@inproceedings{de74eef14f09471b8f1a8bb120a34e3a,
title = "Hybrid HVAC-HVDC Grid Fault Detection & Classification Using ANN",
abstract = "This paper presents a novel approach to fault detection and diagnosis in hybrid grid systems by integrating Travelling Wave (TW) analysis with Artificial Neural Networks (ANNs). The TW techniques is advocated in replacing conventional fault detection to capture fault behaviour, credited to their sensitivity to grid disturbances and ability to provide spatial information crucial for fault localization. Moreover, the adoption of ANNs is justified by their capability to handle non-linear and complex systems. This paper outlines the construction of a hybrid HVAC-HVDC grid system, focusing on a bipolar HVDC link and rectifying VSC stations. The presented ANN model is trained with TW fault data extracted from the simulation, achieving high performance in fault detection and classification with an overall accuracy of 96.25%. The implications of these findings for practical implementation in hybrid power industries reflects their contribution to stability and protection in hybrid grid systems.",
keywords = "ANN, Fault Classification, Fault Detection, HVAC, HVDC, Hybrid, Pattern Recognition, TW, VSC",
author = "Wong, {Zhe Ming} and Chew, {Ing Ming} and Wong, {W. K.} and Saaveethya Sivakumar and Juwono, {Filbert H.}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE. ; 10th International Conference on Smart Computing and Communication, ICSCC 2024 ; Conference date: 25-07-2024 Through 27-07-2024",
year = "2024",
doi = "10.1109/ICSCC62041.2024.10690637",
language = "English",
series = "2024 10th International Conference on Smart Computing and Communication, ICSCC 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "401--407",
booktitle = "2024 10th International Conference on Smart Computing and Communication, ICSCC 2024",
}