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

Engineering and Technology

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

M. A. Khan, B. Asad, et al.

This innovative research presents a groundbreaking strategy for fault classification and localization in power transmission networks by leveraging variational autoencoders to synthesize fault data. Conducted by Muhammad Amir Khan and colleagues, the study achieves an impressive 99% accuracy in fault classification and a mean absolute error of just 0.2 in fault localization, outpacing existing methods.

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Playback language: English
Abstract
This paper introduces a novel strategy for fault classification and localization in power transmission networks using variational autoencoders (VAEs) to generate synthetic data, which is then used in conjunction with machine learning algorithms. The VAE generates synthetic fault data that capture the statistical properties of real-world data, augmenting the available dataset and improving fault recognition. A CatBoost algorithm, along with SVM, Decision Trees, Random Forest, and K-Nearest Neighbors, is used for classification and localization. The approach achieves 99% accuracy in fault classification and a mean absolute error (MAE) of 0.2 in fault localization, surpassing existing state-of-the-art techniques.
Publisher
Machines
Published On
Jul 18, 2023
Authors
Muhammad Amir Khan, Bilal Asad, Toomas Vaimann, Ants Kallaste, Raimondas Pomarnacki, Van Khang Hyunh
Tags
fault classification
localization
variational autoencoders
synthetic data
machine learning
CatBoost
power transmission networks

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