Fault section estimation for power networks using logic cause-effect models

Wen Hui Chen; Shun Hung Tsai; Hsien I. Lin

doi:10.1109/TPWRD.2010.2093585

Fault section estimation for power networks using logic cause-effect models

Wen Hui Chen
, Shun Hung Tsai
, Hsien I. Lin

Graduate Institute of Automation Technology

Research output: Contribution to journal › Article › peer-review

54 Scopus citations

Abstract

A graphic modeling approach for fast fault section estimation via Boolean rule matrix transformations is proposed in this paper. This study extends matrix-based inference in fault diagnosis from radial distribution networks to mesh transmission networks. The proposed approach offers a clear framework, rapid reasoning, and has no problem of convergence and parameter setting during the diagnosis procedure. Furthermore, the required data used for inference can be obtained online from existing supervisory control and data-acquisition/ energy-management systems without installing dedicated measurement devices, which reduces extra hardware investment and makes it more feasible to power utilities. By testing three different types of transmission networks, the proposed approach showed some clear merits over existing methods.

Original language	English
Article number	5677624
Pages (from-to)	963-971
Number of pages	9
Journal	IEEE Transactions on Power Delivery
Volume	26
Issue number	2
DOIs	https://doi.org/10.1109/TPWRD.2010.2093585
State	Published - Apr 2011

Bibliographical note

Funding Information:
Manuscript received March 24, 2010; revised July 20, 2010; accepted November 03, 2010. Date of publication December 30, 2010; date of current version March 25, 2011. This work was supported by the National Science Council of Taiwan (NSC98-2221-E-027-072 and NSC 99-2218-E-002-005). Paper no. TPWRD-00212-2010.

Keywords

Boolean rule matrix
cause-effect networks
fault diagnosis
supervisory control and data-acquisition/energy-management system (SCADA/EMS) systems

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10.1109/TPWRD.2010.2093585

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Fault section estimation for power networks using logic cause-effect models

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Keywords

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