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12-22 

12 

 

 

 

Article 

State-of-the-art techniques and algorithms for swift 

and precise fault detection and protection in 

transmission lines 
Siphesihle Sibonelo Xulu1, Bongumsa Mendu1,2*, Bessie Baakanyang Monchusi1 

 1University of South Africa, 28 Pioneer Ave, Florida Park, Roodepoort, 1700, South Africa 

 2National Transmission Company South Africa SOC Ltd, Maxwell Dr, Sunninghill, Sandton, 2157, South Africa 

A R T I C L E   I N F O 
 

Article history: 
Received 29 November 2024  
Received in revised form 
10 January 2025 
Accepted 18 January 2025 
 
Keywords:  
Fault detection, Transmission lines protection, 
Three-phase, Techniques and algorithms 
 
*Corresponding author 
Email address: 
mendubongumsa@gmail.com 
 
 
DOI: 10.55670/fpll.futech.4.1.2 
 

A B S T R A C T 
 

Transmission lines are crucial for power systems, enabling bulk power transfer 
from generation sites to load centers. They face challenges such as faults, losses, 
and delays, necessitating effective management and maintenance strategies. 
The aim of this paper is to conduct a systematic literature review focusing on 
techniques and algorithms for swift and precise fault detection and protection 
in transmission lines. The methodology included a collection of relevant papers, 
a filtering process, eligibility identification, synthesizing, and trend analysis. 
This process was facilitated using the Scopus database and VOSviewer software. 
Results of this survey revealed some key noticeable aspects (among others) 
across the studies, which included the utilization of diverse signal processing 
and machine learning techniques to analyze voltage and current signals for 
identifying faults. This work will contribute by reviewing recent advances in 
signal processing, analyzing methods to enhance fault detection speed and 
accuracy, exploring the use of machine learning and neural networks in fault 
detection models, investigating advanced relay technologies and protection 
schemes, evaluating statistical techniques for fault isolation, and examines 
indexing techniques and evolutionary programming tools for precise fault 
identification, while also proposing future research directions. 

 

1. Introduction 

Transmission lines are crucial for power systems, 
enabling bulk power transfer from generation sites to load 
centers [1].  They face challenges such as faults, losses, and 
delays, necessitating effective management and maintenance 
strategies. Reliability-centered maintenance can prioritize 
lines based on their condition and importance [2]. Energy 
harvesting methods for powering wireless sensors are being 
developed to enhance monitoring capabilities [3]. Fault 
detection systems are essential for minimizing interruptions 
and improving reliability [4]. Inspection robots, including 
climbing, flying, and hybrid types, are emerging technologies 
for early fault detection [5]. Research trends in transmission 
lines focus on areas like line inspection, fault location, and 
artificial intelligence [6]. Delays in transmission projects 
often stem from right-of-way issues and route changes [7]. 
Minimizing power losses through techniques like capacitor 
compensation is crucial for efficient power delivery [8]. 
Recent research on fault detection in transmission lines 
focuses on developing swift and precise methods to enhance 
power system reliability. Various approaches have been 

proposed, including smart algorithms using phasor 
measurement units [9], machine learning techniques for 
simultaneous detection and localization [10], and deep 
learning models combined with Discrete Wavelet Transform 
[11]. Wavelet Transform has been widely explored for its 
effectiveness in fault detection and classification [12,13]. 
Artificial Neural Networks have shown promise in fault 
detection, classification, location, and direction 
discrimination [14]. Some studies have utilized digital signal 
processing techniques [15] and evolutionary programming 
tools [16] to improve fault analysis. These advanced methods 
aim to minimize power losses, reduce downtime, and 
optimize maintenance efforts in transmission systems, 
addressing the growing demand for reliable power 
distribution. Current transmission line analysis and modeling 
advancements have significantly improved power system 
management and reliability. State-of-the-art techniques for 
power flow analysis, such as particle swarm optimization and 
hybrid algorithms, have shown superior accuracy and 
efficiency compared to classical methods [17]. Energy 
harvesting technologies for wireless sensors on transmission 

