distort the accuracy of this particular implementation.
2-3-2
Travelling Wave based Fault Location Algorithms
Travelling-waves occur after faults, switching, or lightning strikes. When a fault occurs
along a transmission line, the voltage and current transients will travel towards the line
terminals. By wave reflection theory, these transients will continue to bounce back and
forth between the fault point and the two terminals for the faulted line until the postfault steady state is reached. Since the travelling-waves move along the transmission line
at the speed of light, by accurately measuring the time taken for the travelling-wave to
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Master of Science Thesis
2-3 Fault Location Algorithms
13
propagate to the line terminals, the distance to the fault can be effectively found. An
advantage of this method over impedance-based techniques is persistence to pre-fault line
loading, fault and grounding resistance. Disadvantage of Travelling-wave Fault Location
Algorithms (TWFLAs) is that they cannot be used on transmission corridors consisting of
overhead lines and underground cables as the surge impedance changes drastically between
them, resulting in large inaccuracies in the location of the fault. The accuracy of TWFLAs
can be affected by errors in detection of the waves. Strong buses on the power system
network influencing the voltage and current waveforms due to line impedances can reduce
the amplitude of voltage waves making them harder to detect, and thus reducing the
accuracy of the FLA.
Single ended TWFLA use wave sensors at one line terminal as it does not require any
form of communication between the line terminals, where as two-ended TWFLAs work by
recording the exact time that the transient travelling wave from the fault reaches each line
terminal where accurate timing is usually achieved by using a GPS system. Two-ended
TWFLAss are known to be more accurate than their single-terminal counterparts and do
not require as much signal processing at the sensors since they do not have to correlate the
reflected waves to their original waves as they work by simple timing. However, two-ended
TWFLAs are more expensive than single-ended TWFLAs because of their requirement for
a communications link and synchronization between the two line terminals. This communications link and its inherent problems also make two-ended TWFLAs less reliable and
less robust than single-ended TWFLAs. A high sampling time for computations and expensive implementations add to the overall demerits of using TWFLAs. More information
on travelling wave based FLAs can be found in [9], [10] and a combined Impedance based
and travelling wave based fault location is established in [11].
2-3-3
Knowledge based Fault Location Algorithms
Uncertainty of line parameter affecting variables, such as length of cables and unknown
fault resistance, coupled with the complex structure of distribution management systems
tends to make fault location through impedance and travelling wave techniques inaccurate.
As a result of this, knowledge-based technique for locating faults have receiving attention
from researchers in the last few years. In general, the technique requires information such
as substation and distribution switch status, line measurements, atmospheric conditions,
and information provided by fault detection devices installed along the distribution feeders.
This information is analyzed using artificial intelligence methods to locate a fault.
The three major knowledge based techniques used in power systems are based on the
following:
• Expert Sytem Techniques (XPS)– mainly used in power-system automation and
control to decipher off-line tasks such as settings coordination, post–fault analysis
and fault–section identification.
Master of Science Thesis
Shreya Parmar
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Fault Location Techniques
• Artificial Neural Networks (ANN) – well known for solving many engineering
problems related to classification and optimization. Its ability to recognize complex
patterns has made it possible to use in locating a fault where the training input can
be from measurement data such as voltage, current, status of circuit breaker and
feeder and the target output is the location of fault.
• Fuzzy Logic systems (FL) – based on the theory of fuzzy sets that was developed
for a domain in which definitions of activities and observations are fuzzy, or not
well-described, without sharp boundaries.
A table summarizing the key differences in the knowledge-based fault location methods
used in power system is given in Table 2-2.
Table 2-2: Artificial Intelligence used in Power Systems
Approach
Feature
XPS
Knowledge Used
expert level
ANN
FL
information
information
extracted
extracted
difficult
convenient
Troubleshooting and
changes required,
improving a relay
but possible
Self learning
possible
possible
possible
Robustness
easy
difficult
easy
Integration with a relay
convenient
difficult
convenient
Computations
extensive
dedicated hardware
moderate
required
2-3-4
High Frequency component based Fault Location Algorithms
High-frequency transient signals generated in the range of Hz to kHz due to fault conditions
can be applied to achieve high accuracy in fault location as shown in the work established
in [12], [13]. This method of fault location detection, based on the high frequency voltage
and current components, has been shown to be immune to power frequency phenomena
such as power swings and current transformer saturations.
This method mainly uses the fault-generated high-frequency signals, negating the problem
of identifying multiple reflections of the travelling wave from bus-bars and the fault point,
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Master of Science Thesis
2-3 Fault Location Algorithms
15
as is seen in the travelling wave based FLAs in Section 2-3-2. Problems associated with
fault-inception angle are addressed as the high-frequency signals associated with the fault
arc do not vary with the point on the wave at which the fault occurs. In the scheme
described in [13], a high speed sampling system is used to capture the fault-generated
high-frequency transients.
The high frequency components are used to identify the path of the fault (line or ground)
and then compute the fault location along the identified path based on the power-frequency
signals. The multi-phase transient signals are first decomposed into modal components.
Then, the modal signals are decomposed into their wavelet components and the corresponding wavelet coefficients are obtained. These wavelet coefficients are used to extract
the relevant signal features, which are subsequently used to identify the branch or path
where the fault is located. Finally, the fault distance from the main substation is calculated
