An implementation of a hybrid intelligent tool for distribution system fault diagnosis

Journal Article · Tue Apr 01 04:00:00 EST 1997 · IEEE Transactions on Power Delivery

DOI:https://doi.org/10.1109/61.584434· OSTI ID:492223

Momoh, J A; Dias, L G ^[1]; Laird, D N ^[2]

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1. Howard Univ., Washington, DC (United States). Dept. of Electrical Engineering
2. Los Angeles Dept. of Water and Power, CA (United States)

The common fault in distribution systems due to line outages consists of single-line-to-ground (SLG) faults, with low or high fault impedance. The presence of arcing is commonplace in high impedance SLG faults. Recently, artificial intelligence (AI) based techniques have been introduced for low/high impedance fault diagnosis in ungrounded distribution systems and high impedance fault diagnosis in grounded distribution systems. So far no tool has been developed to identify, locate and classify faults on grounded and ungrounded systems. This paper describes an integrated package for fault diagnosis in either grounded or ungrounded distribution systems. It utilizes rule based schemes as well as artificial neural networks (ANN) to detect, classify and locate faults. Its application on sample test data as well as field test data are reported in the paper.

• Sponsoring Organization: USDOE, Washington, DC (United States)
• OSTI ID: 492223
• Report Number(s): CONF-9609171--
• Journal Information: IEEE Transactions on Power Delivery, Journal Name: IEEE Transactions on Power Delivery Journal Issue: 2 Vol. 12; ISSN 0885-8977; ISSN ITPDE5
• Country of Publication: United States
• Language: English

Related Subjects

24 POWER TRANSMISSION AND DISTRIBUTION
ARTIFICIAL INTELLIGENCE
DETECTION
ELECTRICAL FAULTS
NEURAL NETWORKS
POWER DISTRIBUTION SYSTEMS
SYSTEM FAILURE ANALYSIS