[1] 国家能源局. 国家能源局综合司关于做好可再生能源发展“十四五”规划编制工作有关事项的通知 [EB/OL]. (2020-04-09). http://zfxxgk.nea.gov.cn/2020-04/09/c_138978661.htm.

National Energy Administration. Notice of the Comprehensive Department of the National Energy Administration on doing a good job in the preparation of the “14th five-year plan” for the development of renewable energy [EB/OL]. (2020-04-09). http://zfxxgk.nea.gov.cn/2020-04/09/c_138978661.htm.
[2] 王风云, 张爽. 我国可再生能源发电趋势与市场空间研究——兼析“十四五”期间可再生能源发展潜力 [J]. 价格理论与实践, 2020(4): 36-40 DOI:  10.19851/j.cnki.cn11-1010/f.2020.04.108.

WANG F Y, ZHANG S. Research on the trend and market capacity of renewable energy power generation in China——and predictive analysis of the renewable energy development during the 14th five-year Plan period [J]. Price:theory & practice, 2020(4): 36-40. DOI:  10.19851/j.cnki.cn11-1010/f.2020.04.108.
[3] 王佳蕊, 孙勇, 胡枭, 等. 基于MICP的多能耦合综合能源系统可再生能源消纳能力研究 [J]. 电力建设, 2023, 44(8): 157-170. DOI:  10.12204/j.issn.1000-7229.2023.08.015.

WANG J R, SUN Y, HU X, et al. Research on renewable energy absorption capacity of multi-energy coupling integrated energy systems based on MICP [J]. Electric power construction, 2023, 44(8): 157-170. DOI:  10.12204/j.issn.1000-7229.2023.08.015.
[4] 岑彬. “双碳”背景下可再生能源发展中“弃风弃光”的问题及消纳措施 [J]. 中阿科技论坛(中英文), 2022(10): 60-63.

CEN B. Study on the “forced abandonment of wind and light” in the development of renewable energy under the background of “dual carbon” and its mitigation measures [J]. China-Arab states science and technology forum, 2022(10): 60-63.
[5] 袁家海, 张浩楠, 黄辉. 煤电与新能源耦合发展模式探析 [J]. 中国电力企业管理, 2023(1): 20-22.

YUAN J H, ZHANG H N, HUANG H. Analysis on the coupling development model of coal power and new energy [J]. China power enterprise management, 2023(1): 20-22.
[6] 王金星, 卓建坤, 李菁, 等. 适应燃煤电厂灵活调峰的安全改造技术探讨 [C]//2017热电联产智能高效与灵活性改造技术研讨会论文集, 丹东, 2017-08-16. 丹东: 国家火力发电工程技术研究中心, 2017: 80-86.

WANG J X, ZHUO J K, LI J, et al. Discussion on safety transformation technology suitable for flexible peak shaving of coal-fired power plant [C]//Proceedings of the 2017 Symposium on Smart, Efficient and Flexible Transformation Technology of Cogeneration, Dandong, August 16, 2017. Dandong: National Thermal Power Engineering Technology Research Center, 2017: 80-86.
[7] 张少强, 陈露, 刘子易, 等. 大型燃煤锅炉深度调峰关键问题探讨 [J]. 南方能源建设, 2022, 9(3): 16-28. DOI:  10.16516/j.gedi.issn2095-8676.2022.03.003.

ZHANG S Q, CHEN L, LIU Z Y, et al. Discussion on key problems of depth peak adjustment for large coal-fired boilers [J]. Southern energy construction, 2022, 9(3): 16-28. DOI:  10.16516/j.gedi.issn2095-8676.2022.03.003.
[8] 周俊波, 刘茜, 张华, 等. 典型燃煤锅炉低负荷及变负荷运行控制特性分析 [J]. 热力发电, 2018, 47(9): 34-40. DOI:  10.19666/j.rlfd.201803071.

ZHOU J B, LIU X, ZHANG H, et al. Control characteristic analysis of typical coal-fired boilers during low load or variable load running [J]. Thermal power generation, 2018, 47(9): 34-40. DOI:  10.19666/j.rlfd.201803071.
[9] 国家发展改革委, 国家能源局, 工业和信息化部. 关于推进“互联网+”智慧能源发展的指导意见 [R]. 2016: 4-9.

National Development and Reform Commission, National Energy Administration, Ministry of Industry and Information Technology of the People's Republic of China. Guidance on promoting the development of “Internet +” smart energy [R]. 2016: 4-9.
[10] 国家发展改革委, 国家能源局. 电力发展“十三五”规划(2016—2020) [R]. 2016: 1-46.

