Transformer fault diagnosis based on selective Bayes classifier

Dianli Zidonghua Shebei/Electric Power Automation Equipment

Volumn 31, Issue 2, 2011, Pages 44-47

  Zhao, Wenqing ^a  

^a NORTH CHINA ELECTRIC POWER UNIVERSITY   (China)

Author keywords

Bayes network; Fault diagnosis; Selective classifier; Transformer

Indexed keywords

BAYES CLASSIFIER; BAYES NETWORKS; DISSOLVED GAS ANALYSIS; ELECTRIC POWER; ELECTRICAL TESTS; FAULT DIAGNOSIS; NETWORK STRUCTURES; SELECTIVE CLASSIFIER; TEST DATA; TRAINING SAMPLE; TRANSFORMER; TRANSFORMER FAULT DIAGNOSIS; TRANSFORMER FAULTS; UNCERTAIN INFORMATIONS;

DATA PROCESSING; ELECTRIC FAULT CURRENTS; POWER TRANSFORMERS; PROBABILITY DISTRIBUTIONS;

ELECTRIC TRANSFORMER TESTING;

EID: 79953022502     PISSN: 10066047     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (16)

1. WEI Shouzhi, JIN Ningde. Fault diagnosis system based on information fusion and embedded internet[C]//2007 IEEE International Conference on Integration Technology. Shenzhen, China: IEEE Computer Society, 2007: 203-207.
2. WANG Chenhao, HUANG Huixian, XIAO Yewei, et al. Fault diagnosis of power transformers based on BP network with colonial selection algorithm[C]//Third International Conference on Natural Computation. Haikou, China: IEEE Computer Society, 2007: 13-16.
3. JIA Honghong, DAI Wenzhan. Fault diagnosis against oil-immersed transformer based on PNN and GM(1, 1)[C]//2007 IEEE International Conference on Integration Technology. Shenzhen, China: IEEE Computer Society, 2007: 394-397.
4. BABU P, KALAVATHI S. Use of wavelet and neural network (BPFN) for transformer fault diagnosis[C]//2006 IEEE Conference on Electrical Insulation and Dielectric Phenomena. Kansas City, Missouri, USA: IEEE Electrical Insulation Magazine, 2006: 93-96.
5. DONG Ming, QU Yanming, ZHOU Mengge, et al. Fault diagnosis of oil-immersed power transformer using combinatorial decision tree[J]. Proceedings of the CSEE, 2005, 25(16): 36-41.
6. NILSSON N. Artificial intelligence, a new synthesis[M]. San Francisco, USA: Morgan Kaufmann Publishers, 1998: 196-240.
7. WANG Huawei, ZHOU Jinglun, HE Zuyu, et al. Fault diagnosis of complex system based on Bayesian networks[J]. Computer Integrated Manufacturing Systems, 2004, 10(2): 230-234.
8. LI Jianchuan, HU Niaoqing, QIN Guojun, et al. Bayesian network and its applications for device fault diagnosis[J]. China Mechanical Engineering, 2003, 14(10): 896-900.
9. WU Lizeng. Assessing approach of transformer condition[D]. Baoding: North China Electric Power University, 2005.
10. CHICKERING D M, HECKERMAN D, MEEK C. Large sample learning of Bayesian networks is NP-hard[J]. Journal of Machine Learning Research, 2004, 39(5): 1287-1330.
11. CHEESEMAN P, STUTZ J. Bayesian classification (autoclass): theory and results[M]//FAYYAD U M, PIATETSKY-SHAPIRO G, SMYTH P, et al. Advances in knowledge discovery and data mining. Menlo Park, California, USA: AAAI Press, 1996: 153-180.
12. FRIEDMAN N. The Bayesian structural EM algorithm[C]//Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence. San Francisco, USA: Morgan Kaufmann Publishers, 1998: 129-138.
13. LITTLE R J A, RUBIN D B. Statistical analysis with missing data[M]. New York, USA: Wiley, 1987: 120-149.
14. SPIEGELHALTER D J, COWELL R G. Learning in probabilistic expert systems[C]//Proceedings of the Fourth Valencia International Meeting on Bayesian Statistics. Oxford, England: Oxford University Press, 1992: 447-466.
15. RAMONI M, SEBASTIANI P. Robust Bayes classifiers[J]. Artificial Intelligence, 2001, 125(1-2): 209-226.
16. CHEN Jingnian, HUANG Houkuan, TIAN Fengzhan, et al. Selective Bayes classifiers for incomplete data[J]. Journal of Computer Research and Development, 2007, 44(8): 1324-1329.