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- W2017628180 abstract "Dry-type power transformer was used widely because of its advantages. But unplanned outage effect to construct a strong intelligent power grid because of various stress. Dry-type power transformer’s fault repair time is long and impossible to repair. So it is very important to realize state maintains of dry-type transformer through state monitor and diagnosis. Based on current diagnostic methods, this paper proposed using self-organizing neural network to realize dry-type power transformer the key point temperature parameters of grading evaluation and then to realize the real-time state evaluation and analysis of failure causes. Study results to prolong the dry-type power transformer life and its design production provide theoretical guidance, in order to reduce and avoid dry-type power transformer failure." @default.
- W2017628180 created "2016-06-24" @default.
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- W2017628180 date "2013-02-01" @default.
- W2017628180 modified "2023-09-27" @default.
- W2017628180 title "Research on Method of State Evaluation and Fault Analysis of Dry-Type Power Transformer Based on Self-Organizing Neural Network" @default.
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- W2017628180 doi "https://doi.org/10.4028/www.scientific.net/amm.303-306.562" @default.
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