 

 

Future Technology 

Open Access Journal 

https://doi.org/10.55670/fpll.futech.4.1.2 

 

February 2025| Volume 04 | Issue 01 | Pages 12-22 

Journal homepage: https://fupubco.com/futech 

 

ISSN 2832-0379 

mailto:mendubongumsa@gmail.com
https://doi.org/10.55670/fpll.futech.4.1.2
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SS. Xulu et al. /Future Technology                                                                                          February 2025| Volume 04 | Issue 01 | Pages 12-22 

13 

 

lines are evolving rapidly, enabling real-time monitoring and 
predictive maintenance [18]. Innovative approaches for 
transmission line simulation using S-parameter data [19] and 
parameter identification through state estimation [20] have 
enhanced modeling accuracy. Advanced fault diagnosis and 
prognosis techniques, including artificial intelligence 
methods, improve network reliability [21]. Wavelet 
transform analysis is being applied to identify and classify 
disturbances on transmission lines [22]. Additionally, 
metamaterial transmission lines [23] and computational 
methods for electromagnetic field analysis [24] are expanding 
the capabilities of microwave and millimeter-wave 
technologies. 

It is evidence that research on fault detection in 
transmission lines emphasizes the development of swift and 
precise methods to enhance power system reliability. Various 
approaches have been proposed, including smart algorithms 
using phasor measurement units, machine learning 
techniques for simultaneous detection and localization, and 
deep learning models combined with Discrete Wavelet 
Transform. Wavelet Transform has been widely explored for 
its fault detection and classification effectiveness. Artificial 
Neural Networks have shown promise in fault detection, 
classification, location, and direction discrimination. Some 
studies have utilized digital signal processing techniques and 
evolutionary programming tools for improved fault analysis. 
These advanced methods aim to minimize power losses, 
reduce downtime, and optimize maintenance efforts in 
transmission systems, addressing the growing demand for 
reliable power distribution. However, there is a distinguished 
gap in the literature regarding a comprehensive review of 
state-of-the-art techniques and algorithms for swift and 
precise fault detection and protection in transmission lines. 
Thus, the aim is to conduct a systematic review of these state-
of-the-art techniques and algorithms, with contributions 
focused on: 
• Reviewing recent advances in signal processing methods. 
• Analysing methods that enhance the speed and accuracy of 

fault detection and classification. 
• Exploring the application of machine learning and artificial 

neural networks in developing sophisticated models for 
fault detection. 

• Investigating the latest relay technologies and protection 
schemes that utilize advanced algorithms and smart 
sensors to improve fault detection and system protection. 

• Evaluating specific fault detection methods that employ 
statistical techniques like the summation of squared 
currents and moving average methods to identify and 
isolate faults. 

• Examining the development and implementation of 
indexing techniques and evolutionary programming tools 
that aid in the precise identification and indexing of faults. 

The rest of the paper is arranged as follows: Section II: 
Developments in fault detection, classification, and location in 
power systems. Section III: Methodology of how the project 
was conducted. Section IV: Results and discussion, where all 
the papers are grouped according to their relevance and the 
topic being researched. Section V: Literature Review Analysis 
Observations provides an in-depth examination of the 
literature review. Section VI: Conclusion and Future Research 
summarizes the key findings and contributions of the study 
and offers recommendations for future research 
recommendations based on the knowledge acquired. 