using the information provided by the power-frequency signal.
This technique for fault location detection is not widely used as the method is considered
expensive and complex, since use of specially tuned filters for measuring high-frequency
components is required.
2-3-5
Synchronous Sampling in Fault Location Algorithms
With decrease in hardware costs and increase in the use of microprocessors in computations, newer and more accurate algorithms that utilize voltage and current from both the
ends of the line are being developed. Digitization of power system components has made
way for digitization of critical bus data such as bus voltages and line currents samples.
Hence, synchronized sampling which refers to sampling of various parameters of the system synchronously, is gaining popularity as a means of transmission line data acquisition.
Time synchronization allows synchronized real-time measurements of multiple remote measurement points on the grid and the algorithm uses the SMT which is an emerging concept in power system engineering [14]. Synchronized measurements makes the algorithm
settings-free or less dependent on the line parameters. PMUs are considered to be the most
suitable platform to implement SMT for protection of transmission lines as the paper [15]
discusses the same.
Advantages of using FLAs based on synchronized data sampling are:
• they are not sensitive to changes in loading or environmental conditions.
• they are not affected by conditions on other parts of the power system.
• they are not affected by the fault resistance, mutual coupling in parallel lines.
If synchronization devices are not available during measurements, the same can be established by detecting the phase shift in the time sampling of the data and then collectively
applying the difference between the standard values to achieve a homogeneous result.
Master of Science Thesis
Shreya Parmar
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Fault Location Techniques
2-3-6
Time and Frequency Domain Analysis
The development of all the fault location types and techniques mentioned in the Section
2-3 is usually established in two domains– time or frequency. Both domains add their own
merits and demerits into the overall analysis of fault location, as can be seen in Table 2-3
below.
Table 2-3: Comparison of Frequency and Time domain Analysis
Frequency Domain
Use of Fourier Transforms or wavelet
Time Domain
Use of Differential equations.
transforms.
Uses only fundamental phasors.
Uses TL parameters.
Can be affected by DC decay.
Unaffected by DC decay or changes
( although FFT corrects this )
in power system frequencies.
Slower time convergence.
Faster time convergence,
but requires more processing power.
However, this thesis project focuses on use of phasors for fault location detection. Hence,
the frequency or spectral domain is used for analysis here. Majority of power system
control devices are based on phasors. In an AC system, voltage, current and other fault
loop parameters can be represented as sampled waveforms or sinusoids. These waveforms
are represented in the frequency domain as phasors. Since a sinusoidal signal waveform
can easily be represented as a phasor, Fourier Analysis can be implemented for phasor
extraction.
Colloquial factors that can affect the accuracy of fault location estimation which are common to all techniques of fault location detection can be summarized as the following:
• Inaccurate line model
• Affects of fault resistance and fault arcs
• Uniform line impedance assumption
• Neglecting pre-fault load flow and power balance
• Neglecting fault inception angles
• Measurement and sampling errors
• Noise in line due to disturbances or auxiliary instruments
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Master of Science Thesis
2-3 Fault Location Algorithms
17
Taking account of the points mentioned above, we see that despite the advances in communication, processing and storage technology, many reasons still exist which contribute
to failure of fault protection and detection devices. The need of smarter and more efficient
fault locators, banking on lesser line specifications is noticeable.
Master of Science Thesis
Shreya Parmar
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Shreya Parmar
Fault Location Techniques
Master of Science Thesis
Chapter 3
Transmission Line Fault Location
Algorithms
Over the course of time, various fault location algorithms have been developed for estimating fault location with different techniques. The fault locater used to determine the
fault on the transmission lines comprises of many of the approaches discussed in Chapter
2, Section 2-3. This chapter derives the impedance based fault location algorithms tested
in this thesis report, one being dependent on line parameters and line end terminal measurements and the second, independent of the line parameters and dependent only on the
line terminal measurements. Both algorithms use only the fundamental phasors of the line
voltages and currents sampled at each end of the transmission line. They are designed
with respect to locating all fault types, including balanced three-phase faults.
3-1
Line Parameter Dependent Algorithm
Overhead transmission lines and underground cables are commonly found in densely populated cities. The line parameter dependent algorithm uses the Telegrapher’s equations to
translate voltages and currents on an electrical transmission line and define them with respect to distance and time. The solution to Telegrapher’s equations is converted to a three
phase solution using Clarck’s Modal transformation as the original set of phase variables
are converted into a set of 0, α and β variables. This algorithm uses the work established
in E. Sayed’s paper in [16] and by Z. Radojevic in [17].
Consider the three phase network in Figure 3-1, where a fault F occurs from one of the
line’s phase to the ground. D is the total length of the transmission line whereas ` is
the distance at which a fault F occurs from the sending end terminal S of the network.
Master of Science Thesis
Shreya Parmar
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Transmission Line Fault Location Algorithms
Figure 3-1: Three phase representation of a faulted line
The same fault point can located at a distance (D − `) when seen from the receiving end
terminal R of the line.
For a single phase transmission line, Telegrapher’s equations are given as:
∂I
∂V
+L
= −RI
∂x
∂t
∂V
∂I
C
+
= −GV
∂t
∂x
(3-1)
(3-2)
where V , I, R, L, C and G are the voltage, current, resistance, inductance, capacitance
and conductance of the line and the conductors respectively. The propagation constant γ
and characteristic impedance of the line zc are:
zc =
q
γ=
q
(R + jωL)/(G + jωC),
((R + jωL)(G + jωC)).
From the solution of the telegraph equations in Equation 3-1, the sending end voltages and
currents VS , IS can be described as :


