模型-数据混合驱动的电网安全特征选择和知识发现关键技术与工程应用
|
摘要
随着可再生能源的大规模并网、需求响应的逐步实现,电网运行方式的复杂性和波动性不断攀升,电力系统的安全运行正面临新的需求与挑战。因此,基于人工智能技术,在广东电网建立了"模型—数据混合驱动的电网安全特征选择和知识发现平台",保证电网安全、稳定、经济运行。文中首先定义了电网安全特征和知识,阐述了模型—数据混合驱动的思想与具体实现方法,并分析了降低误差的手段;其次阐释了平台的并行计算技术;接着设计了平台的软硬件架构;最后,展示了平台在广东电网的实际应用效果,结果表明:(1)从运行规则制定层面,将运行专家离线制定粗放运行规则的模式,变革为人工智能在线发现精细运行规则的模式;(2)从运行规则应用层面,将调度员人工判定运行规则的模式,变革为人工智能实时判定运行规则的模式。
With the integration of large-scale renewable energy and the implementation of demand response,the power system operation scenarios have become increasingly complicated and variable,leading to new requirements and challenges in power system security operation.Therefore,based on artificial intelligences,a hybrid model and data driven platform for power system security feature selection and knowledge discovery is established in the Guangdong Power Grid in China,to keep power system in secure,stable and economic conditions.Firstly,the definitions of power system security feature and knowledge are put forward,the concepts for the hybrid model and data driven platform are described,and the methods for reducing error are analyzed.Secondly,parallel techniques for the platform are discussed.Thirdly,the software and hardware architecture of the platform is designed.Finally,the application results in the Guangdong Power Grid are demonstrated,which show that:(1)from the perspective of rule-making,the platform transfers the pattern that conservative operation rules should be made by expert offline to the pattern that specific operation rules can be discovered by artificial intelligences online;(2)from the perspective of rule-application,the platform transfers the pattern that operation rules used online should be determined by operators artificially to the pattern that the operation rules can be determined by artificial intelligences automatically.
With the integration of large-scale renewable energy and the implementation of demand response,the power system operation scenarios have become increasingly complicated and variable,leading to new requirements and challenges in power system security operation.Therefore,based on artificial intelligences,a hybrid model and data driven platform for power system security feature selection and knowledge discovery is established in the Guangdong Power Grid in China,to keep power system in secure,stable and economic conditions.Firstly,the definitions of power system security feature and knowledge are put forward,the concepts for the hybrid model and data driven platform are described,and the methods for reducing error are analyzed.Secondly,parallel techniques for the platform are discussed.Thirdly,the software and hardware architecture of the platform is designed.Finally,the application results in the Guangdong Power Grid are demonstrated,which show that:(1)from the perspective of rule-making,the platform transfers the pattern that conservative operation rules should be made by expert offline to the pattern that specific operation rules can be discovered by artificial intelligences online;(2)from the perspective of rule-application,the platform transfers the pattern that operation rules used online should be determined by operators artificially to the pattern that the operation rules can be determined by artificial intelligences automatically.
引文
[1]孙宏斌,黄天恩,郭庆来,等.基于仿真大数据的电网智能型超前安全预警技术[J].南方电网技术,2016,10(3):42-46.SUN Hongbin,HUANG Tianen,GUO Qinglai,et al.Power grid intelligent security early warning technology based on big simulation data[J].Southern Power System Technology,2016,10(3):42-46.
[2]中华人民共和国国务院.促进大数据发展行动纲要[EB/OL].[2018-02-10].http://www.gov.cn/gongbao/content/2015/content_2929345.htm.The State Council of the People’s Republic of China.Action outline for promoting the development of big data[EB/OL].[2018-02-10].http://www.gov.cn/gongbao/content/2015/content_2929345.htm.
[3]中华人民共和国务院.新一代人工智能发展规划[EB/OL].[2018-02-10].http://www.gov.cn/zhengce/content/2017-07/20/content_5211996.htm.The State Council of the People’s Republic of China.Development planning for a new generation of artificial intelligence[EB/OL].[2018-02-10].http://www.gov.cn/zhengce/content/2017-07/20/content_5211996.htm.
