Presentation on fault detection in power transmission lines.pdf

Fault detection in power transmission lines

1. FAULT DETECTION, CLASSIFICATION AND
LOCATION IN POWER TRANSMISSION
NETWORK
D1 Presentation (State of Art)
SAMA Promise AWA
Laboratory of Computer Science Engineering and Automation,
ENSET Douala
President :
Professor, University of Douala
Telephone No: /655037902
Email Address :
Rapporteur :
Professor, University of Douala
Member :
Lecturer, University of Douala
January 12, 2023

2. CONTENTS
1GENERAL INTRODUCTION
Institutional and Partnership Frameworks
Scientic Motivations of the Thesis
Statement of Problem and Research Objectives
2CALENDAR OF ACTIVITIES
D1 Activities
D2 Activities
D3 Activities
3STATE OF ART

3. GENERAL INTRODUCTION
Institutional Framework
This thesis is founded in the Framework of acquiring a Doctorate Degree (PhD) in
Engineering Science (Eng.Sc.) at the Higher Technical Teacher Training College (ENSET)
Douala, aliated through the Post Graduate School of Fundamental and Applied Sciences
(POSPAS) of the University of Douala (UD), more particularly in the Doctorate Training unit
of Engineering Sciences (UFD-SCI), in the eld of Electrical, Electronic Engineering and
Industrial Computing (EEII) of the Laboratory of Computer Science and Automation
Engineering (CAE).
Partnership Framework
This work was carried out solely in the Laboratory of Computer Science and Automation
Engineering (CAE) of the Higher Technical Teacher Training College (ENSET) Douala: -
aliated to the Post Graduate School of Pure and Applied Sciences (POSPAS) of the
University of Douala. Sponsorship was mainly solicited from the collaboration of
supervision, parents and Individual nances.

4. GENERAL INTRODUCTION
National Context
At the national level, though a few researches related to power systems have been attempted
and preliminary studies have been studied at both undergraduate and post graduate levels,
one is still to nd out if there exist any internationally accepted article on fault detection,
classication and location in PTNs published by a Cameroonian. Thus, based on intensive
survey and availability of literature on the subject under study, no work has been reported or
published in Cameroon in relation to fault detection or classication or location in PTNs.
This is a call for concern in the science and engineering community of Cameroon.
International Context
At the international level, tens of articles have been written on fault detection, location and
classication in power transmission lines. The most recent one that directly ts into the
framework of our study is that of Le Van Dai et al., published in 2022. However, most of the
works published online are based on Machine Learning and the technology of Deep learning
are still under-explored in this domain if not for publishers like Le Van Dai et al., who have
recently delved into the glimpse of this Deep learning approach.

5. GENERAL INTRODUCTION
Problem Statement
Most of the approaches used in fault detection, location and classication in
power transmission networks are based on ML. Several algorithms have
been proposed which actually do the work of fault detection classication
and location but not without limitations. Currently, researchers, engineers
and scientists are more inclined to the opinion that DL approaches could be
used to solve this problem of fault detection, classication and location.
PTNs are the most important parts of any power supply system. Thus
detecting, classifying and locating faults in a bit to rapidly cure the system of
any failure each time a fault occurs is of utmost importance.
With all the challenges faced by the energy industry, a novel, and accurate
algorithm for fault detection, classication and location in PTNs is our
priority. A Deep Learning approach is one of the new approaches which can
be used to target this subject.

6. GENERAL INTRODUCTION
Research Objectives General Objective
The general objective of this research is to come out with a novel algorithm for the detection,
classication and location of faults in a power transmission network (PTN).
Specic Objectives
The specic objectives of this study are to
rCarry out a critical review of fault detection, classication and location approaches
used in PTNs bringing out the pros and cons of each algorithm with respect to the
scientic and engineering principles governing PTNs.
rDevelop a novel algorithm for fault detection and classication using deep learning
(DL) approach.
rHybridize machine learning (ML) and deep learning (DL) approaches to bring out an
algorithm for fault location in PTNs.

7. CALENDAR OF ACTIVITIES
D1 (2021/2022)
SEMESTER 1
Trimester 1 Trimester 2
First meeting with the supervisor
immediately after admission to in-
quire what to do and how to work
on the research.
Gathering articles and materials for
an in-depth understanding of the
theme of the thesis.
SEMESTER 2
Trimester 3 Trimester 4
Writing out a General Introduction
and bringing out the objectives of
the study.
Writing a concise literature review
from the articles and materials gath-
ered.

