Statistical Feature Extraction for Fault Locations in Nonintrusive Fault Detection of Low Voltage Distribution Systems

Energies

Nguyen Viet Linh¹  Hsueh-Hsien Chang² 

[1] Department of Electrical, Electronic, Computers and Systems Engineering, University of Oviedo, Oviedo 33003, Spain;Department of Electronic Engineering, JinWen University of Science and Technology, New Taipei 23154, Taiwan;

关键词: artificial intelligence (AI);    wavelet transform;    non-intrusive fault monitoring (NIFM);    distribution systems;    feature extraction;   

DOI  :  10.3390/en10050611

来源: DOAJ

【 摘 要 】

This paper proposes statistical feature extraction methods combined with artificial intelligence (AI) approaches for fault locations in non-intrusive single-line-to-ground fault (SLGF) detection of low voltage distribution systems. The input features of the AI algorithms are extracted using statistical moment transformation for reducing the dimensions of the power signature inputs measured by using non-intrusive fault monitoring (NIFM) techniques. The data required to develop the network are generated by simulating SLGF using the Electromagnetic Transient Program (EMTP) in a test system. To enhance the identification accuracy, these features after normalization are given to AI algorithms for presenting and evaluating in this paper. Different AI techniques are then utilized to compare which identification algorithms are suitable to diagnose the SLGF for various power signatures in a NIFM system. The simulation results show that the proposed method is effective and can identify the fault locations by using non-intrusive monitoring techniques for low voltage distribution systems.

【 授权许可】

Unknown