National Development and Reform Commission, National Energy Administration. 13th five year plan for electric power development (2016—2020) [R]. 2016: 1-46.
[11] 黄建平, 胡志勇, 吴小明, 等. 回转式空预器联合热管空预器防堵应用及效果 [J]. 电力科技与环保, 2021, 37(6): 31-39 DOI:  10.19944/j.eptep.1674-8069.2021.06.005.

HUANG J P, HU Z Y, WU X M, et al. Application and effect amalysis of rotary air prebeater combined with heat pipe air preheater preveuting blocking technology [J]. Electric power technology and environmental protection, 2021, 37(6): 31-39. DOI:  10.19944/j.eptep.1674-8069.2021.06.005.
[12] 翟博. 三分仓回转式空预器防堵灰技术改造研究 [J]. 科技与创新, 2022(18): 38-40. DOI:  10.15913/j.cnki.kjycx.2022.18.011.

ZHAI B. Research on the technical transformation of the three section rotary air preheater to prevent ash blocking [J]. Science and technology & innovation, 2022(18): 38-40. DOI:  10.15913/j.cnki.kjycx.2022.18.011.
[13] 昂永波. 350 MW超临界机组深度调峰运行优化调整技术分析 [J]. 内蒙古电力技术, 2018, 36(1): 32-36 DOI:  10.3969/j.issn.1008-6218.2018.01.012.

ANG Y B. Adjustment technology analysis of deep cyclic operation optimization of 350 MW supercritical unit [J]. Inner Mongolia electric power, 2018, 36(1): 32-36. DOI:  10.3969/j.issn.1008-6218.2018.01.012.
[14] 陈辉, 王爱英, 戴维葆, 等. 高灰分烟煤煤粉细度对燃烧特性及低负荷稳燃影响研究 [J]. 电站系统工程, 2020, 36(5): 13-15.

CHEN H, WANG A Y, DAI W B, et al. Study on effect of pulverized coal fineness on combustion characteristics and low load stable combustion of high ash bituminous coal [J]. Power system engineering, 2020, 36(5): 13-15.
[15] 刘增春, 刘江涛. 高海拔地区660 MW超超临界煤电机组深度调峰试验解析 [J]. 中国新技术新产品, 2020(11): 15-17. DOI:  10.13612/j.cnki.cntp.2020.11.008.

LIU Z C, LIU J T. Analysis on deep peak shaving test of 660 MW ultra supercritical coal fired power unit in high altitude area [J]. New technology & new products of China, 2020(11): 15-17. DOI:  10.13612/j.cnki.cntp.2020.11.008.
[16] 华敏, 董益华, 项群扬, 等. 超临界660 MW燃煤机组深度调峰试验研究 [J]. 电站系统工程, 2019, 35(5): 35-36, 40.

HUA M, DONG Y H, XIANG Q Y, et al. Research on deep load regulation of 660 MW supercritical coal-fired units [J]. Power system engineering, 2019, 35(5): 35-36, 40.
[17] 王振波, 李国成. 工业锅炉技术 [M]. 北京: 中国石化出版社, 2010: 145-148.

WANG Z B, LI G C. Industrial boiler technology [M]. Beijing: China Petrochemical Press, 2010: 145-148.
[18] 刘志刚. 电厂锅炉自动化控制系统分析 [J]. 中国石油和化工标准与质量, 2012, 32(8): 257. DOI:  10.3969/j.issn.1673-4076.2012.08.231.

LIU Z G. Analysis of power plant boiler automatic control system [J]. China petroleum and chemical standard and quality, 2012, 32(8): 257. DOI:  10.3969/j.issn.1673-4076.2012.08.231.
[19] 白鑫. 锅炉控制的智能优化算法研究 [D]. 石家庄: 河北科技大学, 2019.

BAI X. Research on intelligent optimization algorithm for boiler control [D]. Shijiazhuang: Hebei University of Science and Technology, 2019.
[20] 赵静, 王敏. 基于PLC锅炉专家PID智能控制的研究 [J]. 工业炉, 2018, 40(5): 59-61. DOI:  10.3969/j.issn.1001-6988.2018.05.016.

