An Equipment Condition Warning Method Based on MEP in Smart Substation
Authors
Jiansheng Li, Zhicheng Zhou, Yiming Wu, Yuncai Lu, Chao Wei, Peng Wu
Corresponding Author
Jiansheng Li
Available Online May 2015.
- DOI
- 10.2991/asei-15.2015.411How to use a DOI?
- Keywords
- condition assessment, K-means clustering, maximum entropy principle, Monte Carlo simulation
- Abstract
To monitor the equipment condition changes, a warning method based on MEP (Maximum Entropy Principle) is proposed. First, the data is normalized and the data objects are divided into K clusters by K-means clustering algorithm. Second, the parameters are calculated and its probability density function is obtained by maximum entropy principle. Last, the new parameter data is compared with warning data to decide the condition changes. In this method, clustering operation can reduce the impacts of outside environments and probability density function based on maximum entropy principle avoids the short comings of subjective assumption. Monte Carlo simulations verify the effectiveness and accuracy.
- Copyright
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Jiansheng Li AU - Zhicheng Zhou AU - Yiming Wu AU - Yuncai Lu AU - Chao Wei AU - Peng Wu PY - 2015/05 DA - 2015/05 TI - An Equipment Condition Warning Method Based on MEP in Smart Substation BT - Proceedings of the 2015 International conference on Applied Science and Engineering Innovation PB - Atlantis Press SP - 2095 EP - 2099 SN - 2352-5401 UR - https://doi.org/10.2991/asei-15.2015.411 DO - 10.2991/asei-15.2015.411 ID - Li2015/05 ER -