8. CALENDAR OF ACTIVITIES
D2 (2022/2023)
SEMESTER 3
Trimester 5 Trimester 6
Developing a virtual power
transmission network which
will be used to test any algo-
rithms developed during the re-
search.
Editing the literature review while De-
veloping a novel algorithm on fault de-
tection and classication in power trans-
mission networks using deep learning
approach.
SEMESTER 4
Trimester 7 Trimester 8
Concretizing the new algorithm
for fault detection and classica-
tion and drafting the rst article
from the work already done.
Editing and publishing the rst article
while developing a novel algorithm for
fault location in power transmission net-
works using a hybridized approach of
deep learning and machine learning.

9. CALENDAR OF ACTIVITIES
D3 (2023/2024)
SEMESTER 5
Trimester 9 Trimester 10
Concretizing the algorithm on fault
location while drafting the second
article from the work already done.
Editing and publishing the second
article in an internationally recog-
nized scientic journal.
SEMESTER 6
Trimester 11 Trimester 12
Conclusions and comparative stud-
ies between the research carried out
and other published works related.
Editing, printing the thesis and nal
presentation of the thesis before the
jury.

10. STATE OF ART
Published Works
rArticial Neural Network (ANN),
rAI in Internet of Things (IoT),
rGSM Technology,
rGroup Method of Data Handling
(GMDH) function,
rWavelet Transform (WT),
r8051 Microcontroller,
rKernel Density Estimation,
rauthorization and distance calculation
through impedance variation,
rEuclidean metric method,
rFast current method,
rair borne laser LiDAR approach,
rfuzzy logic techniques,
r
adaptive neuro-fuzzy inference system
(ANFIS) techniques,
rNaive Bayes classier,
rMIMO systems with derivative
estimations,
rUse of PLC and SCADA,
rADC current sensors,
rprogramming with Arduino 328,
rphasor based approach,
rD-STATCOM, matching pursuit
decomposition (MPD),
rLuenberger observer method,
runmanned aerial vehicle (UAV) smart
systems,

11. STATE OF ART
Published Works
rsupport vector machine (SVM),
rMagnetic eld sensoring coils,
rpattern recognition approach,
rPhasor measurement unit (PMU)
measurements,
rsoft computing methods,
rhierarchical multiview features
approach,
rfrequency domain analysis, and
rDeep learning methods.
Sanaye and Khorashadi, 2003
Sanaye and Khorashadi presented the use of articial neural networks (ANN) as a protective
relaying pattern classier algorithm. The proposed method used current signals to learn the
hidden relationship in the input patterns.

12. STATE OF ART: FAULT DETECTION
Sanaye and Khorashadi, 2003
Figure:

13. STATE OF ART: FAULT DETECTION
Tahar Bouthiba, 2004
Bouthiba applied articial neural networks (ANNs) to the fault detection and
location in extra high voltage (EHV) transmission lines for high speed
protection using one terminal line.
Figure:

14. STATE OF ART: FAULT LOCATION
Tahar Bouthiba, 2004
the fault locator (FL) is designed to indicate the distance of the fault in the
transmission line.
Figure:

15. STATE OF ART: FAULT LOCATION
Le Van Dai et al., 2022 (Deep Learning Approach)
Figure:

16. STATE OF ART: FAULT CLASSIFICATION
Sanaye and Khorashadi, 2003
Table:
Fault Type ABCN
AG 1001
BG 0101
CG 0011
AB 1100
BC 0110
CA 1011
ABG 1101
ACG 1011
BCG 0111
ABC 1110

17. STATE OF ART: FAULT CLASSIFICATION
Le Van Dai et al., 2022
Table:
Fault Type ABCGOutput
Normal 0000 0
AG 1001 1
BG 0101 2
CG 0011 3
AB 1100 4
BC 0110 5
CA 1011 6
ABG 1101 7
ACG 1011 8
BCG 0111 9
ABCG 1111 10

18. STATE OF ART: DEEP LEARNING ALGORITHM
Le Van Dai et al., 2022
Figure:

19. PROSPECTIVE
SONATREL PTN to be designed in EMTP-RV and MATLAB
Figure:

20. Thank you for your keen attention: : :
SAMA Promise Awa
‰[phone]
[email protected]