Fault Location after Fault Classification in Transmission Line Using Voltage Amplitudes and Support Vector Machine

Fei, Chunguo; Qin, Junjie

doi:10.3103/s1068371221020048

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…The system adjusts depth to balance generalization and training set performance [26]. Gini index, entropy, and CART determination analyze points [27,28]. Random Forest divides the dataset into training data (the "in bag" data) and validation data (the "out of the bag" data) to detect power system problem characteristics [29,30].…”

Section: The Process Of Data Generation and Simulation For T/l With A...mentioning

confidence: 99%

Improved Fault Classification and Localization in Power Transmission Networks Using VAE-Generated Synthetic Data and Machine Learning Algorithms

Khan,

Asad,

Vaimann

et al. 2023

Machines

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The reliable operation of power transmission networks depends on the timely detection and localization of faults. Fault classification and localization in electricity transmission networks can be challenging because of the complicated and dynamic nature of the system. In recent years, a variety of machine learning (ML) and deep learning algorithms (DL) have found applications in the enhancement of fault identification and classification within power transmission networks. Yet, the efficacy of these ML architectures is profoundly dependent upon the abundance and quality of the training data. This intellectual explanation introduces an innovative strategy for the classification and pinpointing of faults within power transmission networks. This is achieved through the utilization of variational autoencoders (VAEs) to generate synthetic data, which in turn is harnessed in conjunction with ML algorithms. This approach encompasses the augmentation of the available dataset by infusing it with synthetically generated instances, contributing to a more robust and proficient fault recognition and categorization system. Specifically, we train the VAE on a set of real-world power transmission data and generate synthetic fault data that capture the statistical properties of real-world data. To overcome the difficulty of fault diagnosis methodology in three-phase high voltage transmission networks, a categorical boosting (Cat-Boost) algorithm is proposed in this work. The other standard machine learning algorithms recommended for this study, including Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF), and K-Nearest Neighbors (KNN), utilizing the customized version of forward feature selection (FFS), were trained using synthetic data generated by a VAE. The results indicate exceptional performance, surpassing current state-of-the-art techniques, in the tasks of fault classification and localization. Notably, our approach achieves a remarkable 99% accuracy in fault classification and an extremely low mean absolute error (MAE) of 0.2 in fault localization. These outcomes represent a notable advancement compared to the most effective existing baseline methods.

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Section: The Process Of Data Generation and Simulation For T/l With A...mentioning

confidence: 99%

Improved Fault Classification and Localization in Power Transmission Networks Using VAE-Generated Synthetic Data and Machine Learning Algorithms

Khan,

Asad,

Vaimann

et al. 2023

Machines

View full text Add to dashboard Cite

show abstract

“…When a fault occurs, the power transmission line's fault current becomes abnormally high, while the fault voltage falls to a low level [45].…”

Section: Data Preparation and Extractionmentioning

confidence: 99%

The Use of Artificial Neural Network for Low Latency of Fault Detection and Localisation in Transmission Line

2022

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The use of artificial neural network for low latency of fault detection and localisation in transmission line

Ogar¹,

Hussain²,

Gamage³

2023

Heliyon

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Fault Location after Fault Classification in Transmission Line Using Voltage Amplitudes and Support Vector Machine

Cited by 4 publications

References 26 publications

Improved Fault Classification and Localization in Power Transmission Networks Using VAE-Generated Synthetic Data and Machine Learning Algorithms

Improved Fault Classification and Localization in Power Transmission Networks Using VAE-Generated Synthetic Data and Machine Learning Algorithms

The Use of Artificial Neural Network for Low Latency of Fault Detection and Localisation in Transmission Line

The use of artificial neural network for low latency of fault detection and localisation in transmission line

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