Open AccessLocating short‐circuit faults in HVDC systems using automatically selected frequency‐domain features
Author(s) -
Farshad Mohammad
Publication year - 2019
Publication title -
international transactions on electrical energy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 42
ISSN - 2050-7038
DOI - 10.1002/etep.2765
Subject(s) - fault (geology) , frequency domain , fault indicator , engineering , aliasing , signal (programming language) , filter (signal processing) , estimator , computer science , voltage , electronic engineering , pattern recognition (psychology) , algorithm , artificial intelligence , fault detection and isolation , mathematics , electrical engineering , statistics , computer vision , seismology , actuator , programming language , geology
Summary In this paper, a novel fault‐location method is presented for high‐voltage direct current (HVDC) transmission lines based on the pattern recognition techniques and the machine learning strategies. In the proposed method, the voltage signal at one of the HVDC stations is stepped down via a resistive‐capacitive voltage divider (RCVD) and passed through an anti‐aliasing low‐pass active filter (LPAF). Then, the frequency spectrum is obtained by applying the discrete Fourier transform (DFT) to the postfault voltage signal. The input pattern for presenting to the fault‐location estimator is formed based on the most useful features selected from the extracted harmonic spectrum. In this paper, the regression relief (RReliefF) algorithm is utilized for automatic feature selection. Also, the random forest (RF) algorithm is used to build the fault‐location estimator founded on a group of regression decision trees. The method is applied for fault locating in a 700‐km‐long HVDC line considering various fault locations, short‐circuit resistances, prefault currents, and fault types. The obtained overall average of percentage errors in the fault‐location estimate for 1800 different unseen test cases is 0.188%, which confirms the accurate and good generalization performance of the presented method.