SS. Xulu et al. /Future Technology February 2025| Volume 04 | Issue 01 | Pages 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 https://fupubco.com/futech 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 14 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 15 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 16 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 SS. Xulu et al. /Future Technology February 2025| Volume 04 | Issue 01 | Pages 12-22 17 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 18 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 SS. Xulu et al. /Future Technology February 2025| Volume 04 | Issue 01 | Pages 12-22 19 Data availability statement The datasets analyzed during the current study are available and can be given upon reasonable request from the corresponding author. 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