access icon free Transformer failure diagnosis using fuzzy association rule mining combined with case-based reasoning

© The Institution of Engineering and Technology
Received 02/10/2019, Accepted 06/02/2020, Revised 31/01/2020, Published 18/02/2020

In the field of transformer failure diagnosis, the potential correlation between different characteristic parameters and failures is difficult to detect using traditional methods. Further, the quantities of inspection data have not been fully utilized. To improve the accuracy of transformer diagnosis, this study establishes a diagnosis model based on fuzzy association rules combined with case-based reasoning (CBR) to evaluate the failure types, fault locations, and cause of breakdown in power transformers. First, the inspection data of transformers are collected from several substations over 10 years. Then, the pre-processed data are randomly separated into training and testing sets. For the training set, fuzzy association rules are built for multiple parameters to narrow the search scope of base case preliminarily. Next, CBR is applied to determine the most similar cases. The failure information of the target transformer can be obtained in detail along with the most similar base case. Finally, the accuracy of the model is validated shown in case studies using the testing data set. The result demonstrates that the diagnosis model provides a higher accuracy than the classic IEC 60599 three-ratio method used in the current industry, which means that this diagnosis model has better performance on diagnosis accuracy.

Inspec keywords: power transformers; data mining; fault diagnosis; fault location; case-based reasoning; fuzzy set theory; power engineering computing

Other keywords: diagnosis model; target transformer; pre-processed data; characteristic parameters; failure types; diagnosis accuracy; case-based reasoning; inspection data; power transformers; testing data; fuzzy association rule; failure information; transformer failure diagnosis

Subjects: Data handling techniques; Combinatorial mathematics; Knowledge engineering techniques; Power engineering computing; Transformers and reactors; Combinatorial mathematics

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