ZHAO J, WANG M. Research on intelligent control of boiler expert PID based on PLC [J]. Industrial furnace, 2018, 40(5): 59-61. DOI:  10.3969/j.issn.1001-6988.2018.05.016.
[21] 刘华, 刘敏层. 基于模糊PID在锅炉温度控制系统的仿真研究 [J]. 自动化与仪表, 2018, 33(4): 20-25. DOI:  10.19557/j.cnki.1001-9944.2018.04.005.

LIU H, LIU M C. Simulation research of boiler temperature control system based on fuzzy PID [J]. Automation & instrumentation, 2018, 33(4): 20-25. DOI:  10.19557/j.cnki.1001-9944.2018.04.005.
[22] 张建宏. 基于自适应PID控制算法的壁挂式锅炉控制软件开发 [D]. 西安: 西安电子科技大学, 2009.

ZHANG J H. The development of the control software of the wall-mounted gas boiler based on the adaptive PID control [D]. Xi'an: Xidian University, 2009.
[23] 许天鹏. 锅炉汽包水位的无模型自适应PID控制研究 [D]. 兰州: 兰州理工大学, 2013.

XU T P. Research on model-free adaptive PID control of boiler drum water level [D]. Lanzhou: Lanzhou University of Technology, 2013.
[24] 黎丹. 基于无模型自适应控制算法的锅炉汽包水位控制研究 [D]. 南宁: 广西大学, 2016.

LI D. Research of boiler drum level based on model-free adaptive control [D]. Nanning: Guangxi University, 2016.
[25] 刘晓丹. 基于RBF模糊神经网络的船用锅炉汽包水位控制研究 [D]. 大连: 大连海事大学, 2008.

LIU X D. Study on control of water level in marine boiler based on RBF fuzzy neural network [D]. Dalian: Dalian Maritime University, 2008.
[26] 刘佳. 基于神经网络PID控制算法的热水锅炉燃烧控制的研究 [D]. 秦皇岛: 燕山大学, 2005.

LIU J. Study on combustion control of hot water boiler based on neural network PID [D]. Qinhuangdao: Yanshan University, 2005.
[27] 颜廷学, 何岩, 武旭, 等. 一种燃烧锅炉稳燃控制方法: 113847620A [P]. 2021-12-28.

YAN T X, HE Y, WU X, et al. A stable combustion control method for combustion boiler: 113847620A [P]. 2021-12-28.
[28] 刘树明. 基于神经网络的锅炉内火焰燃烧稳定性研究 [J]. 计算机仿真, 2012, 29(8): 187-189, 195 DOI:  10.3969/j.issn.1006-9348.2012.08.045.

LIU S M. Research on combustion within boiler stability based on neural network [J]. Computer simulation, 2012, 29(8): 187-189, 195. DOI:  10.3969/j.issn.1006-9348.2012.08.045.
[29] 蔡国源, 牛玉广, 刘雪菲, 等. 基于图像卷积变分自编码的电站锅炉燃烧稳定性评价方法 [J]. 仪器仪表学报, 2022, 43(3): 210-220. DOI:  10.19650/j.cnki.cjsi.J2108460.

CAI G Y, NIU Y G, LIU X F, et al. Combustion stability judgment of power plant boiler based on image convolutional variational auto-encoder [J]. Chinese journal of scientific instrument, 2022, 43(3): 210-220. DOI:  10.19650/j.cnki.cjsi.J2108460.
[30] HAN W, SUN Z, SCHOLTISSEK A, et al. Machine Learning of ignition delay times under dual-fuel engine conditions [J]. Fuel, 2021, 288: 119650. DOI:  10.1016/j.fuel.2020.119650.
[31] XING J K, LUO K, HEINZ P, et al. Predicting kinetic parameters for coal devolatilization by means of Artificial Neural Networks [J]. Proceedings of the combustion institute, 2019, 37(3): 2943-2950. DOI:  10.1016/j.proci.2018.05.148.
[32] 张振宇, 王刚, 刘宗奎, 等. 330 MW机组深度调峰控制系统问题分析及优化 [J]. 能源研究与管理, 2021(2): 119-123. DOI:  10.16056/j.2096-7705.2021.02.023.

ZHANG Z Y, WANG G, LIU Z K, et al. Analysis and optimization of deep peak load regulation control system for 330 MW power unit [J]. Energy research and management, 2021(2): 119-123. DOI:  10.16056/j.2096-7705.2021.02.023.
[33] 王子杰, 李健, 孙万云. 基于神经网络和遗传算法的锅炉燃烧优化方法 [J]. 华北电力大学学报, 2008, 35(1): 14-17. DOI:  10.3969/j.issn.1007-2691.2008.01.004.

