The analysis of transformer's condition based on Bayes-discriminant method with fault-tree fuzzy evaluation
1
Northeastern University,College Of Information Science and Engineering
2
State Grid Shenyang Electric Power Co., Ltd
3
Northeastern University,College Of Information Science and Engineering,
Submission date: 2019-10-16
Final revision date: 2020-03-03
Acceptance date: 2020-03-03
Online publication date: 2020-04-28
Publication date: 2020-04-28
Corresponding author
Diagnostyka 2020;21(2):3-12
KEYWORDS
Fault Tree Analysisoil-immersed power transformerFuzzy-evaluation systemBayes-discriminantHealth -operation
TOPICS
ABSTRACT
China's electric power construction is renewing Increasingly, and the network is complex and changeable where the automation is getting higher. In this paper, Fuzzy evaluation system is established according to fault tree, and the estimation of transformer’s state is judged by analytic hierarchy process.Bayes-discriminant and discriminant formula are used to discriminate transformer’s attributes, which are based on historical data. The machine identification of transformer faults combines the fuzzy evaluation and Bayes-discriminant. It’s accuracy can be improved by correcting parameters. This method can effectively avoid subjective interference caused by artificial weights. The example shows that this method could be applied to judge health status of electric power equipment and this method can play an early-warning role in the operation of monitoring system.
REFERENCES (15)
1.
Yu H, Wu ZR, Bao TF, Zhang L. Multivariate analysis in dam monitoring data with PCA. Science China Technological Sciences. 2010; 53(4): 1088-1097. https://doi.org/10.1007/s11431....
2.
Hao M, Sun X, Yin L and Ding Q. Prediction of GAMIT baseline solution based on Bayesian classifier. 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA). 2018; pp. 2735-2739. https://doi.org/10.1109/ICIEA.....
3.
Chiacchio F, D'Urso D, Compagno L, Pennisi M, Pappalardo F, Manno G. A stochastic hybrid fault tree automaton for the modelling and simulation of dynamic reliability problems. Expert Systems with Applications. 2016;47(3):42-57. https://doi.org/10.1016/j.eswa....
4.
Li ZJ, Chi GT. Factors study of credit risks of farmer loans based on projection pursuit. Proceedings of 2013 IEEE International Conference on Service Operations and Logistics, and Informatics. 2013: 274-277. https://doi.org/10.1109/SOLI.2....
5.
Jiang SC, Chin KS, Wang L, Qu G, K.L. T. Modified genetic algorithm－ based feature selection combined with pre-trained deep neural network for demand forecasting in outpatient department. Expert Systems With Applications. 2017; 82(3): 1726-1730. https://doi.org/10.1016/j.eswa....
6.
Li JZ, Zhang QG, Wang K, Wang JY, Zhou TC, Zhang YY. Optimal dissolved gas ratios selected by genetic algorithm for power transformer fault diagnosis based on support vector machine. IEEE Transactions on Dielectrics and Electrical Insulation. 2016;23(2):1198-1206. https://doi.org/10.1109/TDEI.2....
7.
Huang YC, Sun HC. Dissolved gas analysis of mineral oil for power transformer fault diagnosis using fuzzy logic. IEEE Transactions on Dielectrics and Electrical Insulation. 2013; 20(3): 974-981. https://doi.org/10.1109/TDEI.2....
8.
Dai JJ, Song H, Sheng GH, Jiang XC. Dissolved gas analysis of insulating oil for power transformer fault diagnosis with deep belief network. IEEE Transactions on Dielectrics and Electrical Insulation. 2017;24(5):2828-2835. https://doi.org/10.1109/TDEI.2....
9.
Chen LL, Yu Z, Ye PF, Zhang J, Zou JZ. Detecting driving stress in physiological signals based on multimodal feature analysis and kernel classifiers. Expert Systems with Applications. 2017; 76(3): 1987-1993. https://doi.org/10.1109/TDEI.2....
10.
PURBA JH. A fuzzy-based reliability approach to evaluate basic events of fault tree analysis for nuclear power plant probabilistic safety assessment. Annals of Nuclear Energy. 2014; 70(3): 21-29. https://doi.org/10.1016/j.anuc....
11.
Ruijters E, Stoeling M. Fault tree analysis: A survey of the state-of-the-art in modeling, analysis and tools. Computer Science Review. 2015; 15-16(3): 29-62. https://doi.org/10.1016/j.cosr....
12.
Biswas SS, Srivastava AK, Whitehead D. A Real-Time Data-Driven Algorithm for Health Diagnosis and Prognosis of a Circuit Breaker Trip Assembly. IEEE Transactions on Industrial Electronics. 2015; 62(6):3822-3831. https://doi.org/10.1109/TIE.20....
13.
Duval M. A review of faults detectable by gas-in-oil analysis in transformers. IEEE Electrical Insulation Magazine.2002;18(3):8-17. https://doi.org/10.1109/MEI.20....
14.
Dai J, Song H, Sheng G, Jiang X. Dissolved gas analysis of insulating oil for power transformer fault diagnosis with deep belief network. IEEE Transactions on Dielectrics and Electrical Insulation. 2017;24(5)2828-2835 . https://doi.org/10.1109/TDEI.2....
15.
He X, Ai Q, Qiu RC, Huang W, Piao L, Liu H. A Big Data Architecture Design for Smart Grids Based on Random Matrix Theory. IEEE Transactions on Smart Grid.2017;8(2):674-686. https://doi.org/10.1109/TSG.20....
| eISSN: | 2449-5220 |
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