Enhancing Power Grid Reliability With Machine Learning and Auxiliary Classifier Generative Adversarial Networks: A Study on Fault Detection Using the Georgia Electric System Load Dataset

Enhancing Power Grid Reliability With Machine Learning and Auxiliary Classifier Generative Adversarial Networks: A Study on Fault Detection Using the Georgia Electric System Load Dataset

Power networks are vital to society, yet service outages and faults can have devastating consequences. This study introduces a novel integration of machine learning and data augmentation techniques for fault detection and classification, addressing gaps in data diversity and imbalance. Unlike tradit...

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Bibliographic Details
Main Authors: Hafeez Ur Rehman Siddiqui, Robert Brown, Adil Ali Saleem, Muhammad Amjad Raza, Sandra Dudley
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
ACGAN
grid event signature library
smart grid
fault tolerance
Machine Learning
spectral features
Online Access:https://ieeexplore.ieee.org/document/10818412/
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https://ieeexplore.ieee.org/document/10818412/

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