智慧电厂大数据云平台的架构建设与模型开发研究

王志敏; 黄骞; 王可轩; 陈树宽; 李敏; 王海; 李水清

doi:10.19805/j.cnki.jcspe.2025.230703

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智慧电厂大数据云平台的架构建设与模型开发研究

数字化与智能化 | 更新时间：2025-11-28

- 智慧电厂大数据云平台的架构建设与模型开发研究
- Architecture Construction and Model Development of Big-data Cloud Platform for Intelligent Coal-fired Power Plants
- 动力工程学报 2025年45卷第2期页码：282-291
- 作者机构：
  
  1. 清华大学能源与动力工程系, 热科学与动力工程教育部重点实验室,北京,100084
  2. 大唐陕西发电有限公司延安热电厂,陕西,延安,716004
- 作者简介：
  
  [ "王志敏(1998—),女,内蒙古乌海人,硕士研究生,研究方向为低碳智能发电,E-mail:wangzm21@mail.tsinghua.edu.cn" ]
- 基金信息：
  
  国家重点研发计划资助项目(2022YFB4003900)
- DOI：10.19805/j.cnki.jcspe.2025.230703
  中图分类号： TM621;TP393.09
- 网络出版：2025-02-15，
  
  纸质出版：2025
- 稿件说明：
移动端阅览
王志敏,黄骞,王可轩,陈树宽,李敏,王海,李水清. 智慧电厂大数据云平台的架构建设与模型开发研究动力工程学报, 2025, 45(2): 282-291 https://doi.

org/10.19805/j.cnki.jcspe.2025.230703
王志敏,黄骞,王可轩,陈树宽,李敏,王海,李水清. 智慧电厂大数据云平台的架构建设与模型开发研究动力工程学报, 2025, 45(2): 282-291 https://doi. DOI： 10.19805/j.cnki.jcspe.2025.230703.

org/10.19805/j.cnki.jcspe.2025.230703 DOI：

摘要

火电机组运行智慧化是“双碳”目标约束下机组灵活运行的关键技术

基于大数据云计算平台的机组数字化架构是其重要组成部分。但目前现役机组的云平台改造建设尚缺乏深入技术探索及报道。以2台350 MW机组为例

系统开展了火电大数据云平台改造建设的实践研究。首先

发展了利旧的私有云平台搭建技术

实现了整合厂内原有6台服务器资源的低成本部署

同时保障建设期内机组正常运行;其次

针对火电机组万亿级的年数据量存储及电厂应用场景下数据读取需求

发展了云平台数据库数据同步技术

改进了数据库的测点管理方式及数据存储方式

开发了数据接口服务

使云平台数据库实现10

量级测点数据的秒级写入及3 s内全年数据的单点检索。在此基础上

在云平台上部署了依托2.4510

条数据的大数据寻优算法

实现了机组能耗的可视化运行指导;搭建了针对高噪声测量数据的预处理模型

完成了对风量等数据的趋势提取;开发了选择性催化还原(SCR)入口NO

浓度的神经网络预测模型

并通过构建负荷变化因子对预测结果进行反馈调节

可捕捉不同幅度的喷氨量变化需求。上述模型框架均可在云平台实现灵活调整与拓展

所提技术可为火电企业及新型低碳/零碳燃烧能源设备的智慧云平台建设提供参考。

Abstract

Smart coal-fired power generation plays a pivotal role in facilitating flexible unit operations aimed at achieving carbon neutrality. The digital unit architecture based on cloud platforms is an essential component. However

an in-depth exploration concerning the construction of cloud platforms for the existing units is scarce. The research and practice of the critical technologies involved in the construction

of big-data cloud platform for two 350 MW units were performed. A cost-effective deployment technology for cloud platforms integrating six existing servers in the plant was developed

which ensured the normal operation of units throughout the construction phase. Considering the requirements of trillions of data storage annually and data retrieval in the application scenarios of power plant

cloud platform database synchronization technology

database management and storage methodologies

and data interface services were developed. These innovations allow the cloud platform database to achieve second-level write capabilities for 10

-level measurement data and facilitate single-point retrieval within 3 s across an entire year's data. Based on these foundations

a large-scale optimization algorithm utilizing 2.4510

data points was implemented

offering visualized operational guidance for unit energy consumption. A preprocessing model for high-noise measurement data was established

effectively extracting trends from variables such as wind speed. Furthermore

a neural network prediction model for inlet NO

concentration of SCR was developed by integrating a feedback adjustment mechanism that accounts for load variations to address varying demands for ammonia injections. The proposed modeling framework allows for flexible adjustment and expansion

which can be implemented on the cloud platform. The proposed technical solutions offer a reference for the construction of intelligent cloud platforms for thermal power enterprises and novel low-carbon/zero-carbon combustion systems.