 

2. Developments in fault detection, classification, and 

location in power systems 

In the past two decades, significant progress has been 
made in detecting, classifying, and locating faults in power 
systems. Advancements in signal processing, artificial 
intelligence, machine learning, GPS technology, and 
communication systems have allowed researchers to improve 
and extend traditional fault protection methods. These 
innovations have also addressed key limitations in online 
fault diagnosis, enhancing system reliability and performance 
[25]. As illustrated in Figure 1, the process begins by sampling 
current and voltage signals, which are fed into a feature 
extraction module. The extracted features are used by the 
fault detection, classification, and location modules. The final 
outputs provided by the system are the fault type and 
location, determined by the fault classifier and locator, 
respectively [26]. Figure 2 depicts the structure of the 
extreme learning machine (ELM) model. The hidden layer 
comprises 700 nodes, while the output layer includes a single 
node for fault detection (FD) and 11 nodes for fault 
classification (FC). Each node is equipped with an activation 
function, and the ReLU function was used to activate them 
[27]. 

 
Figure 1. Simplified framework for fault detection, classification, and 
location 

 

 

Figure 2. Structure of elm classifier employed 

 



SS. Xulu et al. /Future Technology                                                                                          February 2025| Volume 04 | Issue 01 | Pages 12-22 

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The performance of the models was evaluated using 
common statistical metrics such as accuracy, precision, recall, 
and F1-score. The formula for calculating accuracy is 
provided in Equation 1: 

𝐴𝑐𝑐 =
(𝑃𝑇+𝑇𝑁)

(𝑃𝑇+𝑇𝑁+𝐹𝑃+𝐹𝑁)
 ×  100 %                                                    (1) 

In this context, TP represents true positives, indicating 
correctly detected faults, while TN denotes true negatives, 
meaning correctly identified non-faulty cases. FP refers to 
false positives, where non-faulty cases are mistakenly 
identified as faulty, and FN represents false negatives, where 
actual faults are missed [28]. Precision (P), as defined in 
Equation 2, is calculated by dividing the true positives by the 
total predicted positives. It shows the proportion of correctly 
detected faults out of all cases predicted as faulty [10]:   

𝑃 =
𝑇𝑃

(𝑇𝑃+𝐹𝑃)
                                                                                          (2) 

  Recall (𝑅) measures the model's effectiveness in 
identifying actual faulty cases. It is calculated as the ratio of 
correctly predicted faults to the total number of actual faults 
and can be expressed by the following formula:                                   

𝑅 =
𝑇𝑃

(𝑇𝑃+𝐹𝑁)
                                                                                        (3) 

The F1 score is a metric that evaluates a model’s overall 
performance by balancing precision and recall. It is the 
harmonic mean of precision and recall, with a maximum value 
of 1.0 indicating perfect precision and recall, while a value of 
0 means both are absent. The formula for calculating the F1 
score is as follows [29]: 

𝐹1 − 𝑆𝑐𝑜𝑟𝑒 =
2(𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 𝑥 𝑅𝑒𝑐𝑎𝑙𝑙)

(𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛+𝑅𝑒𝑐𝑎𝑙𝑙)
                                                    (4) 

3. Methodology 

The methodology involved identifying relevant 
keywords, conducting a thorough search using the Scopus 
database, filtering and exporting data, and utilizing 
VOSviewer software for data analysis. Below is a detailed 
description of each step taken in the process. 

3.1 Topic and keywords 
The research topic chosen for this review is the detection 

of faults in three-phase transmission lines and its societal 
impact. To gather relevant information, the keywords “Fault 
Detection,” “Transmission Lines,” AND “Three Phase” were 
designed. These keywords were selected to ensure 
comprehensive coverage of the topic. 

3.2 Scopus database 
3.2.1 Initial search 

The Scopus database was accessed using a University 
of South Africa student email. In the search settings, "Article 
title, Abstract, Keywords" was selected. The designed 
keywords were entered into the search documents box, and 
the search was executed. This initial search yielded 296 
documents. The results were sorted by relevance to prioritize 
the most pertinent documents. 

3.2.2 Filtering by year and subject area 
To focus on recent advancements, the publication date 

range was narrowed to 2019-2023, reducing the number of 
documents to 137. Next, the subject area was refined to 
"Engineering," further limiting the results to 102 documents. 
This step ensured that only engineering-related papers were 
considered. 