[4]薛禹胜,赖业宁.大能源思维与大数据思维的融合:(一)大数据与电力大数据[J].电力系统自动化,2016,40(1):1-8.DOI:10.7500/AEPS20151208005.XUE Yusheng,LAI Yening.Integration of macro energy thinking and big data thinking:Part one big data and power big data[J].Automation of Electric Power Systems,2016,40(1):1-8.DOI:10.7500/AEPS20151208005.
[5]薛禹胜,赖业宁.大能源思维与大数据思维的融合:(二)应用及探索[J].电力系统自动化,2016,40(8):1-13.DOI:10.7500/AEPS20160311004.XUE Yusheng,LAI Yening.Integration of macro energy thinking and big data thinking:Part two applications and explorations[J].Automation of Electric Power Systems,2016,40(8):1-13.DOI:10.7500/AEPS20160311004.
[6]冷喜武,陈国平,蒋宇,等.智能电网监控运行大数据应用模型构建方法[J].电力系统自动化,2018,42(20):115-122.DOI:10.7500/AEPS20170928003.LENG Xiwu,CHEN Guoping,JIANG Yu,et al.Model construction method of big data application for monitoring and control of smart grid[J].Automation of Electric Power Systems,2018,42(20):115-122.DOI:10.7500/AEPS20170928003.
[7]冷喜武,陈国平,白静洁,等.智能电网监控运行大数据分析系统总体设计[J].电力系统自动化,2018,42(12):160-166.DOI:10.7500/AEPS20170920002.LENG Xiwu,CHEN Guoping,BAI Jingjie,et al.General design of smart grid monitoring operation big data analysis system[J].Automation of Electric Power Systems,2018,42(12):160-166.DOI:10.7500/AEPS20170920002.
[8]张东霞,苗新,刘丽平,等.智能电网大数据技术发展研究[J].中国电机工程学报,2015,35(1):2-12.ZHANG Dongxia,MIAO Xin,LIU Liping,et al.Research on development strategy for smart grid big data[J].Proceedings of the CSEE,2015,35(1):2-12.
[9]彭小圣,邓迪元,程时杰,等.面向智能电网应用的电力大数据关键技术[J].中国电机工程学报,2015,35(3):503-511.PENG Xiaosheng,DENG Diyuan,CHENG Shijie,et al.Key technologies of electric power big data and its application prospects in smart grid[J].Proceedings of the CSEE,2015,35(3):503-511.
[10]刘世成,张东霞,朱朝阳,等.能源互联网中大数据技术思考[J].电力系统自动化,2016,40(8):14-21.DOI:10.7500/AEPS20151106007.LIU Shicheng,ZHANG Dongxia,ZHU Chaoyang,et al.Aview on big data in Energy Internet[J].Automation of Electric Power Systems,2016,40(8):14-21.DOI:10.7500/AEPS20151106007.
[11]赵俊华,董朝阳,文福拴,等.面向能源系统的数据科学:理论、技术与展望[J].电力系统自动化,2017,41(4):1-11.DOI:10.7500/AEPS20160813002.ZHAO Junhua,DONG Zhaoyang,WEN Fushuan,et al.Data science for energy systems:theory,techniques and prospect[J].Automation of Electric Power Systems,2017,41(4):1-11.DOI:10.7500/AEPS20160813002.
[12]程乐峰,余涛,张孝顺,等.信息-物理-社会融合的智慧能源调度机器人及其知识自动化:框架、技术与挑战[J].中国电机工程学报,2018,38(1):25-40.CHENG Lefeng,YU Tao,ZHANG Xiaoshun,et al.Cyberphysical-social systems based smart energy robotic dispatcher and its knowledge automation:framework,techniques and challenges[J].Proceedings of the CSEE,2018,38(1):25-40.
[13]尚宇炜,马钊,彭晨阳,等.内嵌专业知识和经验的机器学习方法探索:(一)引导学习的提出与理论基础[J].中国电机工程学报,2017,37(19):5560-5571.SHANG Yuwei,MA Zhao,PENG Chenyang,et al.Study of a novel machine learning method embedding expertise:PartⅠproposals and fundamentals of guiding learning[J].Proceedings of the CSEE,2017,37(19):5560-5571.
