智能电站数据中台建设与应用

doi:10.11930/j.issn.1004-9649.202006220

中国电力 ›› 2021, Vol. 54 ›› Issue (3): 61-67,176.DOI: 10.11930/j.issn.1004-9649.202006220

• 国家“十三五”智能电网重大专项专栏：（六）先进计算与人工智能技术专栏 • 上一篇下一篇

智能电站数据中台建设与应用

王毅, 王智微, 何新

西安热工研究院有限公司，陕西西安 710054

收稿日期:2020-06-23 修回日期:2021-01-18 出版日期:2021-03-05 发布日期:2021-03-17
作者简介:王毅(1980-)，男，硕士，正高级工程师，从事发电企业信息化与智能电站研究，E-mail：wangyi@tpri.com.cn
基金资助:
中国华能集团公司科技攻关项目(智能电站关键技术研究及应用，HNKJ19-H34)

Data Middle Platform Construction and Application of Intelligent Power Stations

WANG Yi, WANG Zhiwei, HE Xin

Xi'an Thermal Power Research Institute Co., Ltd., Xi'an 710032, China

Received:2020-06-23 Revised:2021-01-18 Online:2021-03-05 Published:2021-03-17
Supported by:
This work is supported by Science and Technology Project of Huaneng Group (Key Technology Research and Application of Intelligent Power Station, No.HNKJ19-H34)

摘要/Abstract

摘要： 在电站智能化建设过程中，为解决现有系统的数据孤岛、数据利用率低等问题，设计了数据中台产品。基于全域数据采集、数据湖底座建设、数据萃取与分析、数据服务化交付、数据可视化关键技术，建立了智能电站数据中台，实现了多源异构数据融合、共享和服务能力，有效提升电站精益化运行管理水平。最后介绍了智能电站数据中台的典型应用场景，对发电企业建设和应用数据中台具有一定的参考价值。

关键词: 智能电站, 数据中台, 数据湖, 数据萃取, 数据治理, 数据可视化

Abstract: In order to solve such problems as data island and low data utilization rate of existing systems for intelligent power station construction, a data middle platform is designed. Based on the key technologies of data collection, data lake, data extraction and analysis, data service delivery and data visualization, a data middle platform of intelligent power station is established, which realizes the ability of multi-source heterogeneous data fusion as well as sharing and service, and is of great significance to improving the operation and management level of power stations. Finally, the paper introduces the typical application scenarios of intelligent power station data middle platform, which has a certain reference values for construction and application of the data middle platform.

Key words: intelligent power station, data middle platform, data lake, data extraction, data government, data visualization

王毅, 王智微, 何新. 智能电站数据中台建设与应用[J]. 中国电力, 2021, 54(3): 61-67,176.

WANG Yi, WANG Zhiwei, HE Xin. Data Middle Platform Construction and Application of Intelligent Power Stations[J]. Electric Power, 2021, 54(3): 61-67,176.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202006220