Vx 


Ix



cosh(γ(D − x))
−zc sinh(γ(D − x)) Vs 
=

 
− sinh(γ(D − x))/zc
cosh(γ(D − x))
Is
(3-3)
where D is the total length of the line and x is any point on the line. Same approach is
used to obtain the receiving end voltages and currents VR , IR .
Shreya Parmar
Master of Science Thesis
3-1 Line Parameter Dependent Algorithm
21
When a fault occurs ` km away from the sending end, by making use of above equations,
the distance to the fault can be determined by:
`=
1
tanh−1 (A/B)
γ
(3-4)
Here, A and B are given as:
A = VS cosh(γD) − zc IS sinh(γD) − VR
B = IR zc − VS sinh(γD) − zc IS cosh(γD)
The application of this algorithm for three phase systems, as shown in Figure 3-1, can
be done by Modal decomposition of the solutions in Equation 3-4. By exercising the
Clarke’s transformation, the single-phase solution can be extended to a stationary threephase reference frame as:


 V0 
 
 
Vα 
 
 
Vβ


1


2



1
1  Va 
 
 
=
 
−1 −1 
  Vb 
√
√  
0
3 − 3 Vc
and


 I0 
 
 
Iα 
 
 

=
Iβ

1


2


1

1  Ia 




 
−1 −1 
  Ib 
√
√  
0
3 − 3 Ic
Hence, the distance to fault becomes:
`0,α,β =
1
tanh−1 (Ai /Bi )
γi
(3-5)
where i= 0, α and β. Consequently,
`0 is the ground mode, `α and `β are the two areal
q
√
modes, γi = zi Yi , zC,i = zi /Yi and
Ai = VS,i cosh(γi D) − zc,i ISi sinh(γi D) − VR,i
Bi = IR,i zc,i + VS,i sinh(γi D) − zc,i IS,i cosh(γi D)
(3-6)
(3-7)
Accurate fault location can be selected by the appropriate mode and the fault type in
equation 3-5. `α is valid for all types of faults except line-line fault where the `β is selected.
As can be noticed from the Equations 3-5 and 3-6, the fault location ` is dependent on the
line parameters of the transmission line undergoing the faults.
Master of Science Thesis
Shreya Parmar
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3-2
Transmission Line Fault Location Algorithms
Line Parameter Independent or Parameterless Algorithm
Understanding the ambiguous nature of line parameters, an algorithm which is capable of
accurately determining the location of any fault on the transmission line without needing
these parameters, becomes more flexible, reliable and user friendly. The parameterless
algorithm in the paper [18] by Z. Radojevic and G. Preston uses only the fundamental
phasors of the line voltages and currents measurements sampled at each end of the transmission line. Discreet Fourier Transform is used in the algorithm to transform the samples
in frequency domain and then symmetrical components are applied to arrive at the correct fault location on a transmission line. The algorithm presented in this report is only
applicable to common unbalanced faults mention in Section 2-1.
An asymmetric or unbalanced faults can be easily analyzed with the use of symmetrical
components, where decomposition of the unbalanced system into a sequence of balanced
networks is done. Except for the balanced three-phase fault, all faults result in an unbalanced system. The key idea of symmetrical component analysis is to decompose the
unbalanced system into three sequences of balanced networks. The networks are then
coupled only at the point of the unbalance, that is, at the fault.
Figure 3-2: Symmetrical Components Analysis for unbalanced faults
Assuming three unbalanced voltage or current phasors, it is possible to represent each
Shreya Parmar
Master of Science Thesis
3-2 Line Parameter Independent or Parameterless Algorithm
23
phasor quantity. The symmetrical components shown in the Figure 3-2 can be described
as:
• Positive Sequence Components:
Three phasors, equal in magnitude, displaced by 120◦ in phase, have the same sequence as the original phasors (abc).
• Negative Sequence Components:
Three phasors, equal in magnitude, displaced by 120◦ in phase, have the opposite
sequence as the original phasors (acb).
• Zero Sequence Components:
Three phasors, equal in magnitude, have the same phase shift.
Thus, by using the symmetrical components technique, the positive, negative and zero sequence symmetrical components of the voltages and currents from the sending and receiving
end of the transmission line can be determined from the voltage and current phasors.
In the three phase network represented in Figure 3-1, the equivalent positive and negative
sequence circuits can be shown as in the figures below.
(a) Positive sequence circuit
(b) Negative sequence circuit
Figure 3-3: Sequence circuits of the three phase faulted line respectively.
Using Kirchoffs laws, from the Figure 3-3, we get the following equations:
VpS − z`IpS = VpR − z(D − `)IpR
VnS − z`InS = VnR − z(D − `)InR
(3-8)
(3-9)
p,n
p,n
where Vp,n
and Ip,n
R , are positive and negative sequence voltages and currents
S , VR , IS
for sending and receiving ends respectively and z is the positive (and negative) sequence
line impedance.
Solving the Equations 3-8, we get the total line impedance as a representation of the voltage
and current phasors measured at the line end terminals:
(VpS − VpR )InR − (VnS − VnR )IpR
IpR InR − InS IpR
(VpS − VpR )InS − (VnS − VnR )IpS
z(D − `) =
IpR InR − InS IpR
z` =
Master of Science Thesis
(3-10)
(3-11)
Shreya Parmar
24
Transmission Line Fault Location Algorithms
To remove the influence of line impedance in the fault distance calculations, the distance
to the fault, `, can now be expressed as a percentage of the line length D as:
`% =
`
∗ 100
D
We can also represent this as:
`% =
z`
∗ 100
z` + z(D − `)
(3-12)
Rearranging Equations 3-10 and 3-12, we get the final solution as:
(VpS − VpR )InR − (VnS − VnR )IpR
`% =
∗ 100
(VpS − VpR )(InS + InR ) − (VnS − VnR )(IpS + IpR )
(3-13)
As can be seen from Equation 3-13, the fault location uses only the symmetrical components
of the measured current and voltage phasors from the sending and receiving end of the
transmission network and not the line impedance for its result. Though line parameters
are used in initial decoupling of the faulted network, it can be negated and only critical
data measurements are used to arrive at the final result. Thus, a parameter independent
fault location algorithm is developed.
This method to locate fault on a transmission line, highlights the use of SMT, which can
be implemented most easily using PMUs in an actual grid system to receive voltage and
current samples from measuring and recording devices installed at each end of the line.
The presence of negative–sequence components in the calculations can be used to signify an
asymmetrical fault. This information can also be used to effectively differentiate between
the fault types.
In summary, the fault location algorithms described in this chapter follow the basic flow as
is represented in Figure 3-4. Both algorithms extract the voltage and current values from
the line end terminals during simulations. Thereafter, the waveforms undergo sampling
and phasor extraction (as shown in blocks in Figures 4-7 and 4-8 of Chapter 4), done with
the use of DFT to convert the samples to a phasor domain. This is in coherence with a
practical and real measuring system where the output of instrument transformers is always
with respect to continuous time, where as the recording of signals is done in discrete time.
Modal transformation and symmetrical transformation is applied to the sampled phasors in
each respective algorithm and the final result is found as a representation of fault distance
in terms of kilometers using the sending end terminal as reference.
Shreya Parmar
Master of Science Thesis
3-2 Line Parameter Independent or Parameterless Algorithm
25
Figure 3-4: Flowchart depicting approach towards fault location algorithms
Master of Science Thesis
Shreya Parmar
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Shreya Parmar
Transmission Line Fault Location Algorithms
Master of Science Thesis
Chapter 4
Modelling Methodologies
The design of the power system models for this project was done with the intent of testing
and implementation of the algorithms in different simulation environments. This chapter describes the models adopted for the simulation tests. The different line parameter
modelling will be discussed, along with the line fault control design. The implementation