WANG Z J, LI J, SUN W Y. Boiler combustion optimization based on neural network and genetic algorithm [J]. Journal of North China electric power university, 2008, 35(1): 14-17. DOI:  10.3969/j.issn.1007-2691.2008.01.004.
[34] 顾燕萍, 赵文杰, 吴占松. 采用最优MVs决策模型的电站锅炉燃烧优化 [J]. 中国电机工程学报, 2012, 32(2): 39-44. DOI:  10.13334/j.0258-8013.pcsee.2012.02.009.

GU Y P, ZHAO W J, WU Z S. An optimal MVs decision-model for boiler combustion optimization [J]. Proceedings of the CSEE, 2012, 32(2): 39-44. DOI:  10.13334/j.0258-8013.pcsee.2012.02.009.
[35] 薛阳, 叶建华, 钱虹, 等. 火电机组过热汽温神经网络控制的研究 [J]. 上海电力学院学报, 2009, 25(1): 35-38. DOI:  10.3969/j.issn.1006-4729.2009.01.010.

XUE Y, YE J H, QIAN H, et al. Research of neural network control of the superheated steam temperature of fossil-fired power station [J]. Journal of Shanghai university of electric power, 2009, 25(1): 35-38. DOI:  10.3969/j.issn.1006-4729.2009.01.010.
[36] 吴智群, 黄廷辉, 胡洪华, 等. 电站锅炉智能燃烧优化技术的应用研究 [J]. 热力发电, 2008, 37(9): 23-27, 31. DOI:  10.19666/j.rlfd.2008.09.007.

WU Z Q, HUANG T H, HU H H, et al. Study on application of intelligent combustion-optimizing technology on utility boilers [J]. Thermal power generation, 2008, 37(9): 23-27, 31. DOI:  10.19666/j.rlfd.2008.09.007.
[37] 沙骁. 宽负荷火电机组锅炉受热面超温分析及智能预测研究 [D]. 北京: 清华大学, 2021.

SHA X. Research on over-temperature analysis and intelligent prediction of boiler heating surface of wide-load thermal power unit [D]. Beijing: Tsinghua University, 2021.
[38] 陈鑫, 王乃斌, 刘利, 等. 大数据技术应用于火电机组深度调峰的研究 [J]. 电力设备管理, 2018(10): 53-55.

CHEN X, WANG N B, LIU L, et al. Study on the application of big data technology in deep peak regulation of thermal power units [J]. Electric power equipment management, 2018(10): 53-55.
[39] 王政, 刘继伟. 电站锅炉燃烧优化技术的应用与发展 [J]. 华北电力技术, 2015(11): 63-70. DOI:  10.16308/j.cnki.issn1003-9171.2015.11.011.

WANG Z, LIU J W. Application and development of boiler combustion optimization technology [J]. North China electric power, 2015(11): 63-70. DOI:  10.16308/j.cnki.issn1003-9171.2015.11.011.
[40] 王立, 王燕晋, 李战国, 等. 火力发电机组深度调峰试验及优化 [J]. 发电设备, 2019, 33(2): 133-137. DOI:  10.3969/j.issn.1671-086X.2019.02.014.

WANG L, WANG Y J, LI Z G, et al. Deep peak shaving tests and optimization for thermal power units [J]. Power equipment, 2019, 33(2): 133-137. DOI:  10.3969/j.issn.1671-086X.2019.02.014.
[41] 李彦军, 郭洪远, 任海彬, 等. 基于灵活性改造的机组深度调峰模式运行研究 [J]. 中国高新科技, 2020(19): 96-97. DOI:  10.3969/j.issn.2096-4137.2020.19.041.

LI Y J, GUO H Y, REN H B, et al. Research on unit deep peak shaving mode operation based on flexibility transformation [J]. China high and new technology, 2020(19): 96-97. DOI:  10.3969/j.issn.2096-4137.2020.19.041.
[42] 周昊, 茅建波, 池作和, 等. 燃煤锅炉低氮氧化物燃烧特性的神经网络预报 [J]. 环境科学, 2002, 23(2): 18-22 DOI:  10.13227/j.hjkx.2002.02.004.

