A Negative Selection Algorithm-Based Identification Framework for Distribution Network Faults With High Resistance

• Published in: IEEE access Vol. 7; pp. 109363 - 109374
• Main Authors: Song, Xiaohui, Gao, Fei, Chen, Zhenning, Liu, Wenjing
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Classification algorithms; Fault classification; Fault detection; Fault diagnosis; Feature extraction; HHT; High impedance; High resistance; high resistance fault identification; Hilbert transformation; Impedance; improved negative selection algorithm; Neural networks; Power lines; Resistance; Signal processing algorithms; small samples; Transforms; Wavelet analysis
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2933566

Most high resistance faults in distribution network are caused by overhead lines contacting with high impedance objects. It is difficult to identify the high resistance faults with the steady-state characteristics in distribution network. In this paper, a Negative Selection Algorithm (NSA) based identification framework is proposed to detect the distribution network faults with high resistance. The Hilbert-Huang transform (HHT) analysis method is used to distinguish the faults from normal state. The sum of the first two order intrinsic mode function (IMF) components of zero sequence voltage within a cycle after fault is taken as the extracted characteristic of high resistance faults. An improved negative selection method is proposed to increase detection rate and realize the classification of abnormal states, so that normal training samples and a few fault samples can generate enough detector sets with higher coverage of non-self set area. Based on a 10 kV distribution network, the performance of the proposed identification framework is evaluated. The simulation results show that, compared with the wavelet analysis and the neural network algorithm, the proposed algorithm can effectively identify the high resistance faults in distribution network with small samples.