3.2.3 Filtering by document type and language 
The document type was limited to "Conference paper" 

and "Article," resulting in 100 documents. The language filter 
was set to English, further reducing the number to 99 
documents. These steps ensured that the selected papers 
were both relevant and accessible (Table 1). 

Table 1. Inclusion and exclusion criteria for fault detection in 
transmission lines with three-phase systems 

 

3.2.4 Exporting data 
The 99 documents deemed relevant were selected for 

export. The CSV file format was chosen, and all relevant 
information was included in the export. The file was 
downloaded and saved to a secure location on the computer. 

3.2.5 Data processing 
The downloaded CSV file was opened, and its content 

was copied into a new Excel sheet titled "Book 1." This new 
sheet was used for data processing and analysis. The abstracts 
were reviewed to ensure relevance, and a new column was 
added to synthesize the information provided in each paper. 

3.2.6 Relevancy assessment 
Three additional columns were added to the Excel 

sheet: Column S for "What was done, “Column T for” Where 
the problem was solved, and Column U for "Methods, 
techniques, or procedures used to solve the problem." 
Irrelevant articles were identified and removed from the 
dataset, ensuring a focused and relevant literature review. 

3.3 VOSviewer software 
VOSviewer, a software tool for constructing and 

visualizing bibliometric networks, was used to analyze the 
data obtained from the Scopus database. The software was 
downloaded, installed, and launched. The "Create" option was 
selected to create a new file. The type of data was set to 
"Create a map based on bibliographic data," and the data 
source was set to "Read data from bibliographic database 
files." The Scopus CSV file was uploaded for analysis. 

3.4 Generating and verifying network visualization 
The type of analysis was set to "Co-occurrence," the 

counting method to "Full counting," and the unit of analysis to 
"All keywords." A threshold of 5 occurrences was set, 
resulting in 56 keywords meeting the threshold criteria. The 
network visualization map was generated, displaying all 
keywords linked in a network. The keywords were verified, 
and the final network visualization was reviewed for 
accuracy. 

 

 

 

 

Item name  Inclusion criteria Exclusion criteria 

Database Scopus  Google Scholar 
Publication 
period 

2019-2023 2018 and before 

Document 
type 

Article and conference 
paper  

Notes, letters, books 

Subject area Engineering  
Social, physical, 
health 

Language English Spanish, Chinese 

File type CSV 
Plain text, RIS, 
Bibtex, endnote 



SS. Xulu et al. /Future Technology                                                                                          February 2025| Volume 04 | Issue 01 | Pages 12-22 

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4. Results and discussion 

4.1 Signal processing techniques for fault detection in 
transmission lines 
Prasad and Nayak [30] proposed a method utilizing 

discrete Fourier transform (DFT) to estimate fundamental 
components of three-phase current phasors, which aids in 
fault detection. Similarly, Gupta et al. [31] performed 
MATLAB simulations on a two-terminal transmission line, 
employing DFT along with a time-frequency approach for 
fault detection and classification. Patel and Bera [32] 
leveraged wavelet packet transform (WPT) with the db1 
wavelet to isolate high-frequency components from current 
signals, enabling effective identification of faults in 
transmission lines.  

AsghariGovar et al. [33] further explored WPT in the 
context of high-impedance fault protection for transmission 
and distribution systems by extracting high-frequency signal 
coefficients. Building on wavelet-based techniques, Ashok 
and Yadav [34] introduced maximal overlap discrete wavelet 
transform (MODWT) to analyze faulty signals during power 
swings, employing a fault triangle approach to classify faults. 
Sailakshmi et al. [35] applied discrete wavelet transform 
(DWT) in MATLAB to simulate and detect various fault 
conditions, while Kapoor et al. [36] developed a fault 
detection framework using Discrete Fast Walsh-Hadamard 
Transform (DFWHT), evaluating its performance across 
multiple fault scenarios.  

