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International Journal of Emerging Electric Power Systems

Editor-in-Chief: Sidhu, Tarlochan

Ed. by Khaparde, S A / Rosolowski, Eugeniusz / Saha, Tapan K / Gao, Fei


CiteScore 2018: 0.86

SCImago Journal Rank (SJR) 2018: 0.220
Source Normalized Impact per Paper (SNIP) 2018: 0.430

Online
ISSN
1553-779X
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Volume 16, Issue 4

Issues

Decision-Making Method for Electrical Equipment Condition-Based Maintenance

Wei Zeng / Feng Jiang / Qin Shun Zeng / Yuanyu Ye / Ruixiang Fan
Published Online: 2015-07-17 | DOI: https://doi.org/10.1515/ijeeps-2014-0195

Abstract

With the rapid development of power grid construction, appropriate maintenance can bring great benefits to the electricity company. On the contrary, it will cause sacrifices, even effect the development of economy. Thus, electrical equipment condition-based maintenance plays a key role to assure healthy operation of electrical equipment and improve power supply reliability. Under the background, in this paper, we combine the Cloud Model with TOPSIS which is improved by Grey Correlation Theory and propose a new decision-making method for electrical equipment condition-based maintenance. In this method, Cloud Model is used to solve the fuzziness and randomness of uncertain linguistic sets in the progress of decision-making. At the same time, we integrate the grey correlation degree into TOPSIS, and calculate the comprehensive relative closeness. Through the numerical example and comparing the sensitivity of some related methods about decision-making, the method in this is proved to be credibility. The basis of electrical equipment condition-based maintenance decision-making can be provided and supply dispatcher decision-making reference when arrange the CBM plan.

Keywords: decision-making; electrical equipment CBM; Cloud Model; Grey Correlation degree; TOPSIS

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About the article

Published Online: 2015-07-17

Published in Print: 2015-08-01


Funding: The project is supported by National Natural Science Foundation of China (71271084) and Science and Technology Project of SGCC (521820140017).


Citation Information: International Journal of Emerging Electric Power Systems, Volume 16, Issue 4, Pages 349–355, ISSN (Online) 1553-779X, ISSN (Print) 2194-5756, DOI: https://doi.org/10.1515/ijeeps-2014-0195.

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