Review on Fault Detection and Classification in Transmission Line using Machine Learning Methods

Nandhini, K; Prajith, C A

doi:10.1109/iccc57789.2023.10164876

Cited by 4 publications

(3 citation statements)

References 29 publications

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“…ML-based techniques have garnered widespread acclaim in the domain of transmission line fault classification [2,4]. ML-based methods require substantial data to effectively train classifier models.…”

Section: Introductionmentioning

confidence: 99%

“…Previous research has introduced feature extraction methodologies such as wavelet packet transformation (WT) [6,7,14,15], multiwavelet packet transformation (MWT) [13], Hilbert-Huang Transform (HHT) [8], time series imaging [10], and mathematical morphology (MM) [9]. The precise classification of faults in transmission lines through WTs necessitates dynamically adjusting their parameters according to the prevailing power system topology [4]. Additionally, selecting an appropriate mother wavelet for analysis is imperative to address specific fault scenarios.…”

Section: Introductionmentioning

confidence: 99%

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Transmission Line Fault Classification Using Conformer Convolution-Augmented Transformer Model

Lee,

Huang,

Chang

et al. 2024

Applied Sciences

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Ensuring a consistently reliable power supply is paramount in power systems. Researchers are engaged in the pursuit of categorizing transmission line failures to design countermeasures for mitigating the associated financial losses. Our study employs a machine learning-based methodology, specifically the Conformer Convolution-Augmented Transformer model, to classify transmission line fault types. This model processes time series input data directly, eliminating the need for expert feature extraction. The training and validation datasets are generated through simulations conducted on a two-terminal transmission line, while testing is conducted on historical data consisting of 108 events that occurred in the Taiwan power system. Due to the limited availability of historical data, they are utilized solely for inference purposes. Our simulations are meticulously designed to encompass potential faults based on an analysis of historical data. A significant aspect of our investigation focuses on the impact of the sampling rate on input data, establishing that a rate of four samples per cycle is sufficient. This suggests that, for our specific classification tasks, relying on lower frequency data might be adequate, thereby challenging the conventional emphasis on high-frequency analysis. Eventually, our methodology achieves a validation accuracy of 100%, although the testing accuracy is lower at 88.88%. The discrepancy in testing accuracy can be attributed to the limited information and the small number of historical events, which pose challenges in bridging the gap between simulated data and real-world measurements. Furthermore, we benchmarked our method against the ELM model proposed in 2023, demonstrating significant improvements in testing accuracy.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Transmission Line Fault Classification Using Conformer Convolution-Augmented Transformer Model

Lee,

Huang,

Chang

et al. 2024

Applied Sciences

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“…Fault identification in transmission lines is critical because it has the ability to create power interruptions and extend power outages. This is especially important given the increasing industrialization and electricity consumption that has resulted in a more complex power system network [11]. Advanced techniques like machine learning and deep learning have been found to considerably increase the accuracy and speed of fault identification [12].…”

Section: Introductionmentioning

confidence: 99%

Advancements in Fault Detection Techniques for Transmission Lines: A Literature Review

Arboleda,

Daang,

Omas-as

2024

Preprint

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This literature review delves into the realm of fault detection techniques for transmission lines, aiming to provide a comprehensive overview of recent advancements and key trends in the field. Employing a structured approach, the review synthesizes a plethora of research spanning from 2019 to 2024, sourced from diverse databases including IEEE Xplore, ScienceDirect, ResearchGate, Scopus, Litmaps, and Google Scholar. The methodology encompasses a systematic literature search protocol, stringent inclusion and exclusion criteria, meticulous data extraction, and a multi-dimensional analysis framework. The literature review uncovers a spectrum of fault detection methodologies, ranging from traditional signal processing techniques like Discrete Wavelet Transform and phase angle-based methods to cutting-edge deep learning algorithms such as Capsule Networks and Convolutional Neural Networks. Insights gleaned from the review underscore the critical importance of fault detection in maintaining the reliability, safety, and efficiency of power grids, highlighting its role as a frontline defense against widespread outages and equipment damage. Key findings from the review shed light on the efficacy of different fault detection approaches, showcasing their strengths and limitations across diverse system conditions. Furthermore, the review identifies common trends and challenges, including the need for real-world validation, scalability, adaptability to various network configurations, and cybersecurity considerations. This literature provides valuable insights and recommendations for future research endeavors in fault detection for transmission lines. By embracing advancements in both traditional and emerging techniques, researchers can continue to enhance the resilience and dependability of power transmission systems, ensuring their ability to withstand evolving challenges and safeguard critical infrastructure.

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Short Transmission Line Fault Identification and Classification Using Supervised Machine Learning

Akther,

Khanam,

Hossain

et al. 2024

2024 3rd International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE)

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Review on Fault Detection and Classification in Transmission Line using Machine Learning Methods

Cited by 4 publications

References 29 publications

Transmission Line Fault Classification Using Conformer Convolution-Augmented Transformer Model

Transmission Line Fault Classification Using Conformer Convolution-Augmented Transformer Model

Advancements in Fault Detection Techniques for Transmission Lines: A Literature Review

Short Transmission Line Fault Identification and Classification Using Supervised Machine Learning

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