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references

GU Yujiong, XU Jing, CHEN Dongchao, et al. Overall review of peak shaving for coal-fired power units in China[J]. Renewable and Sustainable Energy Reviews, 2016, 54: 723-731.

华志刚, 郭荣, 崔希, 等. 火电智慧电厂技术路线探讨与研究[J]. 热力发电, 2019, 48(10): 8-14. HUA Zhigang, GUO Rong, CUI Xi, et al. Discussion and study on technical route of smart thermal power plant[J]. Thermal Power Generation, 2019, 48(10): 8-14.

刘吉臻, 胡勇, 曾德良, 等. 智能发电厂的架构及特征[J]. 中国电机工程学报, 2017, 37(22): 6463-6470. LIU Jizhen, HU Yong, ZENG Deliang, et al. Architecture and feature of smart power generation[J]. Proceedings of the CSEE, 2017, 37(22): 6463-6470.

张文建, 梁庚, 李庚达, 等. 智慧化全数字技术及其在电厂中的应用[J]. 中国电力, 2020, 53(11): 202-211. ZHANG Wenjian, LIANG Geng, LI Gengda, et al. Intelligent full-digital technology and its applications in power plant[J]. Electric Power, 2020, 53(11): 202-211.

张振宇, 刘宗奎, 高文松, 等. 火力发电厂厂级监控信息系统的设计与应用[J]. 能源研究与管理, 2020(4): 77-82. ZHANG Zhenyu, LIU Zongkui, GAO Wensong, et al. Design and application of supervisory information system in thermal power plant[J]. Energy Research and Management, 2020(4): 77-82.

张雄, 肖平, 郭赉佳, 等. 高可用厂级监控信息系统设计和应用[J]. 计算机应用与软件, 2022, 39(10): 35-41, 70. ZHANG Xiong, XIAO Ping, GUO Laijia, et al. The design and application of the high available network of supervisor information system[J]. Computer Applications and Software, 2022, 39(10): 35-41, 70.

刘吉臻, 李云鸷, 宋子秋, 等. 灵活智能燃煤发电技术及评价体系[J]. 动力工程学报, 2022, 42(11): 993-1004, 1012. LIU Jizhen, LI Yunzhi, SONG Ziqiu, et al. Flexible and intelligent coal-fired power generation technology and its evaluation system[J]. Journal of Chinese Society of Power Engineering, 2022, 42(11): 993-1004, 1012.

LIVERA A, TZIOLIS G, FRANQUELO J G, et al. Cloud-based platform for photovoltaic assets diagnosis and maintenance[J]. Energies, 2022, 15(20): 7760.

冯玉山. 智能制造工程理论与实践[M]. 北京: 机械工业出版社, 2021: 367-369.

杨文广, 龙泉, 蒋东翔. 基于云平台的动力设备远程故障诊断系统研究[J]. 制造业自动化, 2017, 39(7): 6-9, 14. YANG Wenguang, LONG Quan, JIANG Dongxiang. Research of the power equipment remote fault diagnosis system based on cloud platform[J]. Manufacturing Automation, 2017, 39(7): 6-9, 14.

SHORT M, TWIDDLE J. An industrial digitalization platform for condition monitoring and predictive maintenance of pumping equipment[J]. Sensors, 2019, 19(17): 3781.

王报辉. CAFe装置的服务器及网络系统设计与监控[D]. 兰州: 中国科学院大学(中国科学院近代物理研究所), 2021.

BROWN T B, MAAN B, RYDER N, et al. Language models are few-shot learners[J]. Proceedings of the 34th Conference on Neural Information Processing Systems, 2020, 33: 1877-1901.

DEVLIN J, CHANG Mingwei, LEE K, et al. BERT: pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Minneapolis, USA: Association for Computational Linguistics, 2019: 4171-4186.

