Open AccessDetection and classification of faults in transmission lines using the maximum wavelet singular value and Euclidean norm
Author(s) -
Guillen Daniel,
Paternina Mario Roberto Arrieta,
Zamora Alejandro,
Ramirez Juan Manuel,
Idarraga Gina
Publication year - 2015
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2014.1064
Subject(s) - singular value decomposition , singular value , wavelet , fault (geology) , discrete wavelet transform , electric power transmission , algorithm , mathematics , wavelet transform , electric power system , fault detection and isolation , norm (philosophy) , euclidean distance , computer science , pattern recognition (psychology) , control theory (sociology) , power (physics) , engineering , artificial intelligence , eigenvalues and eigenvectors , physics , control (management) , seismology , actuator , geology , quantum mechanics , law , political science , electrical engineering
In this study, a novel algorithm for detecting and classifying faults in high‐voltage transmission lines is proposed. The algorithm is based on the discrete wavelet transform (DWT) and singular value decomposition (SVD). The DWT is used for extracting the currents’ high‐frequency components under fault conditions. Signals under each fault condition are scaled in frequency, in order to build a wavelet matrix. By means of the SVD, the maximum singular value is calculated and employed in this proposal. The attained results exhibit that the maximum singular value represents a good indicator for the issue. This novel approach for detecting and classifying faults in power systems is called maximum wavelet singular value. Phase‐to‐ground, two‐phase to ground, and three‐phase faults’ simulations under different fault impedances are carried out by DIgSILENT Power Factory. The analysed fault conditions are evaluated demonstrating that the proposal reduces the computational burden and the time detection.