DocumentCode
2849495
Title
Notice of Retraction
Study on the Uncertain Problems in Power Grid Fault Diagnosis
Author
Sheng Li ; DongMei Zhao ; Xu Zhang
Author_Institution
Sch. of Electr. & Electron. Eng., North China Electr. Power Univ., Beijing, China
fYear
2009
fDate
11-13 Dec. 2009
Firstpage
1
Lastpage
4
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
To solve the uncertain problems existing in power grid fault diagnosis, on the base of fault diagnosis expert system based on rules and causal logic, a method of Bayesian statistics inference is proposed in this paper , through the analysis of historical data, the diagnosis system gets a general memory function; meanwhile, introducing case-based reasoning (CBR), establishing a special case library, enables the system to remember the special events; the application of the two methods, has improved the diagnosis system´s comprehensive reasoning abilities and enhanced the adaptability and self-learning ability of the system.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
To solve the uncertain problems existing in power grid fault diagnosis, on the base of fault diagnosis expert system based on rules and causal logic, a method of Bayesian statistics inference is proposed in this paper , through the analysis of historical data, the diagnosis system gets a general memory function; meanwhile, introducing case-based reasoning (CBR), establishing a special case library, enables the system to remember the special events; the application of the two methods, has improved the diagnosis system´s comprehensive reasoning abilities and enhanced the adaptability and self-learning ability of the system.
Keywords
Bayes methods; fault diagnosis; power grids; power system faults; Bayesian statistics inference; case based reasoning; fault diagnosis expert system; general memory function; power grid fault diagnosis; self-learning ability; uncertain problems; Artificial intelligence; Bayesian methods; Circuit breakers; Circuit faults; Dispatching; Fault diagnosis; Logic; Manuals; Power grids; Protective relaying;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on
Conference_Location
Wuhan
Print_ISBN
978-1-4244-4507-3
Type
conf
DOI
10.1109/CISE.2009.5365291
Filename
5365291
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