Association Rule Mining-Based Dissolved Gas Analysis for Fault Diagnosis of Power Transformers

This paper presents a novel association rule mining (ARM)-based dissolved gas analysis (DGA) approach to fault diagnosis (FD) of power transformers. In the development of the ARM-based DGA approach, an attribute selection method and a continuous datum attribute discretization method are used for cho...

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Bibliographic Details
Published in:IEEE transactions on human-machine systems 2009-11, Vol.39 (6), p.597-610
Main Authors: Yang, Z., Tang, W.H., Shintemirov, A., Wu, Q.H.
Format: Article
Language:English
Subjects:
Applied sciences
Apriori-TFP
association rule mining
Association rules
Bootstrap
Classifiers
Computer science
control theory
systems
Data mining
Data processing. List processing. Character string processing
Dissolution
Dissolved gas analysis
Electrical engineering. Electrical power engineering
Electrical fault detection
Exact sciences and technology
Fault diagnosis
Fitness
Gas analysis
Industrial metrology. Testing
Mechanical engineering. Machine design
Memory organisation. Data processing
Oil insulation
Power system reliability
power transformer
Power transformer insulation
Power transformers
Software
Studies
Testing
Training
Transformers
Transformers and inductors
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Summary:This paper presents a novel association rule mining (ARM)-based dissolved gas analysis (DGA) approach to fault diagnosis (FD) of power transformers. In the development of the ARM-based DGA approach, an attribute selection method and a continuous datum attribute discretization method are used for choosing user-interested ARM attributes from a DGA data set, i.e. the items that are employed to extract association rules. The given DGA data set is composed of two parts, i.e. training and test DGA data sets. An ARM algorithm namely Apriori-Total From Partial is proposed for generating an association rule set (ARS) from the training DGA data set. Afterwards, an ARS simplification method and a rule fitness evaluation method are utilized to select useful rules from the ARS and assign a fitness value to each of the useful rules, respectively. Based upon the useful association rules, a transformer FD classifier is developed, in which an optimal rule selection method is employed for selecting the most accurate rule from the classifier for diagnosing a test DGA record. For comparison purposes, five widely used FD methods are also tested with the same training and test data sets in experiments. Results show that the proposed ARM-based DGA approach is capable of generating a number of meaningful association rules, which can also cover the empirical rules defined in industry standards. Moreover, a higher FD accuracy can be achieved with the association rule-based FD classifier, compared with that derived by the other methods.
ISSN:1094-6977
2168-2291
1558-2442
2168-2305
DOI:10.1109/TSMCC.2009.2021989