Review of Transformer Health Index from the Perspective of Survivability and Condition Assessment
Article
Article Title | Review of Transformer Health Index from the Perspective of Survivability and Condition Assessment |
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ERA Journal ID | 210405 |
Article Category | Article |
Authors | Li, Shuaibing, Cui, Yi and Li, Hongwei |
Journal Title | Electronics |
Journal Citation | 12 (11) |
Article Number | 2407 |
Number of Pages | 24 |
Year | 2023 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 2079-9292 |
Digital Object Identifier (DOI) | https://doi.org/10.3390/electronics12112407 |
Web Address (URL) | https://www.mdpi.com/2079-9292/12/11/2407 |
Abstract | As a critical indicator for assessing the survivability and condition of transformers in a fleet, the transformer health index has attracted attention from both asset owners and international organizations like CIGRE and IEEE DEIS/PES. To provide a systematic and comprehensive review for further study or to guide transformer asset management, this paper summarizes the state-of-the-art of the transformer health index, from the early proposed weighted-score-sum approaches to the more recently proposed artificial intelligence algorithm-based methods. Firstly, different methods for determining the transformer health index are reviewed. Each of these is specified as belonging to a certain type on the basis of its formulation and composition schematic. Subsequently, the steps to determine each type of health index are summarized, and examples derived from literature are provided for further illustration. Comparisons are finally carried out in order to better understand the pros and cons of different types of transformer health index, and the future development trends for transformer health indexes are also discussed. This work can serve as a valuable reference for the survivability and condition assessment of transformers in the power industry. |
Keywords | weighted-score-sum; health index; information fusion; condition assessment; power transformer; artificial intelligence |
ANZSRC Field of Research 2020 | 400803. Electrical energy generation (incl. renewables, excl. photovoltaics) |
Byline Affiliations | Lanzhou Jiaotong University, China |
School of Engineering |
https://research.usq.edu.au/item/z15y1/review-of-transformer-health-index-from-the-perspective-of-survivability-and-condition-assessment
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