ZHOU H, MAO J B, CHI Z H, et al. Predicting low NO x combustion property of a coal-fired boiler [J]. Environmental science, 2002, 23(2): 18-22. DOI:  10.13227/j.hjkx.2002.02.004.
[43] 周昊, 朱洪波, 茅建波, 等. 大型四角切圆燃烧锅炉NO x排放特性的神经网络模型 [J]. 中国电机工程学报, 2002, 22(1): 33-37. DOI:  10.13334/j.0258-8013.pcsee.2002.01.007.

ZHOU H, ZHU H B, MAO J B, et al. An artificial neural network model on NO x emission property of a high capacity tangentially firing boiler [J]. Proceedings of the CSEE, 2002, 22(1): 33-37. DOI:  10.13334/j.0258-8013.pcsee.2002.01.007.
[44] 方海泉, 薛惠锋, 李宁, 等. 基于贝叶斯神经网络遗传算法的锅炉燃烧优化 [J]. 系统仿真学报, 2015, 27(8): 1790-1795. DOI:  10.16182/j.cnki.joss.2015.08.020.

FANG H Q, XUE H F, LI N, et al. Boiler combustion optimization based on Bayesian neural network and genetic algorithm [J]. Journal of system simulation, 2015, 27(8): 1790-1795. DOI:  10.16182/j.cnki.joss.2015.08.020.
[45] 王淅芬, 罗自学, 周怀春. 基于炉内温度分布的NO x排放特性的神经网络模型 [J]. 能源技术, 2009, 30(3): 133-136, 148.

WANG X F, LUO Z X, ZHOU H C. Neural network model on NO x emission with furnace temperature distribution in a boiler [J]. Energy technology, 2009, 30(3): 133-136, 148.
[46] 张振星. 基于智能优化算法的电站锅炉燃烧优化 [D]. 北京: 华北电力大学, 2015.

ZHANG Z X. Utility boiler combustion optimization based on intelligent optimization algorithm [D]. Beijing: North China Electric Power University, 2015.
[47] 景雪晖, 张涛, 周曙明, 等. 灵活性深度调峰下锅炉NO x排放的神经网络方法预报 [J]. 上海电力学院学报, 2019, 35(3): 215-220. DOI:  10.3969/j.issn.1006-4729.2019.03.004.

JING X H, ZHANG T, ZHOU S M, et al. Neural network prediction of NO x emission characteristics of boiler under flexibility and deep peak shaving [J]. Journal of Shanghai university of electric power, 2019, 35(3): 215-220. DOI:  10.3969/j.issn.1006-4729.2019.03.004.
[48] 李诚. 深度调峰下燃煤机组低碳运行与氮氧化物协同优化脱除 [D]. 北京: 清华大学, 2021.

LI C. Low-carbon operation and synergistic optimization of nitrogen oxide removal of coal-fired power plants under deep peak regulation [D]. Beijing: Tsinghua University, 2021.
[49] TAN P, HE B, ZHANG C, et al. Dynamic modeling of NO x emission in a 660 MW coal-fired boiler with long short-term memory [J]. Energy, 2019, 176: 429-436. DOI:  10.1016/j.energy.2019.04.020.
[50] YANG G T, WANG Y N, LI X L. Prediction of the NO x emissions from thermal power plant using long-short term memory neural network [J]. Energy, 2020, 192: 116597. DOI:  10.1016/j.energy.2019.116597.
[51] 唐振浩, 朱得宇, 李扬. 基于数据驱动的燃煤锅炉NO x排放浓度动态修正预测模型 [J]. 中国电机工程学报, 2022, 42(14): 5182-5193. DOI:  10.13334/j.0258-8013.pcsee.211426.

TANG Z H, ZHU D Y, LI Y. Data driven based dynamic correction prediction model for NO x emission of coal fired boiler [J]. Proceedings of the CSEE, 2022, 42(14): 5182-5193. DOI:  10.13334/j.0258-8013.pcsee.211426.
[52] 翁卫国, 周灿, 王丁振. 1000 MW燃煤机组变负荷条件下颗粒物排放特性研究 [J]. 能源工程, 2018(2): 1-6. DOI:  10.16189/j.cnki.nygc.2018.02.001.

WENG W G, ZHOU C, WANG D Z. Study on particle emission in a 1000 MW coal-fired unit under varying loads [J]. Energy engineering, 2018(2): 1-6. DOI:  10.16189/j.cnki.nygc.2018.02.001.
[53] 鲍铁军, 刘建平, 侯志, 等. 440 t/h循环流化床机组深度调峰工况下燃烧优化与机炉协调控制的实现 [J]. 锅炉制造, 2021(4): 25-27, 30. DOI:  10.3969/j.issn.1674-1005.2021.04.009.

