[1] DAVIS N N, PINSON P, HAHMANN A N, et al. Identifying and characterizing the impact of turbine icing on wind farm power generation[J]. Wind Energy, 2016, 19(8):1503-1518. [2] HOMOLA M C,VIRK M S, NICKLASSON P J, et al. Performance losses due to ice accretion for a 5 MW wind turbine[J].Wind Energy, 2012,15(3):379-389. [3] BARBER S, WANG Y, JAFARI S, et al. The impact of ice formation on wind turbine performance and aerodynamics[J]. Journal of Solar Energy Engineering, 2011,133(1):311-328. [4] SHAJIEE S, PAO L Y, WAGNER P N, et al. Direct ice sensing and localized closed-loop heating for active de-icing of wind turbine blades[C]// Proceedings of the 2013 IEEE American Control Conference. Piscataway, NJ: IEEE, 2013:634-639. [5] PARENT O, ILLINCA A. Anti-icing and de-icing techniques for wind turbines: critical review[J]. Cold Regions Science and Technology, 2011,65(1): 88-96. [6] WONG P K, YANG Z, VONG C M, et al. Real-time fault diagnosis for gas turbine generator systems using extreme learning machine[J]. Neurocomputing, 2014, 128: 249-257. [7] FAN W, CAI G, ZHU Z, et al. Sparse representation of transients in wavelet basis and its application in gearbox fault feature extraction[J]. Mechanical Systems and Signal Processing, 2015, 56: 230-245. [8] 朱煜奇,黄双喜,杨天祺,等.基于栈式降噪自编码的故障诊断[J]. 制造业自动化,2017,39(3):52-156.(ZHU Y Q, HUANG S X, YANG T Q, et al. Fault diagnosis based on stacked denoising autoencoder[J].Manufacturing Automation, 2017,39(3):52-156.) [9] VINCENT P, LAROCHELLE H, BENGIO Y, et al. Extracting and composing robust features with denoising autoencoders[C]// Proceedings of the 25th International Conference on Machine Learning. New York: ACM, 2008:1096-1103. [10] ERHAN D,MANZAGOL P A,BENGIO Y, et al. The difficulty of training deep architectures and the effect of unsupervised pre-training[C]// Proceedings of the 12th International Conference on Artificial Intelligence and Statistics. New York: JMLR.org, 2009:153-160. [11] ERHAN D,BENGIO Y,COURVILLE A, et al. Why does unsupervised pre-training help deep learning[J]. Journal of Machine Learning Research,2010,11(3):625-660. [12] 陶新民,郝思媛,张冬雪,等.不均衡数据分类算法的综述[J]. 重庆邮电大学学报(自然科学版),2013,25(1):101-110. (TAO X M, HAO S Y, ZHANG D X, et al. Overview of classification algorithms for unbalanced data[J]. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 2013,25(1):101-110.) |