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Language: English

Conflict of Interest: In relation to this article, we declare that there is no conflict of interest.

Publication history: Received March 31, 2008
Accepted April 14, 2008

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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An early warning generation and emergency advisory system and its application to power plants

Jeong Woo Lee Dongil Shin¹ Jin Hwan Yoo² Heon Seok Lee² Jae Wook Ko^2†

AID Corporation, Seocho-gu, Seoul 137-892, Korea ¹Dept. of Chemical Engineering, Myongji University, Yongin, Gyeonggido 449-728, Korea ²Dept. of Chemical Engineering, Kwangwoon University, Nowon-gu, Seoul 139-701, Korea

jwko@kw.ac.kr

Korean Journal of Chemical Engineering, November 2008, 25(6), 1267-1272(6), 10.1007/s11814-008-0208-x

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Abstract

Abstract.Based on the fact that abnormal states continue prior to the breakage of the fault, an early warning system was developed by monitoring the variables in operation real-time, deciding on the operational status, and informing the operator of the process status in order to warn of an abnormal operation in advance. As the traditional system, operating based on threshold limits, separately monitors and manages each operating variable, the interaction/co-relationship_x000D_ among the variables is ignored. The proposed early warning system combines operating variables that interact with one another for each unit process or unit facility, producing a neural network model predicting the normal status values and generating warnings of abnormalities in the process in advance. A time extension function-linkage associative neural network model was designed and used taking consideration of the time lag. Based on the emergency advisory database established, an emergency advisory system was also developed that informs the operators of the cause, effect and emergency measures regarding abnormal operations recognized by the early warning system. The developed system was applied to the power plant operations, and it shows a good performance in early warning generation and provides good advice for the management of diagnosed abnormal situations.

Keywords

Early Warning Generation Emergency Advisory System Power Plant Operation Neural Network-based Modeling Fault Detection and Diagnosis

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