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논문 기본 정보

자료유형
학술저널
저자정보
Jungwon Yu (Pusan National University) Jaeyel Jang (Korea East-West Power) Jaeyeong Yoo (XEONET) June Ho Park (Pusan National University) Sungshin Kim (Pusan National University)
저널정보
대한전기학회 Journal of Electrical Engineering & Technology Journal of Electrical Engineering & Technology Vol.11 No.4
발행연도
2016.7
수록면
848 - 859 (12page)

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초록· 키워드

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System failures in thermal power plants (TPPs) can lead to serious losses because the equipment is operated under very high pressure and temperature. Therefore, it is indispensable for alarm systems to inform field workers in advance of any abnormal operating conditions in the equipment. In this paper, we propose a clustering-based fault detection method for steam boiler tubes in TPPs. For data clustering, k-means algorithm is employed and the number of clusters are systematically determined by slope statistic. In the clustering-based method, it is assumed that normal data samples are close to the centers of clusters and those of abnormal are far from the centers. After partitioning training samples collected from normal target systems, fault scores (FSs) are assigned to unseen samples according to the distances between the samples and their closest cluster centroids. Alarm signals are generated if the FSs exceed predefined threshold values. The validity of exponentially weighted moving average to reduce false alarms is also investigated. To verify the performance, the proposed method is applied to failure cases due to boiler tube leakage. The experiment results show that the proposed method can detect the abnormal conditions of the target system successfully.

목차

Abstract
1. Introduction
2. Data Clustering Algorithm
3. Clustering-Based Fault Detection
4. Description of Target System: 200 MW Coal-Fired Power Plant
5. Experiment Results
6. Conclusion
References

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