4.2 High-speed fault detection and classification in 
transmission lines 
Alizadeh et al. [37] developed a high-speed fault 

detection technique employing Mathematical Morphology, 
which uses voltage and current signals to identify faults and 
distinguish power swings. Das et al. [38] utilized Lissajous 
patterns of voltage and current signals for fault detection and 
classification by calculating changes in area to derive fault 
indices. Anand and Affijulla [39] proposed a method for 
detecting high-impedance faults using the Hilbert-Huang 
Transform, where energy was computed from the intrinsic 
mode functions of voltage and current signals for each phase. 
In a related study, Das et al. [40] applied Principal Component 
Analysis to fault detection in overhead transmission lines by 
analyzing peaks and crests of transient signals at the receiving 
end. 

4.3 Learning algorithms for fault detection and 
classification in transmission lines 
Mukherjee et al. [41] applied a probabilistic neural 

network to simulate and analyze faults at different locations, 
generating a fault intensity index based on three-phase fault 
characteristics. Vyas et al. [42] combined wavelet transform 
with a Chebyshev neural network to design a fault detection 
model, verifying its performance using synthetic fault data. 
Rai et al. [43] and Radhi et al. [44] explored convolutional 
neural networks (CNNs) for fault detection, focusing on 
voltage and current signals. While Rai et al. used standard 
CNN architecture and validated the model through cross-
validation, Radhi applied a one-dimensional CNN to a 132 kV 
transmission system. Mitra et al. [45] built on this by refining 
the 1D-CNN approach to improve computational efficiency in 
classifying faults. Ahmed et al. [46] modeled a four-bus power 
system with three transmission lines for fault detection using 
a deep neural network. Rathore et al. [47] introduced a hybrid 
approach involving wavelet analysis and artificial neural 
networks to detect, classify, and locate faults in a STATCOM-
compensated system. Assadi et al. [48] presented an adaptive 

fault classification method using artificial neural networks. 
Meanwhile, Huang et al. [49] enhanced fault diagnosis with a 
method combining variational modal decomposition and 
support vector machines optimized using a whale algorithm. 
Coban and Tezcan [50] proposed two separate SVM models to 
detect and classify faults, simulating various fault scenarios 
on a 154 kV transmission line. 

4.4 Fault detection and protection relay techniques in 
transmission lines 
Biswas and Nayak [51] focused on how transmission 

lines impact the operation of distance relays by detecting 
faults through changes in positive-sequence current 
magnitude. Gupta et al. [52] developed a relay system that 
identifies and classifies faults by synchronously measuring 
three-phase currents at two different bus locations. 
Elmitwally and Ghanem [53] introduced a method based on a 
reverse synchronous reference frame, which quickly detects 
and classifies faults using only the three-phase current at the 
relay site. Abo-Hamad et al. [54] proposed a relay design that 
initiates fault detection using an impedance index and 
identifies the fault zone by comparing faulted loop currents 
with thyristor-controlled series capacitor (TCSC) terminal 
currents. Alabbawi et al. [55] presented an intelligent relay 
capable of distinguishing ground faults from non-ground 
faults by analyzing three-phase currents and zero-current 
characteristics. Al Kazzaz et al. [56] employed an adaptive 
neuro-fuzzy inference system (ANFIS) to design a distance 
relay that detects faults by monitoring phase-wise voltage 
and current signals. 

4.5 Fault detection methods in transmission lines using 
the summation of squared currents and moving 
average techniques 
Yamuna and Thresia [57,58] as well as Jarrahi et al. [59] 

presented similar fault detection methods for transmission 
lines, utilizing the summation of squared three-phase 
currents (SSC) combined with a moving average approach. 
These methods rely on specific fault detection criteria (FDC) 
to identify faults by monitoring current changes and applying 
a smoothing technique to enhance detection reliability. 

