DC Series Arc Fault Diagnosis Scheme Based on Hybrid Time and Frequency Features Using Artificial Learning Models
Author:
Affiliation:
1. School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
2. Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762, USA
Abstract
Funder
National Research Foundation of Korea (NRF) funded by the Korea government
Publisher
MDPI AG
Subject
Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Control and Optimization,Mechanical Engineering,Computer Science (miscellaneous),Control and Systems Engineering
Link
https://www.mdpi.com/2075-1702/12/2/102/pdf
Reference35 articles.
1. A Series Arc Fault Diagnosis Method in DC Distribution Systems Based on Multiscale Features and Random Forests;Yin;IEEE Sens. J.,2022
2. A Deep Learning Approach for Series DC Arc Fault Diagnosing and Real-Time Circuit Behavior Predicting;Xing;IEEE Trans. Electromagn. Compat.,2021
3. Development of Nottingham Arc Model for DC Series Arc Modeling in Photovoltaic Panels;Jalil;IEEE Trans. Ind. Electron.,2021
4. Yao, X., Herrera, L., and Wang, J. (2015, January 15–19). Impact evaluation of series dc arc faults in dc microgrids. Proceedings of the 2015 IEEE Applied Power Electronics Conference and Exposition (APEC), Charlotte, NC, USA.
5. Yao, X. (2016, January 18–22). Study on DC arc faults in ring-bus DC microgrids with constant power loads. Proceedings of the 2016 IEEE Energy Conversion Congress and Exposition (ECCE), Milwaukee, WI, USA.
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