NDLI: Integrated decision-making method for power transformer fault diagnosis via rough set and DS evidence theories

Content Provider	IET Digital Library
Author	Xu, Yaoyu Li, Yuan Wang, Yijing Wang, Chen Zhang, Guanjun
Abstract	Precise power transformer fault diagnosis involves incorporating multi-source monitoring information. Uncertain information, missing data, usually occurs in transformer fault cases and diagnosis tasks. To address these challenges, the authors proposed an integrated method of comprehensive transformer fault diagnosis. Diagnostic transformer rules extracted from fault cases form a decision-making table, whereby the main transformer monitoring information and fault types serve as conditional and decision attributes, respectively. Different fault-warning symptoms of the conditional attributes and corresponding decision attributes constitute diagnostic rules. Each obtained symptom in a diagnostic task is evidence supporting different fault types. A modified basic probability assignment (BPA) calculation method is proposed to determine the fault type probability by the obtained symptom. To address contradictory evidence, the symptom significance is introduced to design an improved combination rule incorporating all calculated BPA values to accomplish fault diagnosis. The obtained diagnostic results indicate that more symptoms and a higher symptom significance enable reliable transformer fault diagnosis. The recognition rate of the authors’ method reaches 91.2% with 12–14 symptoms and 94.3% for a 0.9 symptom significance coefficient. It is demonstrated that compared with other combination rules, their method attains a suitable performance (contradiction coefficient K = 0.9 at an 81.3% recognition rate) in realising contradictory information fusion.
Starting Page	5774
Ending Page	5781
Page Count	8
ISSN	17518687
Volume Number	14
e-ISSN	17518695
Issue Number	Issue 24, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-gtd/14/24
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2020.0552
Journal	IET Generation, Transmission & Distribution
Publisher Date	2020-08-13
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Combinatorial Mathematics Contradictory Information Fusion Decision Making Decision-making Table Diagnostic Transformer Rules DS Evidence Theories Fault Diagnosis Fault Type Probability Fault-warning Symptoms Integrated Decision-making Multisource Monitoring Information Power Engineering Computing Power Transformer Power Transformer Fault Diagnosis Probability Probability Assignment Calculation Rough Set Rough Set Theory Statistics Transformer And Reactors Transformer Monitoring Information
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Energy Engineering and Power Technology Electrical and Electronic Engineering

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