NDLI: Transformer failure diagnosis using fuzzy association rule mining combined with case-based reasoning

Content Provider	IET Digital Library
Author	Guo, Chenxi Wang, Bin Wu, Zhanyu Ren, Ming He, Yifan Albarracín, Ricardo Dong, Ming
Abstract	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.
Starting Page	2202
Ending Page	2208
Page Count	7
ISSN	17518687
Volume Number	14
e-ISSN	17518695
Issue Number	Issue 11, Jun (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-gtd/14/11
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2019.1423
Journal	IET Generation, Transmission & Distribution
Publisher Date	2020-02-18
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Case-based Reasoning Characteristic Parameter Combinatorial Mathematics Data Handling Technique Data Mining Diagnosis Accuracy Diagnosis Model Failure Information Failure Types Fault Diagnosis Fault Location Fuzzy Association Rule Fuzzy Set Theory Inspection Data Knowledge Engineering Technique Power Engineering Computing Power Transformer Pre-processed Data Target Transformer Testing Data Transformer And Reactors Transformer Failure Diagnosis
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Energy Engineering and Power Technology Electrical and Electronic Engineering

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