of the algorithms mentioned in the Chapter 3 are actualized in both EMTP and RTDS
environment softwares and the method to fulfill the same is discussed in this chapter.
4-1
Network Modelling
The network consists of the high voltage buses fed by two AC sources of 416 KV rms line
to line at the sending end and 400 KV rms line to line, at the receiving end. The basic
layout for the simulation test model is as shown in Figures 4-1 and Figure 4-2.
The two source network is driven by the AC voltages with a phase difference between them.
The RTDS network model also has two purely resistive loads attached to it, to damp out
transients due to fault currents in the terminal voltage and current waveforms.
4-2
Source Modelling
A source model is often used to represent some portion of the power system in a simplified
way. It usually generates a 3 phase power, power system frequency, sine wave behind
an internal impedance. The RSCAD source model can be configured to use impedance
format or discrete RL values whereas the ATPDraw model required an external connection
for source impedance values. Both models are as shown in Figure 4-3.
Master of Science Thesis
Shreya Parmar
28
Modelling Methodologies
Figure 4-1: EMTP-ATPDraw model used for simulation tests.
Figure 4-2: RTDS-RSCAD model used for simulation tests.
Shreya Parmar
Master of Science Thesis
4-3 Transmission Line Modelling
29
Figure 4-3: EMTP-ATPDraw and RTDS-RSCAD source model used for simulation tests.
4-3
Transmission Line Modelling
The design of a transmission line depends on four electrical parameters:
• Series resistance
• Series inductance
• Shunt capacitance
• Shunt conductance
The series resistance relies basically on the physical composition of the conductor at a given
temperature. The series inductance and shunt capacitance are produced by the presence
of magnetic and electric fields around the conductors, and depend on their geometrical
arrangement. The shunt conductance is due to leakage currents flowing across insulators
and air. As leakage current is considerably small compared to nominal current, it is usually
neglected, and therefore, shunt conductance is normally not considered for the transmission
line modeling. The arrangement of the parameters representing the line depends upon the
length of the line which defines a short-length line for lengths is less than 80 km and a
medium length line for lengths between 80 km and 240 km. Lines longer than this range
are considered to be long length lines. Both short and medium-length transmission lines
use approximated lumped-parameter models and same idea was used for line modelling
in the test simulations. The line parameters were modelled using Lumped parameters in
EMTP and a faulted SHARC transmission line model in RTDS.
The line model used in EMTP is the lumped parameter model. One block model was used
to represent the sending end and another for the receiving end, making their total line
length equal to the line length of the simulated transmission line. The R–L–C π-equivalent
LINEPI3S and LINEZT–3 models were used for test simulations.
Master of Science Thesis
Shreya Parmar
30
Modelling Methodologies
Figure 4-4: EMTP-ATPDraw and RTDS-RSCAD line model used for simulation tests.
The TLINE program, a member of the RSCAD software tools is used to lay out and define
the geometry and parameters of an N conductor travelling wave transmission line. The
output of the TLINE program is used by DRAFT when a case involving the line is compiled.
A default TLINE model was used for the modelling in RSCAD, which in theory, should not
affect the algorithms’ accuracy, at least for the Parameter Independent one. Later, the line
parameters were specified as in Table 5-2. Three conductor bergeron faulted transmission
line model as shown in Figure 4-4 was used. Line lengths were modified using a Draft
variable slider to simulate faults at different lengths of the transmission line model.
4-4
Fault Control Modelling
The transmission line faults were simulated by the use of separate single phase switches or
a single three phase switch in the EMTP model. ATPDraw supports most of the switch
type elements and a time controlled design which can be used to monitor the fault current
and voltages at the fault point in the line, was used. The specifications are TSWITCH for
single or 3-phase time controlled switching and SWIT–3XT which is a three-phase time
controlled switch operating independent of the phases.
The fault branch connected to the bus in figure 4-2 were used to create faults at that bus
in the RTDS simulations. The fault branch consists of a switch whose open resistance is
very large and whose closed resistance was specified at different values. The type of fault,
line–ground or line–line, is specified in the fault component CONFIGURATION menu.
For a SLG fault type, any phase can be selected and the model will include a fault branch
in the L–G PARAMETERS menu. Fault initiation and removal was established by using
control components utilizing an arrangement of a pulse generator control function and a
push–button. The pulse width specified for the pulse generator function determines the
fault duration. The length which is being faulted and the fault resistance can be adjusted
with the help of interactive sliders. These were directly used in the RunTime window.
Shreya Parmar
Master of Science Thesis
4-4 Fault Control Modelling
31
Figure 4-5: EMTP fault fault control switch used for simulation tests.
Figure 4-6: RTDS-RSCAD fault control block used for simulation tests.
Master of Science Thesis
Shreya Parmar
32
4-5
Modelling Methodologies
FLA Block
The fault location algorithm was coded in the MODELS simulation language in ATP.
ATPDraw supports only a simplified usage of MODELS. The algorithms were written in
the model–file and ATPDraw applied INPUT/OUTPUT section of MODELS along the
block as instructed in the code file. As shown in the Figure 4-7, the critical values of
voltages and currents from the line end terminals were extracted via splitter connections
and fed into the MODEL block. In this block, the algorithms were implemented, including
the sampling and phase transformations.
Figure 4-7: EMTP algorithm MODEL used for FLA implementation.
In RTDS simulations, the algorithms discussed in Chapter 3 are applied to the network
model using a User Defined Component (UDC) block, known as the CBuilder. RSCAD uses
CBuilder software module for the FLAs making use of the control system type components,
compiled via the GPC Processor Cards available with the RTDS. The node voltages and
line currents were extracted with node point and sampled in UDC blocks. Then these
samples were exported to the algorithm implementation block as shown in Figure 4-8
where the final calculations were done.
Both parameter dependent and parameter independent algorithms were used the FLA
blocks described above to extract the voltage and current waveforms from the sending and
receiving line end terminals. The extracted values were then sampled, and processed via
the hard coding of the CBuilder blocks and the output was set as the fault location, which
was as estimated by each algorithm.
Shreya Parmar
Master of Science Thesis
4-5 FLA Block
33
(a) Calculation block for Parameter Independent(b) Calculation block for Parameter dependent algorithm.
algorithm.
Figure 4-8: RSCAD CBuilder used for FLA implementation.
Master of Science Thesis
Shreya Parmar
34
Modelling Methodologies
Shreya Parmar
Master of Science Thesis
Chapter 5
Simulation Results for different fault
types
In this chapter, the simulation results for the two fault location algorithms presented in the
previous sections is exhibited. The algorithm tests were carried out by simulation analysis
using the ATPDraw and RSCAD v2.025 software on the RTDS for a total line length of
60 km and 100 km. All calculations were established by taking the sending end of the line
as reference.
The schematic of the transmission line shown in Figure 5-1 is used to represent the basic
modelling for the power system network, as explained in Chapter 4 previously. US and
UR represent the active AC power sources. z` and z(D − `) are the sending and receiving