[14]黄彦浩,于之虹,谢昶,等.电力大数据技术与电力系统仿真计算结合问题研究[J].中国电机工程学报,2015,35(1):13-22.HUANG Yanhao,YU Zhihong,XIE Chang,et al.Study on application of electric power big data technology in power system simulation[J].Proceedings of the CSEE,2015,35(1):13-22.
[15]赵峰,孙宏斌,张伯明.基于电气分区的输电断面及其自动发现[J].电力系统自动化,2011,35(5):42-46.ZHAO Feng,SUN Hongbin,ZHANG Boming.Electrical zone division based automatic discovery of flowgates[J].Automation of Electric Power Systems,2011,35(5):42-46.
[16]蒋维勇,张伯明,吴文传,等.应用于在线调度决策的极限传输容量计算方法[J].电力系统自动化,2008,32(10):12-17.JIANG Weiyong,ZHANG Boming,WU Wenchuan,et al.Atotal transfer capability calculation method for power system operation and decision[J].Automation of Electric Power Systems,2008,32(10):12-17.
[17]孙宏斌,赵峰,蒋维勇,等.电网精细规则在线自动发现系统架构与功能设计[J].电力系统自动化,2011,35(18):81-86.SUN Hongbin,ZHAO Feng,JIANG Weiyong,et al.Framework and functions of fine operational rules online automatic discovery system for power grid[J].Automation of Electric Power Systems,2011,35(18):81-86.
[18]赵峰,孙宏斌,黄天恩,等.电网关键断面及安全运行规则自动发现系统设计与工程实现[J].电力系统自动化,2015,39(1):117-123.DOI:10.7500/AEPS20140930009.ZHAO Feng,SUN Hongbin,HUANG Tianen,et al.Design and engineering application of automatic discovery system for critical flowgates and security operation rules in power grids[J].Automation of Electric Power Systems,2015,39(1):117-123.DOI:10.7500/AEPS20140930009.
[19]黄天恩,孙宏斌,郭庆来,等.基于电网运行大数据的在线分布式安全特征选择[J].电力系统自动化,2016,40(4):32-40.DOI:10.7500/AEPS20150424003.HUANG Tianen,SUN Hongbin,GUO Qinglai,et al.Online distributed security feature selection based on big data in power system operation[J].Automation of Electric Power Systems,2016,40(4):32-40.DOI:10.7500/AEPS20150424003.
[20]黄天恩,孙宏斌,郭庆来,等.基于电网运行仿真大数据的知识管理和超前安全预警[J].电网技术,2015,39(11):3080-3087.HUANG Tianen,SUN Hongbin,GUO Qinglai,et al.Knowledge management and security early warning based on big simulation data in power grid operation[J].Power System Technology,2015,39(11):3080-3087.
[21]NG A.Learning theory[EB/OL].[2018-02-10].https://www.stanford.edu/class/cs229/notes/cs229-notes4.ps.
[22]VAPNIK V,LEVIN E,CUN Y.Measuring the VC-dimension of a learning machine[J].Neural Computation,1994,6(5):851-876.
[23]DEAN J,CORRADO G,MONGA R,et al.Large scale distributed deep networks[J/OL].Advances in Neural Information Processing Systems[2018-02-10].DOI:10.1109/ICDAR.2011.95.
[24]周二专,冯东豪,武志刚.内存计算技术及在电网分析中的应用[J].电力系统自动化,2017,41(11):1-7.DOI:10.7500/AEPS20161124004.ZHOU Erzhuan,FENG Donghao,WU Zhigang.In-memory computing and its application to power system analysis[J].Automation of Electric Power Systems,2017,41(11):1-7.DOI:10.7500/AEPS20161124004.
[25]李烨.分布式支持向量机算法研究[D].青岛:山东科技大学,2012.LI Ye.Research on distributed support vector machine algorithm[D].Qingdao:Shandong University of Science and Technology,2012.
[2]中华人民共和国国务院.促进大数据发展行动纲要[EB/OL].[2018-02-10].http://www.gov.cn/gongbao/content/2015/content_2929345.htm.The State Council of the People’s Republic of China.Action outline for promoting the development of big data[EB/OL].[2018-02-10].http://www.gov.cn/gongbao/content/2015/content_2929345.htm.
