杜小泽,金衍胜,姜剑波,等.火电厂直接空冷凝汽器传热性能实验研究[J].工程热物理学报, 2009, 30(1):99-101.DU Xiaoze, JIN Yansheng, JIANG Jianbo, et al.Experimental study on heat transfer performance of direct air-cooled condenser in power plant[J]. Journal of Engineering Thermophysics, 2009, 30(1):99-101.

JIANG J. Fault diagnosis of condenser based on wavelet neural network[J]. Industrial Engineering Journal, 2010,13(4):104-107.

ZHONG D, ZENG H, MENG J, et al. Numerical simulation of power plant condenser performance and roles for tube arrangement[J]. Journal of Engineering Thermophysics, 2014, 35(1):123-127.

薛倩.凝汽器强化传热研究[J].当代化工, 2020, 49(9):2037-2040.XUE Qian. Study on enhanced heat transfer of condenser in power plant[J]. Contemporary Chemical Industry, 2020,49(9):2037-2040.

WANG J, WANG H, BI W. Research of energy consumption of evaporative condenser in ammonia refrigeration system[J]. Cryogenics&Superconductivity,2011, 31(10):74-77.

WANG Q, LIU B, DONG X, et al. Performance characteristics of microchannel condenser under different heat loads[J]. Cryogenics, 2014(5):58-62.

HAJIDAVALLOO E, EGHTEDARI H. Performance improvement of air-cooled refrigeration system by using evaporatively cooled air condenser[J]. International Journal of Refrigeration, 2010, 33(5):982-988.

张远绪.基于PSO-DDSAEN的凝汽器故障诊断研究[D].青岛:青岛科技大学, 2019:1.ZHANG Yuanxu. Research on condenser fault diagnosis based on PSO-DDSAEN[D]. Qingdao:Qingdao University of Science and Technology, 2019:1.

冯金金,章颢,李健平,等. 1 000 MW燃煤机组双压凝汽器冷端系统运行优化[J].科技和产业, 2024, 24(21):198-204.FENG Jinjin, ZHANG Hao, LI Jianping, et al. Operation optimization of the cold end system with dual-pressure condenser of 1 000 MW coal-fired unit[J]. Science Technology and Industry, 2024, 24(21):198-204.

HAFFEJEE R A, LAUBSCHER R. Development of a thermofluid network modeling methodology for doublerow air-cooled condensers[J]. Thermal Science and Engineering Progress, 2020, 19:100646.

朱自强.不同凝汽器冷却方式的燃气蒸汽联合循环系统热经济性研究[D].北京:华北电力大学, 2022:1.ZHU Ziqiang. Study on thermoeconomic performance of gas-steam combined cycle system with different condenser cooling modes[D]. Beijing:North China Electric Power University, 2022:1.

代雨辰.基于机器学习的凝汽器设备故障分类方法研究[D].南京:东南大学, 2022:1.DAI Yuchen. Research on fault classification method of condenser based on machine learning[D]. Nanjing:Southeast University, 2022:1.

WU D, ZHOU D, ZHANG J, et al. Multimode process monitoring based on fault dependent variable selection and moving window-negative log likelihood probability[J]. Computers&Chemical Engineering, 2020,136:106787.

WU H, ZHAO J. Self-adaptive deep learning for multimode process monitoring[J]. Computers&Chemical Engineering, 2020, 141(20):107024.

ZAMZAM A S, FU X, SIDIROPOULOS N D. Datadriven learning-based optimization for distribution system state estimation[J]. IEEE Transactions on Power Systems,2019, 34(6):4796-4805.

高建强,王艳.基于BP神经网络的直接空冷凝汽器换热性能预测[J].动力工程学报, 2013, 33(6):443-447.GAO Jianqiang, WANG Yan. BP neural network prediction on heat-transfer performance of direct aircooled condensers[J]. Journal of Chinese Society of Power Engineering, 2013, 33(6):443-447.

LI X, WANG N, WANG L, et al. A data-driven model for the air-cooling condenser of thermal power plants based on data reconciliation and support vector regression[J].Applied Thermal Engineering Design Processes Equipment Economics, 2018, 129:1496-1507.

LAI C, BARALDI P, ZIO E. Physics-informed deep autoencoder for fault detection in new-design systems[J].Mechanical Systems&Signal Processing, 2024, 215:111420.

王力光,贠勇博,朱保宇,等.基于迁移学习的湿法烟气脱硫系统出口SO2浓度预测研究[J].发电设备,2021, 35(6):420-425.WANG Liguang, YUN Yongbo, ZHU Baoyu, et al.Research on the prediction of SO2 concentration at the outlet of wet flue gas desulfurization system based on transfer learning[J]. Power Equipment, 2021, 35(6):420-425.

BENJAMIN P, KAREN W, MAX G. Survey of multifidelity methods in uncertainty propagation,inference, and optimization[J]. SIAM Review, 2018,60(3):550-591.

TORZONI G, MANZONI R, MARIANI S. A multifidelity surrogate model for structural health monitoring exploiting model order reduction and artificial neural networks[J]. Mechanical Systems and Signal Processing,2023, 197:110376.

任少君,朱保宇,翁琪航,等.基于数据增强和模型迁移的生物质气化产物分布预测方法[J].中国电机工程学报, 2024, 44(18):7309-7321.REN Shaojun, ZHU Baoyu, WENG Qihang, et al.Prediction method of biomass gasification product distribution based on data augmentation and model migration[J]. Proceedings of the CSEE, 2024, 44(18):7309-7321.

GENG X, LIU P, HU T, et al. Multi-fidelity optimization of a quiet propeller based on deep deterministic policy gradient and transfer learning[J]. Aerospace Science and Technology, 2023, 137:108288.

YE X, NI Y Q, SAJJADI M, et al. Physics-guided, datarefined modeling of granular material-filled particle dampers by deep transfer learning[J]. Mechanical Systems&Signal Processing, 2022, 180:109437.

GISELLE M, PARK C, KIM N H, et al. Review of multifidelity models[J]. Advances in Computational Science and Engineering, 2017, 1(4):351-400.