end impedances with respect to fault distance ` respectively. The algorithms calculate the
fault distance ` from the sending side as reference using the fundamental phasors of the
line voltage and currents extracted using DFT, as is already explained in Figure 3-4.
Figure 5-1: Single line diagram for FLA simulation tests.
Master of Science Thesis
Shreya Parmar
36
Simulation Results for different fault types
The network parameters used for the simulation tests were common and are given in Table
5-1 and the line constants used for the simulations, are as in Table 5-2.
Table 5-1: Network Parameters
Parameters
Sending End US
Receiving End UR
ULL,rms (KV)
416
400
φ (◦ )
0
-20
R (Ω)
1.0185892
0.6366183
L (H)
0.0509295
0.0318309
R0 (Ω)
2.0371785
1.2732366
L0 (H)
0.1018589
0.0636618
Table 5-2: Line parameters
Parameters
p-n sequence
zero sequence
R (Ω/km)
0.065
0.195
L (mH/km)
0.95493
2.86479
C (µ/km)
0.008688
0.004762
Z (Ω/km)
0.3317+j0.41634
0.1972+j0.3699
Transmission line faults were simulated at different locations along the line with the use
of a 3 phase controlled switch (in ATPDraw modelling) and a fault control logic (in
RTDS/RSCAD modelling) as shown in Section 4. Fault was set to occur at 40 ms in
all simulation cases with respect to the total simulation time of 200 ms. Percentage error,
which is represented for the parameter dependent algorithm as PDE and the the parameter
independent or parameterless algorithm as PLE in the simulation results were calculated
using:
(actual value − calculated value)
error percentage(%) = 100 ∗
actual value
Shreya Parmar
Master of Science Thesis
5-1 EMTP Simulation Results
5-1
37
EMTP Simulation Results
In EMTP simulations, the faults were set to occur at 40 ms. It was assumed that the
line was loaded before the fault inception. The sampling frequency was fs = 25 kHz. The
data window size was 20 ms. This corresponds to N = 500 samples per data window. It is
assumed the synchronization error is equal to 0 degrees.
The simulation results are tabulated where the results obtained by both algorithms are as
shown. The plots for fault location at various distances are given, where all calculations
have been done for Rf = 0.01Ω.
Sending end Voltage (kV)
Receiving end Voltage (kV)
400
400
phase A
phase B
phase C
200
0
0
−200
−200
−400
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
phase A
phase B
phase C
200
−400
0
0.02
0.04
0.06
0.08
t (s)
Sending end Current (kA)
0.12
0.14
0.16
0.18
Receiving end Current (kA)
40
20
phase A
phase B
phase C
20
0
−20
−10
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
phase A
phase B
phase C
10
0
−40
0.1
t (s)
−20
0
0.02
0.04
0.06
0.08
t (s)
0.1
0.12
0.14
t (s)
Figure 5-2: Simulation waveforms for SLG fault.
The results for an SLG fault with different Rf is tabulated as below, for a transmission
line of 60 kms.
Table 5-3: Simulation result for SLG Fault - EMTP/ATP 60 kms
Actual
Rf = 0.01Ω
Fault (km)
P DE% P LE%
P DE%
10
14.488
1.838
14.329
1.755
14.352
2.695
14.342
0.447
20
4.535
4.345
4.525
4.345
-16.78
4.65
4.495
4.99
30
1.223
4.007
1.226
4.04
1.22
4.42
1.22
5.63
40
-0.402
3.272
-0.402
3.312
-0.402
3.765
-0.402
5.4
50
-1.318
2.428
-1.322
2.466
-1.322
2.99
-1.322
4.842
Master of Science Thesis
Rf = 1.0Ω
Rf = 10.0Ω
P LE% P DE%
P LE%
Rf = 50.0Ω
P DE% P LE%
Shreya Parmar
0.16
0.18
38
Simulation Results for different fault types
50
Fault
Fault
Fault
Fault
Fault
Fault Distance calculated ( km )
45
40
at
at
at
at
at
50
40
30
20
10
km
km
km
km
km
35
30
25
20
15
10
5
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
t (s)
90
Fault
Fault
Fault
Fault
Fault
Fault
Fault
Fault
Fault
80
Fault Distance calculated( km )
70
at
at
at
at
at
at
at
at
at
90km
80km
70km
60km
50km
40km
30km
20km
10km
60
50
40
30
20
10
0
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
t (s)
Figure 5-3: Simulation results for SLG faults in a 60 km and 100 km transmission line model
with parameter independent algorithm in EMTP simulations.
Shreya Parmar
Master of Science Thesis
5-1 EMTP Simulation Results
39
Table 5-4: Simulation result for SLG Fault - EMTP/ATP 100kms
Actual Fault
Rf = 0.01Ω
Rf = 1.0Ω
Rf = 10.0Ω
(km)
P DE% P LE%
P DE%
10
88.046
0.615
16.112
0.267
16.145
0.96
16.614
1.702
20
6.58
3.6
6.485
3.46
6.505
3.54
6.495
4.045
30
3.316
3.734
3.28
3.686
3.29
3.8
3.283
4.467
40
1.69
3.717
1.69
3.465
1.682
3.627
1.68
4.435
50
0.718
3.094
0.72
3.11
0.718
3.304
0.718
4.224
60
0.221
2.678
0.55
2.7
0.08
2.9167
0.08
3.918
70
-0.37
2.154
-0.372
2.262
-0.372
2.497
-0.372
3.558
80
-0.696
1.797
-0.701
1.812
-0.702
2.065
-0.702
3.166
90
9.831
1.341
-0.92
1.354
-0.926
1.638
-0.926
2.756
P LE% P DE%
P LE% P DE% P LE%
Sending end Voltage (kV)
Receiving end Voltage (kV)
400
400
phase A
phase B
phase C
300
200
100
phase A
phase B
phase C
300
200
100
0
0
−100
−100
−200
−200
−300
−400
Rf = 50.0Ω
−300
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
−400
0
0.02
0.04
0.06
t (s)
0.08
0.1
0.12
0.14
0.16
0.18
t (s)
Sending end Current (kA)
Receiving end Current (kA)
100
15
phase A
phase B
phase C
50
phase A
phase B
phase C
10
5
0
0
−5
−50
−10
−100
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
−15
0
0.02
t (s)
0.04
0.06
0.08
0.1
0.12
0.14
t (s)
Figure 5-4: Simulation waveforms for LL fault.
Master of Science Thesis
Shreya Parmar
0.16
0.18
40
Simulation Results for different fault types
Table 5-5: Simulation result for LL Fault - EMTP/ATP 60kms
Actual Fault
P DE%
P LE%
10
16.518
-3.19
15
12.4
-0.133
20
9.78
0.495
25
7.8
1.364
30
6.18
1.563
35
4.791
1.622
40
3.582
1.67
45
2.522
1.655
50
1.606
1.674
55
1.529
1.630
(km)
Shreya Parmar
Master of Science Thesis
5-1 EMTP Simulation Results
41
Table 5-6: Simulation result for LL Fault - EMTP/ATP 100kms
Actual Fault
P DE%
P LE%
10
19.184
-4.04
15
18.868
-0.906
20
13.05
-0.435
25
11.376
0.684
30
10.03
0.926
35
8.88
1.051
40
7.862
1.117
45
6.937
1.146
50
6.086
1.154
55
5.292
1.147
60
4.55
1.131
65
3.850
1.187
70
3.191
1.078
75
2.569
1.045
80
1.985
1.01
85
1.44
0.972
90
0.94
0.937
(km)
Table 5-7: Simulation result for LLG Fault - EMTP/ATP 60kms
Actual Fault
Rf = 0.01Ω
(km)
P DE% P LE%
Rf = 1.0Ω
P DE%
Rf = 10.0Ω
P LE% P DE%
Rf = 50.0Ω
P LE% P DE% P LE%
10
13.90
-7.54
14.113
-9.25
14.332
-19.44
14.333
-19.94
20
4.315
-2.505
4.49
-3.22
4.49
-8.3
4.46
-8.3
30
1.217
-1.1
1.22
-1.47
1.22
-4.703
1.21
-4.703
40
-0.402
-0.12
-0.395
-0.372
-0.402
-2.75
-0.4
-2.75
50
-1.276
0.702
-1.32
0.436
-1.322
-1.406
-1.32
-1.406
Master of Science Thesis
Shreya Parmar
42
Simulation Results for different fault types
55
Fault
Fault
Fault
Fault
Fault
50
Fault Distance calculated( km )
45
at
at
at
at
at
50km
40km
30km
20km
10km
40
35
30
25
20
15
10
5
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
t (s)
150
Fault Distance calculated( km )
Fault
Fault
Fault
Fault
Fault
Fault
Fault
Fault
Fault
at
at
at
at
at
at
at
at
at
90km
80km
70km
60km
50km
40km
30km
20km
10km
100
50
0
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
t (s)
Figure 5-5: Simulation results for LL faults in a 60 km and 100 km transmission line model
with parameter independent and dependent algorithms in EMTP simulations.
Shreya Parmar
Master of Science Thesis
5-1 EMTP Simulation Results
43
Sending end Voltage (kV)
Receiving end Voltage (kV)
400
400
phase A
phase B
phase C
300
200
100
200
100
0
0
−100
−100
−200
−200
−300
−400
phase A
phase B
phase C
300
−300
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
−400
0
0.02
0.04
0.06
t (s)
0.08
0.1
0.12
0.14
0.16
0.18
t (s)
Sending end Current (kA)
Receiving end Current (kA)
20
30
phase A
phase B
phase C
15
10
phase A
phase B
phase C
20
10
5
0
0
−10
−5
−20
−10
−30
−15
−20
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
−40
0
0.02
0.04
0.06
t (s)
0.08
0.1
0.12
0.14
0.16
t (s)
Figure 5-6: Simulation waveforms for LLG fault.
Table 5-8: Simulation result for LLG Fault - EMTP/ATP 100kms
Actual Fault
Rf = 0.01Ω
(km)
P DE% P LE%
P DE%
10
15.573
-4.95
16.112
-4.87
16.118
-4.96
16.116
-5.04
20
6.48
-0.215
6.485
-0.335
6.49
-0.37
6.49
4.535
30
3.283
0.583
3.28
0.613
3.28
0.606
3.25
0.5034
40
1.68
0.827
1.678
0.955
1.678
0.948
1.66
0.86
50
0.716
1.074
0.71
1.092
0.718
1.086
0.71
1.01
60
0.08
1.121
0.08
1.13
0.078
1.125
0.541
1.063
70
-0.374
1.113
-0.361
1.124
-0.373
1.107
-0.368