[3]中华人民共和国务院.新一代人工智能发展规划[EB/OL].[2018-02-10].http://www.gov.cn/zhengce/content/2017-07/20/content_5211996.htm.The State Council of the People’s Republic of China.Development planning for a new generation of artificial intelligence[EB/OL].[2018-02-10].http://www.gov.cn/zhengce/content/2017-07/20/content_5211996.htm.
[4]薛禹胜,赖业宁.大能源思维与大数据思维的融合:(一)大数据与电力大数据[J].电力系统自动化,2016,40(1):1-8.DOI:10.7500/AEPS20151208005.XUE Yusheng,LAI Yening.Integration of macro energy thinking and big data thinking:Part one big data and power big data[J].Automation of Electric Power Systems,2016,40(1):1-8.DOI:10.7500/AEPS20151208005.
[5]薛禹胜,赖业宁.大能源思维与大数据思维的融合:(二)应用及探索[J].电力系统自动化,2016,40(8):1-13.DOI:10.7500/AEPS20160311004.XUE Yusheng,LAI Yening.Integration of macro energy thinking and big data thinking:Part two applications and explorations[J].Automation of Electric Power Systems,2016,40(8):1-13.DOI:10.7500/AEPS20160311004.
[6]冷喜武,陈国平,蒋宇,等.智能电网监控运行大数据应用模型构建方法[J].电力系统自动化,2018,42(20):115-122.DOI:10.7500/AEPS20170928003.LENG Xiwu,CHEN Guoping,JIANG Yu,et al.Model construction method of big data application for monitoring and control of smart grid[J].Automation of Electric Power Systems,2018,42(20):115-122.DOI:10.7500/AEPS20170928003.
[7]冷喜武,陈国平,白静洁,等.智能电网监控运行大数据分析系统总体设计[J].电力系统自动化,2018,42(12):160-166.DOI:10.7500/AEPS20170920002.LENG Xiwu,CHEN Guoping,BAI Jingjie,et al.General design of smart grid monitoring operation big data analysis system[J].Automation of Electric Power Systems,2018,42(12):160-166.DOI:10.7500/AEPS20170920002.
[8]张东霞,苗新,刘丽平,等.智能电网大数据技术发展研究[J].中国电机工程学报,2015,35(1):2-12.ZHANG Dongxia,MIAO Xin,LIU Liping,et al.Research on development strategy for smart grid big data[J].Proceedings of the CSEE,2015,35(1):2-12.
[9]彭小圣,邓迪元,程时杰,等.面向智能电网应用的电力大数据关键技术[J].中国电机工程学报,2015,35(3):503-511.PENG Xiaosheng,DENG Diyuan,CHENG Shijie,et al.Key technologies of electric power big data and its application prospects in smart grid[J].Proceedings of the CSEE,2015,35(3):503-511.
[10]刘世成,张东霞,朱朝阳,等.能源互联网中大数据技术思考[J].电力系统自动化,2016,40(8):14-21.DOI:10.7500/AEPS20151106007.LIU Shicheng,ZHANG Dongxia,ZHU Chaoyang,et al.Aview on big data in Energy Internet[J].Automation of Electric Power Systems,2016,40(8):14-21.DOI:10.7500/AEPS20151106007.
[11]赵俊华,董朝阳,文福拴,等.面向能源系统的数据科学:理论、技术与展望[J].电力系统自动化,2017,41(4):1-11.DOI:10.7500/AEPS20160813002.ZHAO Junhua,DONG Zhaoyang,WEN Fushuan,et al.Data science for energy systems:theory,techniques and prospect[J].Automation of Electric Power Systems,2017,41(4):1-11.DOI:10.7500/AEPS20160813002.
[12]程乐峰,余涛,张孝顺,等.信息-物理-社会融合的智慧能源调度机器人及其知识自动化:框架、技术与挑战[J].中国电机工程学报,2018,38(1):25-40.CHENG Lefeng,YU Tao,ZHANG Xiaoshun,et al.Cyberphysical-social systems based smart energy robotic dispatcher and its knowledge automation:framework,techniques and challenges[J].Proceedings of the CSEE,2018,38(1):25-40.
