Controlled Fault Current Interruption Scheme for Improved Fault Prediction Accuracy

To enhance the accuracy and efficiency of controlled fault current interruption (CFI) in short-circuit current processing within power systems, a half-cycle elimination prediction algorithm and a double-sampling CFI sequence method are proposed in this study. By analyzing the non-periodic and period...

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Main Authors: Xu Yang, Qi Long, Hao Li, Dachao Huang, Shupeng Xue, Jiajie Huang, Hongzhang Liang, Xiongying Duan
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Applied Sciences
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Online Access:https://www.mdpi.com/2076-3417/15/6/3106
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author Xu Yang
Qi Long
Hao Li
Dachao Huang
Shupeng Xue
Jiajie Huang
Hongzhang Liang
Xiongying Duan
author_facet Xu Yang
Qi Long
Hao Li
Dachao Huang
Shupeng Xue
Jiajie Huang
Hongzhang Liang
Xiongying Duan
author_sort Xu Yang
collection DOAJ
description To enhance the accuracy and efficiency of controlled fault current interruption (CFI) in short-circuit current processing within power systems, a half-cycle elimination prediction algorithm and a double-sampling CFI sequence method are proposed in this study. By analyzing the non-periodic and periodic components of short-circuit currents, the half-cycle elimination method and fast Fourier transform are utilized to compute these two components, respectively. The double-sampling CFI sequence approach is designed to fully utilize the response and waiting times of relay protection. Following the first sampling to estimate the target zero-crossing point, the remaining response and waiting times are allocated for a second sampling and recalculation to enhance the precision of zero-crossing prediction. MATLAB R2023a is employed to conduct multi-scenario simulations, and the algorithm’s performance is evaluated using actual recorded waveform data. The results demonstrate that the proposed algorithm accurately predicts the target zero-crossing point after a short circuit, with a computational error of less than 0.2 ms. Furthermore, the double-sampling sequence method is shown to improve the accuracy of open-circuit zero-crossing point calculations by an order of magnitude. This work provides a novel technical approach for the fast and precise handling of short-circuit faults in power systems.
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issn 2076-3417
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spelling doaj-art-ff3a4e5ba5cf4b8b8df7df51cd8b845f2025-08-20T02:11:18ZengMDPI AGApplied Sciences2076-34172025-03-01156310610.3390/app15063106Controlled Fault Current Interruption Scheme for Improved Fault Prediction AccuracyXu Yang0Qi Long1Hao Li2Dachao Huang3Shupeng Xue4Jiajie Huang5Hongzhang Liang6Xiongying Duan7Electric Power Research Institute of EHV Power Transmission Company China Southern Power Grid, Guangzhou 510663, ChinaEHV Power Transmission Company China Southern Power Grid, Guangzhou 510663, ChinaKunming Bureau of UHV Transmission Company of China Southern Power Grid Co., Ltd., Kunming 510663, ChinaElectric Power Research Institute of EHV Power Transmission Company China Southern Power Grid, Guangzhou 510663, ChinaElectric Power Research Institute of EHV Power Transmission Company China Southern Power Grid, Guangzhou 510663, ChinaElectric Power Research Institute of EHV Power Transmission Company China Southern Power Grid, Guangzhou 510663, ChinaSchool of Electrical Engineering, Dalian University of Technology, Dalian 116024, ChinaSchool of Electrical Engineering, Dalian University of Technology, Dalian 116024, ChinaTo enhance the accuracy and efficiency of controlled fault current interruption (CFI) in short-circuit current processing within power systems, a half-cycle elimination prediction algorithm and a double-sampling CFI sequence method are proposed in this study. By analyzing the non-periodic and periodic components of short-circuit currents, the half-cycle elimination method and fast Fourier transform are utilized to compute these two components, respectively. The double-sampling CFI sequence approach is designed to fully utilize the response and waiting times of relay protection. Following the first sampling to estimate the target zero-crossing point, the remaining response and waiting times are allocated for a second sampling and recalculation to enhance the precision of zero-crossing prediction. MATLAB R2023a is employed to conduct multi-scenario simulations, and the algorithm’s performance is evaluated using actual recorded waveform data. The results demonstrate that the proposed algorithm accurately predicts the target zero-crossing point after a short circuit, with a computational error of less than 0.2 ms. Furthermore, the double-sampling sequence method is shown to improve the accuracy of open-circuit zero-crossing point calculations by an order of magnitude. This work provides a novel technical approach for the fast and precise handling of short-circuit faults in power systems.https://www.mdpi.com/2076-3417/15/6/3106short-circuit current predictionphase-controlled switching techniquehalf-period elimination methodrecursive least squares methoddouble-sampling time
spellingShingle Xu Yang
Qi Long
Hao Li
Dachao Huang
Shupeng Xue
Jiajie Huang
Hongzhang Liang
Xiongying Duan
Controlled Fault Current Interruption Scheme for Improved Fault Prediction Accuracy
Applied Sciences
short-circuit current prediction
phase-controlled switching technique
half-period elimination method
recursive least squares method
double-sampling time
title Controlled Fault Current Interruption Scheme for Improved Fault Prediction Accuracy
title_full Controlled Fault Current Interruption Scheme for Improved Fault Prediction Accuracy
title_fullStr Controlled Fault Current Interruption Scheme for Improved Fault Prediction Accuracy
title_full_unstemmed Controlled Fault Current Interruption Scheme for Improved Fault Prediction Accuracy
title_short Controlled Fault Current Interruption Scheme for Improved Fault Prediction Accuracy
title_sort controlled fault current interruption scheme for improved fault prediction accuracy
topic short-circuit current prediction
phase-controlled switching technique
half-period elimination method
recursive least squares method
double-sampling time
url https://www.mdpi.com/2076-3417/15/6/3106
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