1.062
80
-0.702
1.065
-0.702
1.073
-0.693
1.053
-0.695
1.027
90
-0.926
0.989
-0.926
0.88
-0.926
0.977
-0.915
0.968
Master of Science Thesis
Rf = 1.0Ω
Rf = 10.0Ω
P LE% P DE%
Rf = 50.0Ω
P LE% P DE% P LE%
Shreya Parmar
0.18
44
Simulation Results for different fault types
100
Fault
Fault
Fault
Fault
Fault
Fault
Fault
Fault
Fault
90
Fault Distance calculated( km )
80
70
at
at
at
at
at
at
at
at
at
90km
80km
70km
60km
50km
40km
30km
20km
10km
60
50
40
30
20
10
0
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
t (s)
100
Fault
Fault
Fault
Fault
Fault
Fault
Fault
Fault
Fault
90
Fault Distance calculated( km )
80
70
at
at
at
at
at
at
at
at
at
90km
80km
70km
60km
50km
40km
30km
20km
10km
60
50
40
30
20
10
0
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
t (s)
Figure 5-7: Simulation results for LLG fault with parameter independent and dependent
algorithms in EMTP simulations..
Shreya Parmar
Master of Science Thesis
5-2 RTDS Simulation Results
5-2
45
RTDS Simulation Results
The sampling for real time simulations was achieved in a different manner than its EMTP
counterpart. The time step was specified as 40 µs to achieve the desired sampling window
of N = 500 samples for the 200 ms plot duration. Fault was set to occur at 40 ms and
synchronization error was assumed to be 0. The load flow was run to find the initial voltage
magnitude and angle for each bus in the circuit. Subsequent RunTime results were taken
into account.
The RTDS simulation results are presented in the succeeding tables, along with the plots
for both algorithms at various distances for Rf = 0.01Ω.
Table 5-9: Simulation result for SLG Fault - RTDS 100kms
Actual Fault
P DE%
P LE%
10
65.245
25.06
15
-16.666
-0.366
20
7.3
-0.263
25
-0.188
-0.2
30
-5
-0.14167
35
-3.171
-0.1
40
-1.687
-0.068
45
0.971
-0.04
50
-1.4
-0.02
55
-1.272
-0.001
60
0.416
0.016
65
-0.230
0.023
70
1.271
0.021
75
0.44
0.04
80
1.062
0.043
85
1.202
0.043
90
-0.05
0.044
95
-1.766
0.037
(km)
Master of Science Thesis
Shreya Parmar
46
Simulation Results for different fault types
160
Fault
Fault
Fault
Fault
Fault Distance calculated( km )
140
at
at
at
at
70km
50km
30km
10km
120
100
80
60
40
20
0
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
t (s)
Figure 5-8: Simulation results for SLG fault using parameter independent algorithm in RTDS
simulations.
Table 5-10: Simulation result for LL Fault - RTDS 100kms
Actual Fault
P DE%
P LE%
5
-17
-1.08
10
1.04
-0.55
15
-0.653
-0.367
20
-0.39
-0.275
30
-2.78
-0.15
40
-0.19
-0.0675
50
-1.786
-0.02
60
-1.681
0.0138
70
0.18
0.032
80
-0.020
0.043
90
-1.22
0.041
(km)
Shreya Parmar
Master of Science Thesis
5-2 RTDS Simulation Results
47
Figure 5-9: Simulation results for LL fault with Parameter independent algorithms in RTDS
simulations..
Table 5-11: Simulation result for LLG Fault - RTDS 100kms
Actual Fault
P DE%
P LE%
5
-36.8
-1.1
10
-62
-0.56
15
-72.866
-0.373
20
-35.488
-0.262
25
-24.388
-0.194
30
-14.516
-0.15
35
-17.391
-0.098
40
-2.467
-0.052
45
-12.372
-0.040
50
-14.288
-0.019
60
-9.278
0.012
70
-11.388
0.034
80
-11.353
0.043
90
-11.957
0.041
(km)
Master of Science Thesis
Shreya Parmar
48
Simulation Results for different fault types
Figure 5-10: Simulation results Parameter independent algorithms for LLG fault.
Shreya Parmar
Master of Science Thesis
Chapter 6
Conclusions
This chapter discusses the fault location algorithms’ results and draws conclusions based
on the simulation outcomes presented in Chapter 5.
6-1
Result Analysis
The algorithms derived in Chapter 3 use two approaches to correctly locate a fault’s location in a transmission line. The parameter dependent algorithm, uses line parameters and
Clarke’s transformation to assess the correct location for a fault. This algorithm can be
applied to transmission corridors constituting of overhead transmission lines and underground cables. The second algorithm, does not use the parameters of the line, and hence,
proves to be more amicable in locating fault distances when compared with the parameter dependent algorithm. Both algorithms were tested extensively on EMTP/ATPDraw
platforms and then in real time environment on the RTDS with the help of system modelling done in RSCAD v2.025. The results of the algorithm on the two environments were
evaluated to find the usability of the algorithms.
Faults were simulated on different line lengths in both short and medium transmission lines
using three phase control switches and fault control logic. When faults were made closer
to the sending end terminals which was the reference for all simulations, a large value of
error was observed in almost all simulation cases. This is because of the fault currents in
the line phases, collect in a concentrated manner at the end terminals. Since the fault is
being made closer to one of the line terminals which is also a measurement point, a higher
value of fault current gives rise to errors in measurements and hence, an incorrect result.
The algorithms maintained accuracy of 1% for all possible fault resistances in ground faults,
only exacting a higher error value of fault location in parameter dependent algorithm,
Master of Science Thesis
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Conclusions
because of its dependence of the line parameters, which can change with fault resistance,
even more so for a fault resistance as high as 50Ω. In the RTDS simulations, oscillations
around the correct answer were observed in the RunTime window, when fault location
calculations with respect to time were to be plotted. This could be due to synchronization
errors, that is, using a time step as low as 40 µs to get 500 samples for the time window
in the DFT analysis duration, which increases the computations. However, in RTDS
simulation tests, as the initial algorithm results were showing dummy peaks in the final
value graphical plot, an 8 pole Low Pass filter with a cut-off frequency of 100 Hz was used,
which resulted in a more stable result.
Also, as shown in the result tabulations in Chapter 5, fault simulations for ground faults
in Parameter Independent algorithm showed more error for higher fault resistance values
towards the receiving end of the line terminal. This can be explained as, due to the fault
being closer to the receiving end terminal, more fault current flows to the resistive load at
that end, making the voltage phasors at the terminal less in magnitude and thereby, affecting the fault location calculations, which in turn is directly dependent on the magnitude
of the voltage and current phasors. Errors in fault location results are smaller towards the
middle of the total line length than the line end terminals also because of a homogeneity
in the fault currents at the middle of the line, than at the line end terminals.
Errors were also introduced because of use of a basic DFT algorithm. Tests done using
the Fast Fourier Transform (FFT) in MATLAB showed that the calculations were not just
faster, but the graphical plots also showed less or no garbage values, which could be due
to the high frequency components in the closely spaced samples. This is also because the
fundamental phasors for the sampled values were more accurate than those in the DFT.
The FFT is calculated more precisely because the reduction of redundant calculations
results in less round-off error, which also eliminates noise and redundant values from the
calculations. The reason for using DFT in the algorithms on EMTP and RTDS is however,
because of the restrictions in the functions library that both softwares provide.
The algorithms were derived using assumptions that the shunt capacitance of the transmission line can be neglected due to the algorithms being tested on short and medium
line lengths. However, when the algorithms were tested, the lumped RLC and distributedparameter line models that include the shunt capacitance were used and the algorithms
were found to remain accurate in EMTP results. The algorithms mostly maintained their
accuracies for different fault types, proving their flexibility and robustness.
Lastly, the simulation results and algorithm analysis proves that the parameter independent
or parameterless algorithm is not only more accurate, but flexible and robust enough to be