4.6 Fault detection and indexing techniques for 
transmission lines 
Jalilian et al. [60] examined how the integration of 

inverter-based resources (IBRs) influences distance 
protection by calculating the average zero-sequence current 
and its superimposed component. Mondal et al. [61] and 
Kulshrestha et al. [62] developed fault detection methods 
based on fault indices, with Mondal simulating a 400 kV 9-bus 
IEEE system in EMTP to capture fault current data at a single 
line end, while Kulshrestha introduced a classification 
algorithm for network faults. Mukherjee et al. [63] proposed 
a technique using entropy analysis to detect faults by 
observing transient high-frequency current oscillations 
immediately after the fault. Fahim et al. [64] presented an 
unsupervised framework for detecting and categorizing faults 
in transmission systems incorporating superconducting fault 
current limiters (SFCLs). 

4.7 Simulation of a transmission line on 
matlab/simulink and pscad 
Aker et al. [65], Chunguo and Junjie [66], and Naik and 

Koley [67] worked on fault detection approaches for high-
voltage transmission systems using different simulation 
techniques. Aker et al. modeled a power network in 
MATLAB/Simulink, introducing faults in various system 



SS. Xulu et al. /Future Technology                                                                                          February 2025| Volume 04 | Issue 01 | Pages 12-22 

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zones and employing classifiers to determine fault types. 
Chunguo and Junjie created a fault dataset by simulating a 
high-voltage power line in MATLAB for diagnostic analysis. 
Naik and Koley applied the K-nearest neighbor (KNN) 
algorithm to a 500 kV AC/DC transmission line integrated 
with a doubly fed induction generator (DFIG) and analyzed 
multiple fault scenarios through MATLAB simulations. Nale et 
al. [68] proposed a fault protection method for a 400 kV, 50 
Hz system and tested its performance using PSCAD, while 
Akhikpemelo et al. [69] applied a feed-forward neural 
network with a backpropagation algorithm for fault 
identification also utilizing PSCAD for system modeling. 

4.8 Other fault detection, classification, and location 
techniques in transmission lines 
Zakri et al. [70] introduced a fault diagnosis approach for 

wide-area systems using Phasor Measurement Units (PMUs), 
focusing on detecting three-phase short-circuit faults. Ghaedi 
et al. [71] further explored fault location accuracy by 
assessing the influence of measurement errors in PMUs and 
instrument transformers. Mukherjee et al. [72] utilized 
Poincaré-based correlation analysis, where fault signals were 
divided into equal time segments to compute correlation 
coefficients for identifying transmission line faults. Patel [73] 
employed Lissajous figures for fault detection and 
classification during power swings, with a fault index derived 
from the quarter-cycle moving window sum of the Euclidean 
norm. Tatar et al. [74] proposed a fault distance detection 
technique using Field Programmable Gate Arrays (FPGA) and 
implemented it through Xilinx Vivado Design Suite. Srivastava 
et al. [75] validated a transmission line protection model 
using an experimental setup of a scaled-down power system 
consisting of transmission lines, transformers, and loads. 
Andanapalli et al. [76] presented a fault detection and 
classification method for two-terminal long transmission 
lines using a fundamental phasor-based approach. Fahim et 
al. [77] designed an unsupervised fault detection and 
classification framework utilizing an enhanced capsule 
network with sparse filtering. Mishra et al. [78] developed a 
cross-differential protection strategy aimed at improving the 
reliability of parallel transmission lines with thyristor-
controlled series capacitors (TCSCs). 

