@inproceedings{f7b8dd2a5f164412a6de41d334061b34,
title = "A Residual Current Location Method Based on a Back Propagation Neural Network Optimized by a Genetic Algorithm",
abstract = "With the diversified development of source and load in low-voltage distribution system, the time-frequency characteristics of residual current under multi-work condition interference have strong time-variability, and the leakage fire caused by this is difficult to predict. It is also a hidden danger of electric shock. In this paper, a method of locating the residual current of low voltage distribution system based on a back propagation neural network optimized by genetic algorithm is proposed. The residual current and bus current are determined as key input signals based on the theoretical derivation of the impedance method. Through the simulation model of the direct current system, this method has been validated to accurately predict the fault distance under different single-phase grounding resistances.",
keywords = "back propagation neural network, genetic algorithm, location method, residual current",
author = "Jiaxin Liu and Silei Chen and Jing Wang and Xingwen Li",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 69th IEEE Holm Conference on Electrical Contacts, HOLM 2024 ; Conference date: 06-10-2024 Through 10-10-2024",
year = "2024",
doi = "10.1109/HOLM56222.2024.10768405",
language = "英语",
series = "Electrical Contacts, Proceedings of the Annual Holm Conference on Electrical Contacts",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Electrical Contacts 2024 - Proceedings of the 69th IEEE Holm Conference on Electrical Contacts, HOLM 2024",
}