Deep Learning based Fault Detection in Power Transmission Lines

Kumar, Tapan; Meena, RadheyShyam; Mani, P.K; Ramya, S.; Khandan, K. Lakshmi; Mohammed, Arshad; Ramkumar, M. Siva

doi:10.1109/icirca54612.2022.9985700

“…Subsequently, CNN is employed to extract sample features, and the extracted features are inputted into LSTM for prediction. In recent work, [19] two Deep Learning (DL) networks are utilised for the fault classification task, the Artificial Neural Network (ANN) and the Convolutional Neural Network (CNN). Both models undergo validation based on accuracy and loss metrics.…”

Section: Related Workmentioning

confidence: 99%

Comparative assessment of fault detection methods in transmission line during power swing: analyzing single and hybrid networks for the southern Kerala grid

K,

Ramesh,

Das

et al. 2024

Eng. Res. Express

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The blocking function of power oscillation is a highly eﬀective feature found in distance relays. Its purpose is to prevent unintentional tripping of transmission lines. It is crucial to accurately diﬀerentiate between faults and power oscillations in order to minimise the occurrence of widespread power outages and monetary losses. Therefore, the detection and classiﬁcation of faults arising from power swings are critical challenges in ensuring the smooth operation and overall health of transmission lines, particularly in extrahigh-voltage (EHV) and ultra-high-voltage (UHV) power systems. Modern methods rely on machine learning methods which are in their infancy. The paper proposes to identify various fault conditions in transmission lines during power swing by using a hybrid Neural Network architecture. In this research, diﬀerent designs such as 2-D convolution neural network (2D-CNN), 1-D convolution neural network (1D-CNN), long short-term memory (LSTM), and CNN-LSTM hybrid networks are investigated to understand the eﬀect of various deep learning approaches. Along with the southern Kerala Grid, the standard, 9-bus system is selected as a test case for examining various fault circumstances of the system during power swing. Multiple tests were conducted to determine the optimal deep learning architecture, including model parameters and conﬁgurations, for accurate fault detection.

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“…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]. When applied to high and medium voltage networks, these approaches have shown great promise and efficiency in fault detection [10].…”

Section: Introductionmentioning

confidence: 99%

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

Daang,

Omas-as,

Arboleda

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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“…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]. When applied to high and medium voltage networks, these approaches have shown great promise and efficiency in fault detection [10].…”

Section: Introductionmentioning

confidence: 99%

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

Arboleda,

Daang,

Omas-as

2024

Preprint

0

View full text Add to dashboard Cite

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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Deep Learning based Fault Detection in Power Transmission Lines

Cited by 8 publications

References 8 publications

Comparative assessment of fault detection methods in transmission line during power swing: analyzing single and hybrid networks for the southern Kerala grid

Comparative assessment of fault detection methods in transmission line during power swing: analyzing single and hybrid networks for the southern Kerala grid

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

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

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