2019
DOI: 10.1002/tee.23024
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A transfer learning fault diagnosis model of distribution transformer considering multi‐factor situation evolution

Abstract: Aiming at the problem of limited fault data and data expiration of distribution transformers, a transfer learning fault diagnosis model of the distribution transformer considering multi-factor situation evolution is proposed in this paper. First, the state quantities that influence the distribution transformer operation are constructed, and fuzzy binary quantification is used for the state quantities. The association between the state quantities and the fault is explored by the fuzzy Apriori algorithm, and the… Show more

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Cited by 5 publications
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“…In the field of PR, the Apriori algorithm is one of the most common PR rule algorithms and mainly uses the iterative method of layer-by-layer search to mine the relevance in the item sets and then form PR rules [22]. Hong et al proposed using pattern recognition combined with probability images for data mining while constructing a scheme to assess the state of transformers [23].…”
Section: Introduction 1motivation and Backgroundmentioning
confidence: 99%
“…In the field of PR, the Apriori algorithm is one of the most common PR rule algorithms and mainly uses the iterative method of layer-by-layer search to mine the relevance in the item sets and then form PR rules [22]. Hong et al proposed using pattern recognition combined with probability images for data mining while constructing a scheme to assess the state of transformers [23].…”
Section: Introduction 1motivation and Backgroundmentioning
confidence: 99%