BAO T J, LIU J P, HOU Z, et al. Realization of combustion optimization and boiler-turbine coordinated control for 440 t/h CFB unit under deep peak shaving condition [J]. Boiler manufacturing, 2021(4): 25-27, 30. DOI:  10.3969/j.issn.1674-1005.2021.04.009.
[54] 许振宇, 陈鸿伟, 高建强. 火电厂锅炉主要运行参数的耗差分析 [J]. 热力发电, 2007, 36(2): 16-18,30. DOI:  10.3969/j.issn.1002-3364.2007.02.005.

XU Z Y, CHEN H W, GAO J Q. Analysis of consumption deviation for main operation parameters of boilers in thermal power plant [J]. Thermal power generation, 2007, 36(2): 16-18,30. DOI:  10.3969/j.issn.1002-3364.2007.02.005.
[55] 杨志良. 浅析火力发电厂锅炉经济运行 [J]. 科技视界, 2013(24): 258. DOI:  10.19694/j.cnki.issn2095-2457.2013.24.200.

YANG Z L. Analysis on economic operation of boiler in thermal power plant [J]. Science & technology vision, 2013(24): 258. DOI:  10.19694/j.cnki.issn2095-2457.2013.24.200.
[56] 顾先青, 潘卫国, 王文欢, 等. 大型火电机组供电煤耗率比较分析 [J]. 上海电力学院学报, 2009, 25(2): 109-112 DOI:  10.3969/j.issn.1006-4729.2009.02.003.

GU X Q, PAN W G, WANG W H, et al. Compative analysis of the net coal consumption rate of coal-fired power generating units [J]. Journal of Shanghai university of electric power, 2009, 25(2): 109-112. DOI:  10.3969/j.issn.1006-4729.2009.02.003.
[57] 高燕武, 李朋, 张炜, 等. 基于大数据的燃煤机组供电煤耗分析 [J]. 电力与能源, 2020, 41(1): 109-111.

GAO Y W, LI P, ZHANG W, et al. Analysis of power supply coal consumption of coal-fired unit based on big data [J]. Power & energy, 2020, 41(1): 109-111.
[58] 李鑫鑫. 基于历史寻优的火电机组运行优化研究 [D]. 北京: 华北电力大学, 2017.

LI X X. Research on optimal operation of thermal power unit based on the optimal searching method of the historical data [D]. Beijing: North China Electric Power University, 2017.
[59] 李霍生. 基于PID实现对锅炉循环泵的智能控制 [J]. 机械管理开发, 2022, 37(5): 227-228. DOI:  10.16525/j.cnki.cn14-1134/th.2022.05.097.

LI H S. Intelligent control of boiler circulation pump based on PID [J]. Mechanical management and development, 2022, 37(5): 227-228. DOI:  10.16525/j.cnki.cn14-1134/th.2022.05.097.
[60] 胡刚, 刘伟. 300 MW CFB锅炉一次风机耗电率优化研究 [J]. 电力学报, 2021, 36(4): 301-305. DOI:  10.13357/j.dlxb.2021.037.

HU G, LIU W. Study on optimization of the power consumption rate of 300 MW CFB boiler primary fan [J]. Journal of electric power, 2021, 36(4): 301-305. DOI:  10.13357/j.dlxb.2021.037.
[61] 吴桂林, 田斌斌, 杨国. 高压变频器在锅炉风机节能控制中的应用 [C]//第五届全国石油和化工电气技术大会论文集, 淄博, 2020-09-09. 淄博: 中国机电一体化技术应用协会, 2020: 141-146.

WU G L, TIAN B B, YANG G. Application of high voltage inverter in boiler fan energy saving control [C]//Proceedings of the 5th National Petroleum and Chemical Electrical Technology Conference, Zibo, September 9, 2020. Zibo: China Association for the Application of Mechatronics Technology, 2020: 141-146.
[62] 华雪莹, 王润芳, 姚为方, 等. 燃煤机组深度调峰环保设备安全运行策略研究 [J]. 能源与节能, 2021(12): 184-186 DOI:  10.16643/j.cnki.14-1360/td.2021.12.069.

