High-sensitivity vegetation high-impedance fault detection based on signal's high-frequency contents

Douglas P. S. Gomes, Cagil Ozansoy, Anwaar Ul-Haq

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

High-impedance faults (HIFs) are linked to enduring unaddressed knowledge gaps due to their diverse and complex behavior, despite being extensively researched disturbances. Vegetation HIFs, for instance, are a particular type of fault that can lead to great fire hazards and life risks. They have unique fault signatures and should receive special attention if fire risk mitigation is desired. This paper focuses on the detection of these distinct, very small current faults. As the main correlational features, the proposed methodology uses the vegetation fault signatures' high-frequency content. Different from many previous works that rely on HIF models, the approach validation is performed using a real dataset comprising a large number of experiments, sampled in a functioning network in the presence of noise. The classification is performed by boosted decision trees, which showed high dependability and security in the classification of small phase-to-earth and phase-to-phase HIFs.

Original languageEnglish
Pages (from-to)1398-1407
Number of pages10
JournalIEEE Transactions on Power Delivery
Volume33
Issue number3
DOIs
Publication statusPublished - Jun 2018

Fingerprint Dive into the research topics of 'High-sensitivity vegetation high-impedance fault detection based on signal's high-frequency contents'. Together they form a unique fingerprint.

  • Cite this