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智能化风电场运行维护研究 被引量:6

Research on Operation and Maintenance of Intelligent Wind Farm
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摘要 介绍研究智能化风电场运行维护的目的、原则和措施。研究表明,智能化风电场运行维护是风电场运行维护发展的新方向,能提高运行维护的工作效率,降低运行维护的成本。 This paper introduces the composition,purpose,principle and measure prospect of the operation and maintenance of the intelligent power plants.The research shows that the operation and maintenance system is a new development direction of the future operation and maintenance of intelligent wind power plants,which can improve the efficiency and reduce the cost of operation and maintenance.
作者 吴巍 WU Wei(Huaneng New Energy Co.,Ltd.,Mengdong Branch,Tongliao 028000,China)
出处 《通信电源技术》 2020年第5期271-272,共2页 Telecom Power Technology
关键词 风电场 智能 运行 维护 wind power plants intelligent operation maintenance
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  • 1Andreopoulos A, Tsotsos J K. 50 years of objectrecognition: Directions forward [J]. Computer Vision andImage Understanding, 2013,117(8) : 827-891.
  • 2Russakovsky 0,Deng Jia, Su Hao,et al. ImageNet: Largescale visual recognition challenge [J]. International Journalof Computer Vision,2015,115(3) : 211-252.
  • 3Zhou Bolei,Lapedriza A,Xiao Jianxiong,et al. Learningdeep features for scene recognition using Places database [C]//Proc of the 28th Annual Conf on Neural InformationProcessing Systems. Cambridge, MA: MIT Press, 2014:487-495.
  • 4Xiao Jianxiong,Hays J, Ehinger K,et ai. Sun database:Large-scale scene recognition from abbey to zoo [C] //Proc ofthe IEEE Conf on Computer Vision and Pattern Recognition.Piscataway, NJ: IEEE, 2015 : 3485-3492.
  • 5Krizhevsky A, Sutskever I, Hinton G E. ImageNetclassification with deep convolutional neural networks [C] //Proc of the 26th Annual Conf on Neural InformationProcessing Systems. Cambridge MA: MIT Press, 2012 :1097-1105.
  • 6Yosinski J,Clune J, Bengio Y, et al. How transferablefeatures in deep neural networks [C] //Proc of the 28thAnnual Conf on Neural Information Processing Systems.Cambridge, MA: MIT Press, 2014 : 3320-3328.
  • 7Zeiler M D, Fergus R. Visualizing and understandingconvolutional networks [C] //Proc of the 16th European Confon Computer Vision. Berlin: Springer, 2014? 297-312.
  • 8Simonyan K, Zisserman A. Very deep convolutionalnetworks for large-scale image recognition [J], CoRR abs/1409.1556, 2014.
  • 9Szegedy C,Liu Wei, Jia Yangqing,et al. Going deeper withconvolutions [C] //Proc of the IEEE Conf on ComputerVision and Pattern Recognition. Piscataway,NJ: IEEE,2015: 1-9.
  • 10Donahue J, Jia Yangqing, Vinyals 0,et al. DeCAF : A deepconvolutional activation feature for generic visual recognition[C] //Proc of the 31st Int Conf on Machine Learning. NewYork: ACM, 2014: 647-655.

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