HUA X Y, WANG R F, YAO W F, et al. Research on safety operation strategy of environmental protection equipment for deep peak load adjustment of coal-fired units [J]. Energy and energy conservation, 2021(12): 184-186. DOI:  10.16643/j.cnki.14-1360/td.2021.12.069.
[63] 付忠广, 靳涛, 周丽君, 等. 复杂系统反向建模方法及偏最小二乘法建模应用研究 [J]. 中国电机工程学报, 2009, 29(2): 25-29. DOI:  10.3321/j.issn:0258-8013.2009.02.005.

FU Z G, JIN T, ZHOU L J, et al. Research and application of the reversed modeling method and partial least-square regression modeling for the complex thermal system [J]. Proceedings of the CSEE, 2009, 29(2): 25-29. DOI:  10.3321/j.issn:0258-8013.2009.02.005.
[64] 吴啸川. 电站锅炉高温受热面管壁温度的反向建模研究 [D]. 北京: 华北电力大学(北京), 2011.

WU X C. Research on reverse modeling of tube wall temperature of high-temperature heating surface in power plant boiler [D]. Beijing: North China Electric Power University (Beijing), 2011.
[65] 吴斐, 唐必光, 余艳芝, 等. 神经网络在过热器、再热器管壁温度计算中的应用 [J]. 发电设备, 2005(2): 108-111. DOI:  10.3969/j.issn.1671-086X.2005.02.012.

WU F, TANG B G, YU Y Z, et al. Application of neural networks in the calculation of seperheater and reheater tube wall temperature [J]. Power equipment, 2005(2): 108-111. DOI:  10.3969/j.issn.1671-086X.2005.02.012.
[66] 周云龙, 苏耀雷. 基于神经网络的锅炉过热器和再热器管壁温度预测研究 [J]. 热力发电, 2012, 41(5): 22-26. DOI:  10.3969/j.issn.1002-3364.2012.05.022.

ZHOU Y L, SU Y L. Study on predicition of tube-wall temperature in superheater and reheater of boiler based on neural network [J]. Thermal power generation, 2012, 41(5): 22-26. DOI:  10.3969/j.issn.1002-3364.2012.05.022.
[67] 卢彬, 刘茜, 高林, 等. 基于NARX神经网络的锅炉壁温预测模型 [J]. 热力发电, 2019, 48(3): 35-40. DOI:  10.19666/j.rlfd.201812214.

LU B, LIU X, GAO L, et al. Prediction model of boiler platen superheater tube wall temperature based on NARX neural network [J]. Thermal power generation, 2019, 48(3): 35-40. DOI:  10.19666/j.rlfd.201812214.
[68] CHEN Y T, ZHANG D X. Theory-guided deep-learning for electrical load forecasting (TgDLF) via ensemble long short-term memory [J]. Advances in applied energy, 2021, 1: 100004. DOI:  10.1016/j.adapen.2020.100004.
[69] 李友志, 蒋蓬勃, 白帆, 等. 基于LSTM的锅炉四管高温再热器超温预测分析 [J]. 无线互联科技, 2019, 16(18): 103-104. DOI:  10.3969/j.issn.1672-6944.2019.18.049.

LI Y Z, JIANG P B, BAI F, et al. Analysis on prediction of overtemperature of boiler four-tube high temperature reheater based on LSTM [J]. Wireless internet technology, 2019, 16(18): 103-104. DOI:  10.3969/j.issn.1672-6944.2019.18.049.
[70] 金志远, 李胜男, 谭鹏, 等. 基于长短时记忆神经网络的锅炉多参数协同预测模型 [J]. 热力发电, 2021, 50(5): 120-126. DOI:  10.19666/j.rlfd.202007234.

JIN Z Y, LI S N, TAN P, et al. Multi-parameter collaborative prediction model of boilers based on long-short-term memory neural network [J]. Thermal power generation, 2021, 50(5): 120-126. DOI:  10.19666/j.rlfd.202007234.
[71] 韩驰. 超超临界火电机组炉膛受热面金属壁温预测及监测系统 [D]. 吉林: 东北电力大学, 2020.

HAN C. Prediction and monitoring system of metal wall temperature on furnace heating surface of ultra-supercritical thermal power unit [D]. Jilin: Northeast Electric Power University, 2020.
[72] 沙骁, 黄骞, 柳冠青, 等. 燃煤锅炉受热面壁温监测数据的时序特征分析 [J]. 燃烧科学与技术, 2021, 27(5): 475-481. DOI:  10.11715/rskxjs.R202108022.