4.9 Trends and analysis 
4.9.1 Network visualization (gaps) 

 Network visualization helps to analyze the gaps that 
are present in the project by comparing the total link strength, 
the number of links, and the number of occurrences for that 
certain keyword from cluster to cluster. Table 2 is the 
representation of clusters obtained from VOSviewer, from 
these clusters, it is observed that cluster 1 has the highest 
total link strength of 619 and the highest number of 
occurrences of 87 on the keyword fault detection, this means 
that there have been many publications based on this 
keyword and many authors were more interested in 
researching about it. Figure 3 illustrates the network 
visualization obtained from VOSviewer software when all the 
documents exported to Excel are copied to the software for 
the analysis of the gaps and trends of the project being 
reviewed. In this figure, bullets that are much bigger than the 
others symbolize that the topic or keywords have been 
researched more, and therefore, there is no need to dwell 
much on it; only focus more on smaller rounds and fewer links 
between them.  

 

Table 2. Clustering of keywords in fault detection, classification, and 
location for transmission lines 

Cluster 1 

Keywords Links 
Total 
link 
strength 

Occurrences 

Distance Protection 22 48 9 
Electric fault currents 51 221 27 
Electric lines 55 526 66 
Fault detection 55 619 87 

Fault identifications 30 77 8 

Fault inception angles 22 44 6 
Matlab 47 219 24 
Power swings 14 22 5 

Series compensation 23 43 5 

Software testing 26 46 6 
Three phase faults 30 64 8 

Three phase currents 37 110 15 

Timing circuit 28 61 8 
Transmission line 37 94 11 
Transmission system 26 46 6 
Wavelet transforms 23 42 5 

Cluster 2 

Keywords Links 
Total 
link 
strength 

Occurrences 

discrete wavelet 
transforms 

33 90 11 

distance relay 21 25 6 
electric load flow 28 61 7 
Electric power system 
protection 

52 240 25 

Electric power 
transmission networks 

45 184 20 

Fault-detection-and 
classification 

43 143 18 

overhead transmission 
lines 

25 40 5 

power system protection 31 53 6 
power transmission lines 38 75 8 

protection schemes 26 55 7 

relay protection 31 60 7 

signal reconstruction 25 56 7 

transmission line 
protection 

43 114 14 

wavelet transform 23 37 5 
Cluster 3 

Keywords Links 
Total 
link 
strength 

Occurrences 

Electric power 
transmission 

55 415 46 

Failure analysis 25 46 5 

Fault diagnosis 24 45 5 
Mean square error 23 43 5 
Power 31 66 6 

Power system 20 33 6 

Power transmission 29 56 5 

Support vector machines 30 49 5 
transmission 40 100 12 

Cluster 4 

keywords links 
Total 
link 
strength 

occurrences 

Artificial neural network 23 54 6 

Back propagation 30 76 7 
Electric fault location 27 60 7 
Fault location 26 54 7 
Faults detection 48 194 21 



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Location 27 64 8 
Neural networks 35 105 11 

Transmission lines 22 37 8 

Transmission-line 47 232 
24 
 
 

 
Cluster 5 

keywords links 
Total 
link 
strength 

occurrences 

Deep learning 23 34 5 
Electric grounding 39 95 13 
Fault classification 53 239 30 

Learning system 24 51 6 
Machine learning 24 57 6 
Machine-learning 22 49 5 

Signal processing 21 40 5 

Transmission line faults 44 129 16 

 

4.9.2 Overlay visualization (trends) 
The overlay visualization is the diagram found after 

using the keywords on VOSviewer. This diagram shows 
development based on the topic being researched. It will 
show in years how long the topic has been trending and the 
most focus areas when the research was conducted.  

 

 

Figure 4 represents the overlay visualization, based on this 
project, it is evident that from the year 2020 May up to the 
year 2021 May, the researchers started to focus more on the 
issue of fault detection in transmission lines and started to 
find simpler and efficient ways to do this, but as the time goes 
on the focus was more on backpropagation and transmission 
line behavior, this was in the year of 2022. It is clear from the 
diagram that not much research was conducted on keywords 
like wavelet transforms and software testing. 

