Open Access
Issue
MATEC Web Conf.
Volume 63, 2016
2016 International Conference on Mechatronics, Manufacturing and Materials Engineering (MMME 2016)
Article Number 01027
Number of page(s) 5
Section Mechatronic and Application Engineering
DOI https://doi.org/10.1051/matecconf/20166301027
Published online 12 July 2016
  1. Wang Yongqiang, Yue Guoliang, HE Jie, et al. Study on Prediction of TopOil Temperature for Power Transformer Basedon Kalman Filter Algorithm [J]. High Voltage Apparatus, 50 (8),74–79 (2014) [Google Scholar]
  2. Xiong Hao, Chen Weigen, Du Lin, et al. Study on prediction of Top-oil Temperature for Power Transformer Based on T-S Model[J]. Proceedings of the CSEE, 27 (30), 15–19 (2007) [Google Scholar]
  3. Chen Jinming, Wu Yi, Zhu Haibing, et al. Study and application of Transformer Top-oil predicting[J]. Electrotechnical Application, 33 (22),89–93 (2015) [Google Scholar]
  4. Liu Hui, Song Guobing, Luo Junming. Dynamic modeling based on the transformer top oil temperature[J]. Transformer,39 (9),5–9(2002) [Google Scholar]
  5. IEEE Standard C.57.91 —1995 IEEE guide for loading mineral-oil-immersed power transformer[S] (1995) [Google Scholar]
  6. H Qing, J Si, J D. Tylavsky Prediction of top-oil temperature for transformers using neural networks [J]. IEEE Transactions on Power Delivery, 15 (4), 1205–1211 (2000) [CrossRef] [Google Scholar]
  7. B C Lesieutre, W H Hagman, Kirtley Jr J L. An improved transformer top oil temperature model for use inan on-line monitoring an diagnostic system [J]. IEEE Transactions on Power Delivery, 2 (1), 249–256 (1997) [CrossRef] [Google Scholar]
  8. V Galdi, L Ippolito, A Piccolo, et al. Neural diagnostic system for transformer thermal overload protection[J]. Proc. Inst. Elect. Eng., Elect. Power Application, 147 (5), 415–421 (2000) [CrossRef] [Google Scholar]
  9. M K Pradhan, T S. Ramu On-line monitoring of temperature in power transformers using optimal linear combination of ANNs[C]. IEEE International Symposium on Electrical Insulation, Indianapolis, USA, 70-74 (2004) [Google Scholar]
  10. Chen Weigen, XI Hongjuan, Xiaoping Su, et al. Application of Generalized Regression Neural Network to Transformer Winding Hot Spot Temperature Forecasting[J]. High Voltage Engineering, 38 (1), 16–21 (2012) [Google Scholar]
  11. Chen Weigen, SU Xiaoping, Chen Xi, et al. Influence Factor Analysis and Improvement of the Thermal Model for Predicting Transformer Top Oil Temperature[J]. High Voltage Engineering, 37 (6), 1329–1335 (2011) [Google Scholar]
  12. Hu Yusheng, Tu Xuyan, Cui Xiaoyu, et al. Ainferential Method of Uncertain Knowledge Based on Bayes-Network[J]. Computer Integrated manufacturing System,7 (12), 65–68 (2001) [Google Scholar]
  13. SHEN Guchao, ZHOU Zhiyuan, ZHU Xiaolong. et al. Application of Bayesian Network in Intelligence Prediction[J]. Information Science, 32 (10), 3-8 (2014) [Google Scholar]
  14. Wang Yansong Yu Jilai. Joint conditions probability forecastmethod for wind speed and wind power[J]. Proceedings of the CSEE, 31(7), 7–14 (2011) [Google Scholar]
  15. Ren Jia, GAO Xiaoguang. Bayesian network parameter learning and decision support for UAVs [M]. Beijing: National Defence Industry Publisher, 70–100 (2012) [Google Scholar]
  16. JANG Feng, GAO Wen, YAO Hongxu. The inference and learning of Bayesian networks [C]// Proceedings of national network and information security technology conference in 2005. 268–275(2005) [Google Scholar]
  17. LI Haitao, JIN Guang, ZHOU Jinglun, et al. Survey of Bayesian network inference algorithms[J]. Systems Engineering and Electronics, 30(5), 935–939 (2008) [Google Scholar]

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