[13]尚宇炜,马钊,彭晨阳,等.内嵌专业知识和经验的机器学习方法探索:(一)引导学习的提出与理论基础[J].中国电机工程学报,2017,37(19):5560-5571.SHANG Yuwei,MA Zhao,PENG Chenyang,et al.Study of a novel machine learning method embedding expertise:PartⅠproposals and fundamentals of guiding learning[J].Proceedings of the CSEE,2017,37(19):5560-5571.
[14]黄彦浩,于之虹,谢昶,等.电力大数据技术与电力系统仿真计算结合问题研究[J].中国电机工程学报,2015,35(1):13-22.HUANG Yanhao,YU Zhihong,XIE Chang,et al.Study on application of electric power big data technology in power system simulation[J].Proceedings of the CSEE,2015,35(1):13-22.
[15]赵峰,孙宏斌,张伯明.基于电气分区的输电断面及其自动发现[J].电力系统自动化,2011,35(5):42-46.ZHAO Feng,SUN Hongbin,ZHANG Boming.Electrical zone division based automatic discovery of flowgates[J].Automation of Electric Power Systems,2011,35(5):42-46.
[16]蒋维勇,张伯明,吴文传,等.应用于在线调度决策的极限传输容量计算方法[J].电力系统自动化,2008,32(10):12-17.JIANG Weiyong,ZHANG Boming,WU Wenchuan,et al.Atotal transfer capability calculation method for power system operation and decision[J].Automation of Electric Power Systems,2008,32(10):12-17.
[17]孙宏斌,赵峰,蒋维勇,等.电网精细规则在线自动发现系统架构与功能设计[J].电力系统自动化,2011,35(18):81-86.SUN Hongbin,ZHAO Feng,JIANG Weiyong,et al.Framework and functions of fine operational rules online automatic discovery system for power grid[J].Automation of Electric Power Systems,2011,35(18):81-86.
[18]赵峰,孙宏斌,黄天恩,等.电网关键断面及安全运行规则自动发现系统设计与工程实现[J].电力系统自动化,2015,39(1):117-123.DOI:10.7500/AEPS20140930009.ZHAO Feng,SUN Hongbin,HUANG Tianen,et al.Design and engineering application of automatic discovery system for critical flowgates and security operation rules in power grids[J].Automation of Electric Power Systems,2015,39(1):117-123.DOI:10.7500/AEPS20140930009.
[19]黄天恩,孙宏斌,郭庆来,等.基于电网运行大数据的在线分布式安全特征选择[J].电力系统自动化,2016,40(4):32-40.DOI:10.7500/AEPS20150424003.HUANG Tianen,SUN Hongbin,GUO Qinglai,et al.Online distributed security feature selection based on big data in power system operation[J].Automation of Electric Power Systems,2016,40(4):32-40.DOI:10.7500/AEPS20150424003.
[20]黄天恩,孙宏斌,郭庆来,等.基于电网运行仿真大数据的知识管理和超前安全预警[J].电网技术,2015,39(11):3080-3087.HUANG Tianen,SUN Hongbin,GUO Qinglai,et al.Knowledge management and security early warning based on big simulation data in power grid operation[J].Power System Technology,2015,39(11):3080-3087.
[21]NG A.Learning theory[EB/OL].[2018-02-10].https://www.stanford.edu/class/cs229/notes/cs229-notes4.ps.
[22]VAPNIK V,LEVIN E,CUN Y.Measuring the VC-dimension of a learning machine[J].Neural Computation,1994,6(5):851-876.
[23]DEAN J,CORRADO G,MONGA R,et al.Large scale distributed deep networks[J/OL].Advances in Neural Information Processing Systems[2018-02-10].DOI:10.1109/ICDAR.2011.95.
[24]周二专,冯东豪,武志刚.内存计算技术及在电网分析中的应用[J].电力系统自动化,2017,41(11):1-7.DOI:10.7500/AEPS20161124004.ZHOU Erzhuan,FENG Donghao,WU Zhigang.In-memory computing and its application to power system analysis[J].Automation of Electric Power Systems,2017,41(11):1-7.DOI:10.7500/AEPS20161124004.
[25]李烨.分布式支持向量机算法研究[D].青岛:山东科技大学,2012.LI Ye.Research on distributed support vector machine algorithm[D].Qingdao:Shandong University of Science and Technology,2012.