5. Literature review analysis observations 

The literature covers various methodologies and 
techniques employed for fault detection and classification in 
transmission lines. A key noticeable aspect across the studies 
is the utilization of diverse signal processing and machine 
learning techniques to analyze voltage and current signals for 
identifying faults. Methods such as wavelet transforms, 
Fourier transforms, neural networks, and pattern recognition 
algorithms are applied to detect faults, classify fault types, and 
even locate faults within the transmission network. These 
approaches aim to enhance the reliability and efficiency of 
protection systems by swiftly identifying and responding to 
faults, thereby ensuring the stability and integrity of the 
power grid. 

 

 

 

 

Figure 3. Keyword network visualization for fault detection in three-phase transmission lines 



SS. Xulu et al. /Future Technology                                                                                          February 2025| Volume 04 | Issue 01 | Pages 12-22 

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6. Conclusion  

The aim of this paper was to conduct a systematic 
literature review focusing on techniques and algorithms for 
swift and precise fault detection and protection in 
transmission lines. The methodology included the collection 
of relevant papers, a filtering process, eligibility identification, 
synthesis, and trend analysis. This process was facilitated 
using the Scopus database and VOSviewer software. The 
results of this survey revealed several key aspects across the 
studies, including the utilization of diverse signal processing 
and machine learning techniques to analyze voltage and 
current signals for identifying faults. This work contributed 
by reviewing recent advances in signal processing and 
analysis methods to enhance fault detection speed and 
accuracy, exploring the use of machine learning and neural 
networks in fault detection models, investigating advanced 
relay technologies and protection schemes, evaluating 
statistical techniques for fault isolation, and examining 
indexing techniques and evolutionary programming tools for 
precise fault identification. It also proposed future research 
directions. Based on the results obtained from the VOSviewer 
software, it is evident that keywords like, power swing, series 
compensation, software testing, wavelet transform, overhead 
transmission lines, distance relay, failure analysis, fault 
diagnosis, mean square error, power transmission, support 
vector machines, artificial neural network, deep learning, 
machine learning and signal processing are the least 
researched topics. This means that in the future, more 
research can still be done; this is confirmed by the number of 
occurrences in each keyword mentioned above. However, 
considering the increasing adoption of electric vehicles (EVs) 
and their impact on the power grid, the integration of electric 
vehicles into transmission line fault detection and 
classification systems could be a promising future research 
area. Here are some specific aspects to consider: 

 
 
 
 

• Research could investigate how the presence of electric 
vehicles, especially during charging or discharging, affects 
the voltage and current signals on transmission lines.  

• Beyond traditional voltage and current measurements, 
future research could explore integrating data from electric 
vehicle charging stations or smart charging infrastructure.  

• Research could focus on developing fault detection and 
classification systems that consider the presence and 
behavior of electric vehicle fleets within the power grid.  

• Investigating bidirectional communication between the 
power grid and electric vehicles could enable advanced 
fault management strategies. 

• Vehicle-to-grid (V2G) technologies enable electric vehicles 
to provide grid services, including ancillary services during 
fault events.  

• Given the dynamic nature of electric vehicle behavior and 
their potential impact on grid dynamics during fault events, 
future research could focus on validating fault detection 
and classification algorithms under various scenarios 
involving EV integration.   

Acknowledgments 
The authors sincerely acknowledge the support provided by 
the University of South Africa, located at 28 Pioneer Ave, 
Florida Park, Roodepoort, 1709, South Africa, which made 
this research project possible. 

Ethical issue 
The authors are aware of and comply with best practices in 
publication ethics, specifically with regard to authorship 
(avoidance of guest authorship), dual submission, 
manipulation of figures, competing interests, and compliance 
with policies on research ethics. The authors adhere to 
publication requirements that the submitted work is original 
and has not been published elsewhere. 

Figure 4. Research trends in fault detection for three-phase transmission lines 



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19 

 

Data availability statement 
The datasets analyzed during the current study are available 

and can be given upon reasonable request from the 

corresponding author. 

Conflict of interest 

The authors declare no potential conflict of interest. 

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