An integrated method of set pair analysis and association rule for fault diagnosis of power transformers

Fault diagnosis of power transformers is crucial to the healthy operation of transformers. In order to enhance its accuracy and reliability, a new fault diagnosis method based on Set Pair Analysis (SPA) and association rules was proposed in this paper. Via analyzing the relationship of fault symptoms and fault types, the corresponding association rules could be established. Via computing the support degrees and confidence degrees of the association rules, the constant weight coefficient of each symptom and the variable weight coefficient of each type could be obtained. The diagnosis method could avoid the subjective defects effectively. Via introducing the concept of subordinate degree, the connection degrees of each fault type and the whole running state of transformer could be obtained. This method could also improve the accuracy of uncertainty factors of transformer fault diagnosis. Experimental results of the test substation proved this method had a higher accuracy by comparing with both association rules and SPA.

[1]  Shahrul Kamaruddin,et al.  An overview of time-based and condition-based maintenance in industrial application , 2012, Comput. Ind. Eng..

[2]  Zhu Yongli,et al.  Research of transformer fault diagnosis based on Bayesian network classifiers , 2010, 2010 International Conference On Computer Design and Applications.

[3]  Hui Ma,et al.  Power transformer fault diagnosis under measurement originated uncertainties , 2012, IEEE Transactions on Dielectrics and Electrical Insulation.

[4]  Hong-Sheng Su,et al.  Set Pair Analysis Applied for Identifying Power Transformer Faults , 2006, 2006 International Conference on Machine Learning and Cybernetics.

[5]  Jie Lu,et al.  Fault diagnosis of transformer using association rule mining and knowledge base , 2010, 2010 10th International Conference on Intelligent Systems Design and Applications.

[6]  Ruijin Liao,et al.  An Integrated Decision-Making Model for Condition Assessment of Power Transformers Using Fuzzy Approach and Evidential Reasoning , 2011, IEEE Transactions on Power Delivery.

[7]  Q. Henry Wu,et al.  Power Transformer Fault Classification Based on Dissolved Gas Analysis by Implementing Bootstrap and Genetic Programming , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[8]  T. Nipkow,et al.  Probabilistic Models , 2004 .

[9]  S. Denison,et al.  Probabilistic models, learning algorithms, and response variability: sampling in cognitive development , 2014, Trends in Cognitive Sciences.

[10]  Claude Carlet.  A method of construction of balanced functions with optimum algebraic immunity , 2006, IACR Cryptol. ePrint Arch..

[11]  Lee Li,et al.  Condition assessment of power transformers using a synthetic analysis method based on association rule and variable weight coefficients , 2013, IEEE Transactions on Dielectrics and Electrical Insulation.

[12]  Q. Henry Wu,et al.  Association Rule Mining-Based Dissolved Gas Analysis for Fault Diagnosis of Power Transformers , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[13]  Qi-jia Xie,et al.  Transformer fault diagnosis based on bayesian network and rough set reduction theory , 2013, IEEE 2013 Tencon - Spring.

[14]  K. Sato,et al.  Current Limiting Performance of Three-Phase Concentric Transformer Type SFCL at Unbalanced Fault Conditions , 2013, IEEE Transactions on Applied Superconductivity.

[15]  M. H. Wang.  A Novel Extension Method for Transformer Fault Diagnosis , 2002, IEEE Power Engineering Review.

[16]  Hongsheng Su,et al.  A Hybrid Deterministic Model Based on Rough set and Fuzzy set and Bayesian Optimal Classifier , 2006, First International Conference on Innovative Computing, Information and Control - Volume I (ICICIC'06).

[17]  Lina Bertling,et al.  An Approach for Condition-Based Maintenance Optimization Applied to Wind Turbine Blades , 2010, IEEE Transactions on Sustainable Energy.

[18]  C. Koley,et al.  Wavelet-aided SVM tool for impulse fault identification in transformers , 2006, IEEE Transactions on Power Delivery.

[19]  Li Lixiang,et al.  Research and application on Set Pair Analysis for E-government comprehensive evaluation model , 2010, 2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE).

[20]  Angel Javier Quispe Carita,et al.  Bayesian Networks applied to Failure Diagnosis in Power Transformer , 2013, IEEE Latin America Transactions.

[21]  Rafael Paiva Tavares.  Diagnosing faults in power transformers with autoassociative neural networks and mean shift , 2012 .

[22]  Chunming Wu,et al.  Power Transformer State Assessment Based on Grey Target Theory , 2009, 2009 International Conference on Measuring Technology and Mechatronics Automation.

[23]  Feng Gao,et al.  Based on set pair analysis of spare parts supply accurate security scheme decision , 2013, 2013 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE).

[24]  Haozhong Cheng,et al.  Flexible transmission network planning using the connection number , 2008 .

[25]  W.H. Tang,et al.  An evidential reasoning approach to transformer condition assessments , 2004, IEEE Transactions on Power Delivery.

[26]  Xiaohui Li,et al.  RMP neural network based dissolved gas analyzer for fault diagnostic of oil-filled electrical equipment , 2011, IEEE Transactions on Dielectrics and Electrical Insulation.

[27]  Congming Qiu,et al.  Research of a new transformer fault diagnosis expert system , 2012 .