Open AccessFault line detection method based on the improved SVD de‐noising and ideal clustering curve for distribution networks
Author(s) -
Wei Xiangxiang,
Wen Boying,
Yang Dechang,
Wang Bin,
Wang Xiaowei,
Yang Xuefei,
Gao Jie
Publication year - 2018
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/iet-smt.2017.0163
Subject(s) - singular value decomposition , cluster analysis , baseband , ideal (ethics) , algorithm , correctness , fault detection and isolation , mathematics , computer science , artificial intelligence , philosophy , epistemology , actuator , computer network , bandwidth (computing)
This study proposes an adaptive fault line detection method based on the improved singular value decomposition (SVD) de‐noising and ideal clustering curve for distribution networks. First, the improved SVD algorithm is introduced to obtain the pure transient zero sequence current. Then, the fast Fourier transformation is employed to analyse the baseband signal and calculate the phase differences. After that, if the difference values are larger than the set threshold, the detection method based on the improved SVD and ideal clustering curve of baseband components is proposed; if not, the 1/4 cycle damping non‐periodic components are rearranged, then based on the first half waveform extreme values and the rearranging damping non‐periodic components, the detection method based on the ideal clustering curve of damping components is introduced. The simulation results prove the correctness of proposed selection method. After comparing with the existing methods, the advantages of proposed method are confirmed.