李杰. 工业人工智能[M]. 刘宗长, 高虹安, 贾晓东, 译. 上海: 上海交通大学出版社, 2019: 30-31.

王志敏, 黄骞, 王可轩, 等. 宽负荷下供热机组煤耗实时寻优分析[J]. 中国电机工程学报, 2023, 43(4): 1347-1358. WANG Zhimin, HUANG Qian, WANG Kexuan, et al. Real-time optimization analysis of coal consumption of co-generation units under varied loads[J]. Proceedings of the CSEE, 2023, 43(4): 1347-1358.

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. SHA Xiao, HUANG Qian, LIU Guanqing, 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.

SMREKAR J, POTONIK P, SENEGANIK A. Multi-step-ahead prediction of NOx emissions for a coal-based boiler[J]. Applied Energy, 2013, 106: 89-99.

余廷芳, 耿平, 霍二光, 等. 基于智能算法的燃煤电站锅炉燃烧优化[J]. 动力工程学报, 2016, 36(8): 594-599, 607. YU Tingfang, GENG Ping, HUO Erguang, et al. Combustion optimization of a coal-fired boiler based on intelligent algorithm[J]. Journal of Chinese Society of Power Engineering, 2016, 36(8): 594-599, 607.

DAI Bangwu, WANG Fuli, CHANG Yuqing. Multi-objective economic load dispatch method based on data mining technology for large coal-fired power plants[J]. Control Engineering Practice, 2022, 121:105018.

潘玉松, 陈旭伟, 张鹏. 传统火电厂建设智慧电厂的典型方向及构架体系[J]. 分布式能源, 2018, 3(5): 48-53. PAN Yusong, CHEN Xuwei, ZHANG Peng. Typical direction and structure of a smart power plant based on traditional thermal power plant[J]. Distributed Energy, 2018, 3(5): 48-53.

汤利民, 王大鹏, 吕佩哲, 等. 基于厂级监控信息系统的火电厂数据传输监测系统的开发应用[J]. 热力发电, 2022, 51(9): 111-118. TANG Limin, WANG Dapeng, L Peizhe, et al. Development and application of thermal power plant data transmission monitoring system based on plant-level supervisory information system[J]. Thermal Power Generation, 2022, 51(9): 111-118.

DOELITZSCHER F, SULISTIO A, REICH C, et al. Private cloud for collaboration and e-Learning services: from IaaS to SaaS[J]. Computing, 2011, 91(1): 23-42.

LI Zhenlong, YANG Chaowei, HUANG Qunying, et al. Building model as a service to support geosciences[J]. Computers, Environment and Urban Systems, 2017, 61: 141-152.

李文刚. 基于OpenStack的私有云平台设计与实现[D]. 北京: 中国科学院大学, 2017.

KOZHIRBAYEV Z, SINNOTT R O. A performance comparison of container-based technologies for the Cloud[J]. Future Generation Computer Systems, 2017, 68: 175-182.

SHVACHKO K, KUANG Hairong, RADIA S, et al. The hadoop distributed file system[C]//2010 IEEE 26 th Symposium on Mass Storage Systems and Technologies (MSST). Incline Village, NV, USA: IEEE, 2010: 1-10.

辛园园, 钮俊, 谢志军, 等. 微服务体系结构实现框架综述[J]. 计算机工程与应用, 2018, 54(19): 10-17. XIN Yuanyuan, NIU Jun, XIE Zhijun, et al. Survey of implementation framework for microservices architecture[J]. Computer Engineering and Applications, 2018, 54(19): 10-17.

GAN Wensheng, WAN Shicheng, YU Philip S, et al. Model-as-a-service (MaaS):a survey[C]//2023 IEEE International Conference on Big Data(BigData): Sorrento, Italy, 2023: 4636-4645.

CAO Songling, RHINEHART R R. An efficient method for on-line identification of steady state[J]. Journal of Process Control, 1995, 5(6): 363-374.

CLEVELAND R B, CLEVELAND W S, MCRAE J E, et al. STL: a seasonal-trend decomposition procedure based on loess[J]. Journal of Official Statistics, 1990, 6(1): 3-73.

HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780.

HU Zongyang, LI Mian, FANG Jiuwen, et al. Collaborative optimization for deep peak-shaving and ultra-clean emission of coal-fired boiler using flue gas recycling technology[J]. Energy Sources, 2024, 46(1): 7861-7876.

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