SHA X, HUANG Q, LIU G Q, et al. Time series analysis of monitored heating surface wall temperature of coal-fired boiler [J]. Journal of combustion science and technology, 2021, 27(5): 475-481. DOI:  10.11715/rskxjs.R202108022.
[73] 祝建飞, 马建华, 杨康, 等. 超临界垂直管圈直流炉壁温偏差及优化控制 [J]. 锅炉技术, 2021, 52(5): 22-26. DOI:  10.3969/j.issn.1672-4763.2021.05.005.

ZHU J F, MA J H, YANG K, et al. Wall temperature deviation and optimal control of supercritical vertical tube once through boiler [J]. Boiler technology, 2021, 52(5): 22-26. DOI:  10.3969/j.issn.1672-4763.2021.05.005.
[74] ZHANG Q N, SUN F Z, CHEN C X. Research on the three-dimensional wall temperature distribution and low-temperature corrosion of quad-sectional air preheater in larger power plant boilers [J]. International journal of heat and mass transfer, 2019, 128: 739-747. DOI:  10.1016/j.ijheatmasstransfer.2018.09.006.
[75] ÖZDEMIR K, SERINCAN M F. A computational fluid dynamics model of a rotary regenerative heat exchanger in a flue gas desulfurization system [J]. Applied thermal engineering, 2018, 143: 988-1002. DOI:  10.1016/j.applthermaleng.2018.08.011.
[76] BU Y F, WANG L M, CHEN X, et al. Numerical analysis of ABS deposition and corrosion on a rotary air preheater [J]. Applied thermal engineering, 2018, 131: 669-677. DOI:  10.1016/j.applthermaleng.2017.11.082.
[77] LI C, HUANG Q, LIU G Q, et al. In situ visual monitoring of rotary air preheater blockage: setup and image analysis [M]//LYU J F, LI S Q. Clean Coal and Sustainable Energy. Singapore: Springer, 2022. DOI:  10.1007/978-981-16-1657-0_65.
[78] 韩院臣. 660 MW超临界机组深度调峰自动控制技术研究 [J]. 电气传动自动化, 2021, 43(6): 30-32, 50. DOI:  10.3969/j.issn.1005-7277.2021.06.008.

HAN Y C. Research on automatic control technology of deep peak shaving of 660 MW supercritical unit [J]. Electric drive automation, 2021, 43(6): 30-32, 50. DOI:  10.3969/j.issn.1005-7277.2021.06.008.
[79] 高满达, 李庚达, 陈彦桥, 等. 先进检测与5G通信技术在燃煤锅炉运行监测中的应用研究 [J]. 热力发电, 2023, 52(1): 18-25. DOI:  10.19666/j.rlfd.202204061.

GAO M D, LI G D, CHEN Y Q, et al. Application of advanced detection and 5G communication technology in coal-fired boiler operation monitoring [J]. Thermal power generation, 2023, 52(1): 18-25. DOI:  10.19666/j.rlfd.202204061.
[80] 董泽, 姜炜, 王晓燕, 等. 数字孪生在火电机组数字化转型中的应用 [J]. 系统仿真学报, 2023, 35(6): 1144-1156. DOI:  10.16182/j.issn1004731x.joss.22-0229.

DONG Z, JIANG W, WANG X Y, et al. Application of digital twin in digital transformation of thermal power units [J]. Journal of system simulation, 2023, 35(6): 1144-1156. DOI:  10.16182/j.issn1004731x.joss.22-0229.
[81] 卢锦玲, 颜禄涵, 腊志源, 等. 基于数字孪生与动态能效模型的综合能源系统实时优化调度策略 [J]. 电网技术, 2023, 47(1): 226-238. DOI:  10.13335/j.1000-3673.pst.2022.0044.

LU J L, YAN L H, LA Z Y, et al. Real-time optimal scheduling strategy for integrated energy system based on digital twins and dynamic energy efficiency model [J]. Power system technology, 2023, 47(1): 226-238. DOI:  10.13335/j.1000-3673.pst.2022.0044.
[82] 牛百芳, 董功利, 陈文鑫. 基于RFID的锅炉炉膛出口烟气温度智能监测系统 [J]. 电子元器件与信息技术, 2023, 7(6): 76-79. DOI:  10.19772/j.cnki.2096-4455.2023.6.020.

NIU B F, DONG G L, CHEN W X. Intelligent monitoring system for boiler furnace outlet flue gas temperature based on RFID [J]. Electronic components and information technology, 2023, 7(6): 76-79. DOI:  10.19772/j.cnki.2096-4455.2023.6.020.