Using machine learning algorithms for classifying transmission line faults

Ağır, Tuba Tanyıldızı

doi:10.24012/dumf.1096691

“…Another strategy in machine learning is to use ensemble classifiers [9]. These techniques use several learning algorithms so that they can produce more accurate models and avoid overfitting [43].…”

Section: Bsdt For Fault Classificationmentioning

confidence: 99%

“…Tree-based ensemble techniques are commonly used for classification and regression in many research fields [7], [8]. Two such tree-based ensemble techniques are the Boosted Decision Tree (BSDT) for classification [9] and bagged decision tree for regression problems [10].…”

Section: Introductionmentioning

confidence: 99%

IoT based Fourth Generation SCADA System for High Voltage Networks Fault Diagnosis based on BSDT-ANN

Fayyad¹,

Gawad

²

,

Saleh

³

et al. 2023

The Egyptian International Journal of Engineering Sciences and

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Smart Grid substations rely on conventional Supervisory Control and Data Acquisition (SCADA) systems for remote supervision and control. However, these systems are limited in the geographical area they cover. Recently, the Internet of Things (IoT) has paved the way for connecting a vast number of devices to the Internet, which would be effective and beneficial for power system automation and data acquisition. In this paper, an intelligent low-cost SCADA system based on IoT for transmission line fault diagnosis is proposed. In the first step of the proposed scheme, voltage and current signals at the relaying point are preprocessed and analyzed using Discrete Cosine Transform (DCT). Next, signal energy components are extracted and sent to a Boosted Decision Tree (BSDT), a reliable and fast ensemble classifier, to identify fault type and, accordingly, a specific ANN is activated to estimate fault location. The diagnosis data is sent to a microcontroller to be displayed, trip the load circuit, and allow communication with Cloud ThingSpeak platform via the ESP8266 wi-fi communication module which makes data available anywhere all over the world. The approach is applied on a real-world HV transmission line, located between Samalut and Cairo, Egypt, where more than 16000 faults cases are well tested. The results show the reliability, validity, and effectiveness of the proposed approach.

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“…Studies have demonstrated the effectiveness of various ML approaches, including artificial neural networks (ANNs), support vector machines (SVMs), decision trees, and ensemble methods, in analyzing electrical signals and identifying fault patterns. [2,3] For example, Li et al [4] utilized a deep learning-based approach for fault detection in power transmission systems, achieving high accuracy rates even in the presence of noise and disturbances. Similarly, Wong et al [5] proposed a hybrid machine learning model combining convolutional neural networks (CNNs) and long short-term memory (LSTM) networks for fault diagnosis in power systems, showcasing significant improvements in fault detection performance compared to traditional methods.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Advances in Transmission Line Fault Detection: A Literature Review

SARMIENTO,

Delfino,

Arboleda

2024

Preprint

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Fault detection in transmission lines plays a role in maintaining the dependability and steadiness of power networks. Traditional methods for identifying faults often struggle to handle the diverse nature of real world fault situations. Machine learning (ML) algorithms offer a data centered approach that can adjust and learn from datasets potentially overcoming the limitations of traditional approaches. This document presents a review of progress in using ML for detecting faults in transmission lines. By drawing insights from a variety of studies we explore how ML algorithms have evolved in fault detection, including techniques like networks, recurrent neural networks featuring Long Short Term Memory and convolutional neural networks. We delve into the spectrum of applications where ML is used for fault detection across fault scenarios and operational settings. Additionally we discuss the obstacles and prospects linked to putting ML based fault detection systems into practice such as challenges with data quality, model interpretability and integration with existing grid monitoring systems. Lastly we outline future research paths focused on pushing forward the boundaries of fault detection, in power transmission systems through approaches and collaborative endeavors involving academia, industry players and policymakers. In general, this review highlights how machine learning has the power to revolutionize fault detection methods enhancing the resilience and dependability of power systems.

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“…In recent years, there has been a surge in research exploring the application of machine learning algorithms for transmission line fault detection [1]. Studies have demonstrated the effectiveness of various ML approaches, including artificial neural networks (ANNs), support vector machines (SVMs), decision trees, and ensemble methods, in analyzing electrical signals and identifying fault patterns [2,3].…”

Section: Introductionmentioning

confidence: 99%

Machine learning advances in transmission line fault detection: A literature review

Judy Lhyn Porlaje Sarmiento,

Jam Cyrex De Villa Delfino,

Edwin Romeroso Arboleda

2024

Int. J. Sci. Res. Arch.

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Fault detection in transmission lines plays a role in maintaining the dependability and steadiness of power networks. Traditional methods for identifying faults often struggle to handle the diverse nature of real world fault situations. Machine learning (ML) algorithms offer a data centered approach that can adjust and learn from datasets potentially overcoming the limitations of traditional approaches. This document presents a review of progress in using ML for detecting faults in transmission lines. By drawing insights from a variety of studies we explore how ML algorithms have evolved in fault detection, including techniques like networks, recurrent neural networks featuring Long Short Term Memory and convolutional neural networks. We delve into the spectrum of applications where ML is used for fault detection across fault scenarios and operational settings. Additionally we discuss the obstacles and prospects linked to putting ML based fault detection systems into practice such as challenges with data quality, model interpretability and integration with existing grid monitoring systems. Lastly we outline future research paths focused on pushing forward the boundaries of fault detection, in power transmission systems through approaches and collaborative endeavors involving academia, industry players and policymakers. In general, this review highlights how machine learning has the power to revolutionize fault detection methods enhancing the resilience and dependability of power systems.

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Using machine learning algorithms for classifying transmission line faults

Cited by 3 publications

References 29 publications

IoT based Fourth Generation SCADA System for High Voltage Networks Fault Diagnosis based on BSDT-ANN

IoT based Fourth Generation SCADA System for High Voltage Networks Fault Diagnosis based on BSDT-ANN

Machine Learning Advances in Transmission Line Fault Detection: A Literature Review

Machine learning advances in transmission line fault detection: A literature review

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