https://www.electricpower.com.cn/CN/Y2021/V54/I3/61

参考文献

[1] 张晋宾, 周四维. 智能电厂概念及体系架构模型研究[J]. 中国电力, 2018, 51(10): 2-7, 42
ZHANG Jinbin, ZHOU Siwei. Study on the concept of the smart power plant and its architecture model[J]. Electric Power, 2018, 51(10): 2-7, 42
[2] 杨新民, 陈丰, 曾卫东, 等. 智能电站的概念及结构[J]. 热力发电, 2015, 44(11): 10-13
YANG Xinmin, CHEN Feng, ZENG Weidong, et al. Concept and structure of intelligent power stations[J]. Thermal Power Generation, 2015, 44(11): 10-13
[3] 郭为民, 张广涛, 李炳楠, 等. 火电厂智能化建设规划与技术路线[J]. 中国电力, 2018, 51(10): 17-25
GUO Weimin, ZHANG Guangtao, LI Bingnan, et al. Construction planning and technical route for thermal power plant intelligentization[J]. Electric Power, 2018, 51(10): 17-25
[4] 罗军舟, 金嘉晖, 宋爱波, 等. 云计算: 体系架构与关键技术[J]. 通信学报, 2011, 32(7): 3-21
LUO Junzhou, JIN Jiahui, SONG Aibo, et al. Cloud computing: architecture and key technologies[J]. Journal on Communications, 2011, 32(7): 3-21
[5] 王申华, 何湘威, 方小方, 等. 基于泛在电力物联网多源信息的电网动态风险评估系统[J]. 中国电力, 2019, 52(12): 10-19
WANG Shenhua, HE Xiangwei, FANG Xiaofang, et al. Dynamic risk assessment system for power system based on multi-source information of the ubiquitous power Internet of Things[J]. Electric Power, 2019, 52(12): 10-19
[6] 陈霖, 许爱东, 蒋屹新, 等. 基于动态增量聚类分析的电力信息网络攻击模式识别算法[J]. 南方电网技术, 2020, 14(8): 25-32
CHEN Lin, XU Aidong, JIANG Yixin, et al. Attack pattern recognition algorithm of power information network based on dynamic incremental cluster analysis[J]. Southern Power System Technology, 2020, 14(8): 25-32
[7] 王忠杰, 文乐, 杨新民. 大数据在智能化电厂中的应用研究与展望[J]. 中国电力, 2019, 52(3): 133-139
WANG Zhongjie, WEN Le, YANG Xinmin. Applied research and prospect of big data in power plant intelligence[J]. Electric Power, 2019, 52(3): 133-139
[8] 赵增涛, 罗勇, 梁成辉. 电力企业中台云化构建及大数据分析研究[J]. 水电与抽水蓄能, 2019, 5(5): 70-74
ZHAO Zengtao, LUO Yong, LIANG Chenghui. Research on middle platform construction and large data analysis of power enterprises based on aliyun[J]. Hydropower and Pumped Storage, 2019, 5(5): 70-74
[9] 王静怡. 电力厂区防涝水位实时预警系统设计与应用[J]. 中国电力, 2019, 52(5): 127-133
WANG Jingyi. Design and application of real-time monitoring system for local rainstorm water level in substation[J]. Electric Power, 2019, 52(5): 127-133
[10] 俞华, 穆广祺, 牛津文, 等. 智能变电站网络安全防护应用研究[J]. 电力系统保护与控制, 2021, 49(1): 115-124
YU Hua, MU Guangqi, NIU Jinwen, et al. Application research on network security protection of an intelligent substation[J]. Power System Protection and Control, 2021, 49(1): 115-124
[11] 钱斌, 蔡梓文, 肖勇, 等. 基于边缘计算的电表计量系统数据协同检测方案[J]. 中国电力, 2019, 52(11): 145-152
QIAN Bin, CAI Ziwen, XIAO Yong, et al. Data collaborative detection scheme of electric metering system based on edge computing[J]. Electric Power, 2019, 52(11): 145-152
[12] 雷军, 叶航军, 武泽胜, 等. 基于开源生态系统的大数据平台研究[J]. 计算机研究与发展, 2017, 54(1): 80-93
LEI Jun, YE Hangjun, WU Zesheng, et al. Big-data platform based on open source ecosystem[J]. Journal of Computer Research and Development, 2017, 54(1): 80-93
[13] 吴凯峰, 刘万涛, 李彦虎, 等. 基于云计算的电力大数据分析技术与应用[J]. 中国电力, 2015, 48(2): 111-116, 127
WU Kaifeng, LIU Wantao, LI Yanhu, et al. Cloud-computing based power big data analysis technology and its application[J]. Electric Power, 2015, 48(2): 111-116, 127
[14] 周尧. 面向服务的一站式机器学习算法系统的设计与实现[D]. 南京: 南京大学, 2019.
ZHOU Yao. The design and implementation of service-oriented one-stop machine learning algorithm system[D]. Nanjing: Nanjing University, 2019.
[15] 甘似禹, 车品觉, 杨天顺, 等. 大数据治理体系[J]. 计算机应用与软件, 2018, 35(6): 1-8, 69
GAN Shiyu, CHE Pinjue, YANG Tianshun, et al. Big data governance system[J]. Computer Applications and Software, 2018, 35(6): 1-8, 69
[16] 陶鹏, 张洋瑞, 李梦宇, 等. 基于Graph模型的海量用电数据并行聚类分析[J]. 电力科学与技术学报, 2020, 35(6): 144-151
TAO Peng, ZHANG Yangrui, LI Mengyu, et al. Parallel clustering analysis for power consumption data based on graph model[J]. Journal of Electric Power Science and Technology, 2020, 35(6): 144-151
[17] 中国环境保护产业协会电除尘委员会. 燃煤电厂烟气超低排放技术[M]. 北京: 中国电力出版社, 2015.
[18] 关维竹, 陈鸥, 祝业青. 中国燃煤电厂二氧化硫污染控制工作分析与建议[J]. 中国电力, 2017, 50(5): 172-177
GUAN Weizhu, CHEN Ou, ZHU Yeqing. Analysis and suggestions on sulfur dioxide emission control for coal-fired power plants in China[J]. Electric Power, 2017, 50(5): 172-177
[19] 程换新, 于沙家. 面向脱硫系统的BP神经网络预测控制[J]. 石油化工自动化, 2015, 51(5): 39-42
CHENG Huanxin, YU Shajia. BP neural network prediction control for desulfurization system[J]. Automation in Petro-Chemical Industry, 2015, 51(5): 39-42
[20] 孟范伟, 徐博, 吕晓永, 等. 神经网络预测控制在SCR烟气脱硝系统中应用[J]. 东北大学学报(自然科学版), 2017, 38(6): 778-782
MENG Fanwei, XU Bo, LYU Xiaoyong, et al. Application of neural network predictive control in SCR flue gas denitration system[J]. Journal of Northeastern University (Natural Science), 2017, 38(6): 778-782
[21] 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

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