used under different system conditions and topologies, which is an advantage over FLAs
that can only be used for one fault type or that are sensitive to line parameters and fault
resistance.
Shreya Parmar
Master of Science Thesis
6-2 Future Work and suggestions
6-2
51
Future Work and suggestions
The simplicity of the algorithms used in this thesis makes it their best feature, as they can
be programmed or hard coded and implemented into any intelligent electronic device at
the line terminals and will accurately locate any fault that occurs on the line, even when
data sampling time-synchronization is lost. The FLAs are compatible with most devices
using SMT and the algorithms discussed in Chapter 3 use Phasor extraction methods,
hence their implementation into PMUs could result in more accurate and faster fault
location detection. The major disadvantage of these algorithms is that their accuracy can
be reduced when the data sampling synchronization is lost and a high fault resistance is
introduced in the system. Therefore, a synchronization check can be established for the
sampled data for correcting their outputs when the synchronization is lost.
Further research should be carried out for a better understanding of the harmonics induced
by fault arcs, which were assumed to be negligible in all simulation cases, and their affect
on rest of the power system, and in turn the fault location detected by the numerical
algorithms. The phasor-domain fault location algorithm presented in Section 3-2 should
be further tested for symmetrical three phase faults types.
A suitable sampling frequency and data window should be selected for the algorithms’
calculations since this can directly affect the accuracy of the results and the speed of the
calculations. A suitable sampling time and a high data window will always be beneficial,
as it will render the algorithm to be more accurate, since more data will negate the affect
of the incorrect values, as a whole. However, a higher sampling time will compromise the
computation time of the algorithms’. Hence, studies can be conducted and optimization
tools can be used to arrive at the most suitable combination of the two parameters.
The algorithms could be improved if more realistic line models including the shunt capacitance, were used in its derivation. The algorithms can be tested on untransposed and
longer transmission line lengths to validate the accuracy of their results. Using a network
model with more than two line end terminals and more generation and load sources could
also determine the algorithms accuracy in a practical electric grid usage.
Lastly, citing the real time test simulation results, the algorithms can be implemented and
tested using a microprocessor relay or any IED to help in their error improvement.
6-3
Summary
As a result of continuous exposure to unpredictable atmospheric and man-made conditions, the occurrence of faults on transmission lines is a common problem. Fault location
algorithms are important tools for expediting repairs after the occurrence of a fault on a
transmission line. This thesis explains the importance of protection of transmission lines
and how existing relay line protection devices can be insufficient in doing the same. The
Master of Science Thesis
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Conclusions
basics of fault analysis is discussed and the main categories of the fault location algorithm
techniques are critiqued.
This thesis report gives a detailed derivation of two fault location schemes for transmission
line networks. The first algorithm uses line parameters to locate faults. The second
algorithm is independent of the line parameters. Both algorithms can use synchronized
voltage and current measurements from the line end terminals. Extensive simulations
were carried out using ATPDraw, MATLAB and RTDS to evaluate the performance of
both algorithms under SLG, LL and LLG faults which are the most common types of
asymmetrical faults on transmission lines. From the errors observed in both FLAs, we see
that the parameter-independent fault location algorithm gives a better accuracy over the
parameter dependent algorithm. The simplicity of parameter independent fault location
detection is complimented by its capability of being easily practiced with existing power
system protection schemes.
Shreya Parmar
Master of Science Thesis
Appendix A
Simulation softwares used
A-1
MATLAB
MATLAB (matrix laboratory) is a multi-paradigm numerical computing environment and
fourth-generation programming language. Developed by MathWorks, MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation
of user interfaces, and interfacing with programs written in other languages, including C,
C++, Java, Fortran and Python.
Although MATLAB is intended primarily for numerical computing, an optional toolbox
uses the MuPAD symbolic engine, allowing access to symbolic computing capabilities. An
additional package, Simulink, adds graphical multi-domain simulation and Model-Based
Design for dynamic and embedded systems.
MATLAB was largely used for graphical plots made in the report and to check the results
for corrective purposes.
A-2
EMTP/ATPDraw
ATPDraw is a graphical, mouse-driven preprocessor to the ATP version of the Electromagnetic Transients Program (EMTP) on the MS-Windows platform. In ATPDraw the
user can construct an electrical circuit using the mouse and selecting components from
menus, then ATPDraw generates the ATP input file in the appropriate format based on
"what you see is what you get". The simulation program ATP and plotting programs can
be integrated with ATPDraw. A license is required to use the solver ATP.
Master of Science Thesis
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Simulation softwares used
Figure A-1: ATPDraw User Interface
A-3
RTDS/RSCAD
Real Time Digital Simulator or RTS as the abbreviation recommended by IEEE committee
on real-time simulator applied for power systems provides power systems simulation technology for fast, reliable, accurate and cost-effective study of power systems with complex
High Voltage Alternating Current (HVAC) and High Voltage Direct Current (HVDC) networks. The RTS is a fully digital electromagnetic transient power system simulator that
operates in real time.
The system’s graphical user interface, proprietary software and mathematical algorithms
can simulate any modern electric power grid configuration. As new equipment or components are added or subtracted from the simulator’s configuration, the model instantly
updates. For example, researchers can run simulated system-failure scenarios such as a
control system cyber intrusion or a physical damage event such as a terrorist attack or
natural disaster and instantly detect the order and reasoning for why dedicated relays,
breakers or substations failed.
Because the simulator functions in real time, the power system algorithms are processed to
continuously produce output conditions that represent conditions in a real network. Realtime simulation is significant because the user can test physical devices using the ’Hardware
in Loop’ method, which is important for testing of many equipments and algorithms, which
may be difficult to achieve in an actual High Voltage line. Also, simulations can be run
faster since simulation of the system’s response over 1 second is computed in exactly 1
second. This is note worthy as all calculations required to determine the power system’s
state are completed in a time exactly equal to the simulation time step. Power systems
Shreya Parmar
Master of Science Thesis
A-3 RTDS/RSCAD
55
networks can be built and compiled in the Draft case and the simulations are established
on the RunTime window.
Figure A-2: RSCAD Power system user library
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Shreya Parmar
Simulation softwares used
Master of Science Thesis
Appendix B
Symmetrical components and Sequence
Networks
B-1
Conversion Between the Phase and Symmetrical Component Domains
Any set of phase quantities can be converted into symmetrical components, where α is
defined as 1∠120◦ , as follows:

 
1
I0 
 
I 
 1
 
I2
=
1


1  Ia 
 
1
1 α α2   I 

  b
3
 
2
1 α α
Ic
where I0 , I1 , and I2 are the zero, positive, and negative sequence components, respectively.
The above transformation equation shows the symmetrical component transformation in
terms of currents, but the same equations are valid for voltages as well. This results in the
following equations:
1
I0 = (IA + IB + IC )
3
1
I1 = (IA + αIB + α2 IC )
3
1
I2 = (IA + α2 IB + αIC )
3
Master of Science Thesis
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Symmetrical components and Sequence Networks
Similarly, the inverse phase vectors can be achieved as:



1
Ia 
 
I 
 b
 
Ic
=
1


1   I0 
 
1
1 α2 α  I 
  α

3
 
2
Iβ
1 α α
which leads to:
IA = (I0 + I1 + I2 )
IB = (I0 + α2 I1 + αI2 )
IC = (I0 + αI1 + α2 I2 )
Thus, a set of unbalanced system variables can be defined in terms of three balanced systems and the symmetrical transformation may be used to convert phase voltages (or currents) to symmetrical component voltages (or currents) and vice versa. These conversions
are valid for an A-phase base, which can be used for A-phase-to-ground, B-phase-to-Cphase, B-phase-to-C-phase-to-ground, and three-phase faults.
B-2
Sequence Networks for Faults
Sequence networks for the faulted system are always dependent on the type of fault, where
separate symmetrical or balanced networks after symmetrical transformation are linked at
the fault point. Sequence network connections for common shunt fault types are shown in
this section where Zf is defined as the fault impedance from each phase to the common
point, and Zg is defined as the impedance from the common point to ground. The Zg base
term is only significant when Zf differs per phase or if the line impedance to the fault point
is different between phases.
(c) Double line to ground fault
(b) Double line fault network.network.
(a) Single line fault network.
Figure B-1: Asymmetrical fault sequence network.
Shreya Parmar
Master of Science Thesis
B-2 Sequence Networks for Faults
59
Figure B-2: Three phase fault sequence network.
Master of Science Thesis
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Shreya Parmar
Symmetrical components and Sequence Networks
Master of Science Thesis
Appendix C
Phasor Measurement Units
Critical data, like voltage and current in power system calculations can be represented as
complex numbers. This in turn can be represented as ’phasors’ which contains a magnitude
and direction, or angle.
C-1
Basics
Conventional measurement instruments measure only the magnitude of these phasor quantities. Measurements requiring synchronization and accurate time stamping, as in the case
of phasor measurements for fault location detection, have phase angle measurement errors
as a prevalent phenomenon.
As a solution to this problem, PMUs measure the magnitude and angles of phasors with
accurate time stampings with respect to the system frequency. The measurements are synchronized with a clocking signal obtained continuously from a global positioning system
(GPS). Thus, the base operation or control station for the concerned grid of power system
is able to receive the synchronous data from each PMU in real time. The location of malfunctioning circuits or transmission lines can be immediately identified if phase differences
between different PMUs are detected. In synchronous measurement technology, PMUs
are often referred to as ’Synchrophasors’ and they are termed to be the most important
measuring devices in the future of Smart Grids.
PMUs can be used as stand alone devices or can be easily integrated with existing power
system protection and monitoring devices. Some examples of such PMUs are L60, D60
models of the General Electric.
The demerits of using SCADA over PMU based measuring devices are as given below:
• measurements are not synchronized due to inaccurate time stamping.
Master of Science Thesis
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Phasor Measurement Units
• slower rate of measurements – upto 1 sample per sec.
• limited dynamic performance monitoring for the power system
C-2
Applications
• state estimation
• fault detection
• line parameter calculations
• congestion management
• real time control and monitoring in Wide Area Monitoring Systems
C-3
Placement
Figure C-1: PMU placement in power systems
PMU exacts the voltage phasor of the bus where it is installed and current phasors of all
branches incident to that bus. The PMU measurements are given from different buses,
which are synchronized by the common clock signal from global positioning system (GPS).
A PMU and its affiliated communication equipments are generally costly. So it is neither
economical nor necessary to install PMUs at all system buses. Consequently, the placement
and optimal numbers of PMU units becomes of maximal importance to ensure no errors in
the measurements. Much research has been established in this direction. The paper [19]
covers most of the methods used for hierarchy based PMU placement methods.
Shreya Parmar
Master of Science Thesis
Bibliography
[1] M. Kezunovic, “Smart fault location for smart grids,” Smart Grid, IEEE Transactions
on, vol. 2, no. 1, pp. 11–22, 2011.
[2] http://www.reuters.com/article/2015/03/27/us-dutch-power-outages, 2015.
[3] http://www.atpdraw.net/, 2015.
[4] https://www.rtds.com, 2015.
[5] S. Tamronglak, S. Horowitz, A. Phadke, and J. Thorp, “Anatomy of power system
blackouts: preventive relaying strategies,” Power Delivery, IEEE Transactions on,
vol. 11, no. 2, pp. 708–715, 1996.
[6] M. M. Hashim, H. W. Ping, and V. Ramachandaramurthy, “Impedance-based fault
location techniques for transmission lines,” in TENCON 2009-2009 IEEE Region 10
Conference, pp. 1–6, IEEE, 2009.
[7] M. Idris, M. Mustafa, and Y. Yatim, “Effective two-terminal single line to ground fault
location algorithm,” in Power Engineering and Optimization Conference (PEDCO)
Melaka, Malaysia, 2012 Ieee International, pp. 246–251, IEEE, 2012.
[8] J. Izykowski, E. Rosolowski, and M. Saha, “Method and device for fault location in
a two-terminal transmission or distribution power line,” May 22 2012. US Patent
8,183,871.
[9] C. Christopoulos, D. Thomas, and A. Wright, “Scheme, based on travelling-waves,
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[10] F. H. Magnago and A. Abur, “Fault location using wavelets,” Power Delivery, IEEE
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[11] E. Ngu and K. Ramar, “A combined impedance and traveling wave based fault location
method for multi-terminal transmission lines,” vol. 33, pp. 1767–1775, Elsevier, 2011.
[12] Z. Bo, A. Johns, and R. Aggarwal, “A new non-unit protection scheme based on fault
generated high frequency current signals,” 1995.
[13] Z. Bo, G. Weller, and M. Redfern, “Accurate fault location technique for distribution
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[14] A. G. Phadke and J. S. Thorp, Synchronized phasor measurements and their applications. Springer, 2008.
[15] D. Novosel and K. Vu, “Benefits of pmu technology for various applications,” Zbornik
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[16] E. Sayed Tag El Din, M. M. Abdel Aziz, M. Gilany, et al., “Fault location scheme
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[17] M. Popov, G. Rietveld, Z. Radojevic, and V. Terzija, “An efficient algorithm for fault
location on mixed line-cable transmission corridors,” in International Conference on
Power Systems Transients (IPST), 2013.
[18] Z. Radojević, C. Kim, M. Popov, G. Preston, and V. Terzija, “New approach for fault
location on transmission lines not requiring line parameters,” in Proceeding of IPST
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(AUPEC), 2011 21st Australasian, pp. 1–4, IEEE, 2011.
Shreya Parmar
Master of Science Thesis
Glossary
FLA
Fault Location Algorithm(s)
PMU
Phasor Measurement Unit(s)
SMT
Synchronised Measurement Technology(s)
DFT
Discrete Fourier Transform
GPS
Global Positioning System
EMTP
Electro-magnetic Transients Programme
ATP
Alternate Transients Programme
RTDS
Real Time Digital Simulator
SLG
Single line-to-ground
LLG
Line-to-line-to-ground
LL
Line-to-line
STFLA
Single-terminal Fault Location Algorithm(s)
TTFLA
Two-terminal Fault Location Algorithm(s)
TWFLA
Travelling Wave Fault Location Algorithm
SCADA
Supervisory Control and Data Acquisition
XPS
Expert Sytem Techniques
ANN
Artificial Neural Networks
FL
Fuzzy Logic systems
Master of Science Thesis
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66
Glossary
AC
Alternate Current
UDC
User Defined Component
IED
Intelligent Electronic Device
Shreya Parmar
Master of Science Thesis
67
List of Symbols and Variables
R
Line resistance
L
Line inductance
C
Line capacitance
G
Line shunt conductance
F
Fault point on a line
D
Total length of transmission line
`
Fault location calculated by the algorithm(s)
S
Sending end of transmission line
R
Receiving end of transmission line
γ
Propagation constant
Zc
Characteristic impedance of the transmission line
Vx
Voltage at any point x on the transmission line
VS
Voltage from Sending end of the transmission line
Ix
Current at any point x on the transmission line
IS
Current from Sending end of the transmission line
VR
Voltage from Receiving end of the transmission line
IR
Current from Receiving end of the transmission line
Va,b,c
Three phases of voltage
Ia,b,c
Three phases of current
Vα,β,0
Three modes of the voltage after applying Clarcke’s transformation
Iα,β,0
Three phases of current after applying Clarcke’s transformation
z
Impedance of the transmission line
Vp
Positive sequence component of voltage
Vn
Negative sequence component of voltage
Master of Science Thesis
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68
Glossary
Ip
Positive sequence component of current
In
Negative sequence component of current
fs
Sampling frequency
N
Number of samples used
Rf
Fault resistance
Shreya Parmar
Master of Science Thesis