电网调控机器人设计思路
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摘要
随着中国电网规模持续扩大和运行特性日趋复杂,电网调控业务复杂度及调控人员承载力强度日益增加。文中从电网实时调控业务出发,设计了电网调控机器人整体架构,详细阐述了智能学习、智能决策、智能监视、智能执行、智能交互等5个功能模块的设计思路;然后介绍了相应的关键技术,并给出了适用的6类调控应用场景。
As the scale of power grid of China continues to expand and the operation characteristics become increasingly complex, the complexity of dispatching and control business of power grid and the burden of dispatchers are increasing. According to the real-time dispatching and control business of power grid, this paper designs an overall architecture of robotic dispatcher for power grid. The design ideas of five functional modules such as intelligent learning, intelligent decision-making, intelligent monitoring, intelligent execution and intelligent interaction are elaborated. Then the corresponding key technologies are described. The six types of application scenarios of dispatching and control are given.
As the scale of power grid of China continues to expand and the operation characteristics become increasingly complex, the complexity of dispatching and control business of power grid and the burden of dispatchers are increasing. According to the real-time dispatching and control business of power grid, this paper designs an overall architecture of robotic dispatcher for power grid. The design ideas of five functional modules such as intelligent learning, intelligent decision-making, intelligent monitoring, intelligent execution and intelligent interaction are elaborated. Then the corresponding key technologies are described. The six types of application scenarios of dispatching and control are given.
引文
[1] 郑南宁.人工智能面临的挑战[J].自动化学报,2016,42(5):641-642.ZHENG Naning.The challenge of artificial intelligence[J].Acta Automatica Sinica,2016,42(5):641-642.
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[11] ZHU Lipeng,LU Chao,SUN Yuanzhang.Time series shapelet classification based online short-term voltage stability assessment[J].IEEE Transactions on Power Systems,2016,31(2):1430-1439.
[12] YU Tao,ZHOU Bin,CHAN K W,et al.Stochastic optimal relaxed automatic generation control in non-Markov environment based on multi-step Q(λ) learning[J].IEEE Transactions on Power Systems,2011,26(3):1272-1282.
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[14] GLAVIC M.Design of a resistive brake controller for power system stability enhancement using reinforcement learning[J].IEEE Transactions on Control Systems Technology,2005,13(5):743-751.
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[17] 辛耀中,石俊杰,周京阳,等.智能电网调度控制系统现状与技术展望[J].电力系统自动化,2015,39(1):2-8.XIN Yaozhong,DAN Junjie,ZHOU Jingyang,et al.Technology development trends of smart grid dispatching and control systems[J].Automation of Electric Power Systems,2015,39(1):2-8.
[18] 许洪强,姚建国,南贵林,等.未来电网调度控制系统应用功能的新特征[J].电力系统自动化,2018,42(1):1-7.DOI:10.7500/AEPS20170518001.XU Hongqiang,YAO Jianguo,NAN Guilin,et al.New features of application function for future dispatching and control systems[J].Automation of Electric Power Systems,2018,42(1):1-7.DOI:10.7500/AEPS20170518001.
[19] 李明节.大规模特高压交直流混联电网特性分析与运行控制[J].电网技术,2016,40(4):985-991.LI Mingjie.Characteristic analysis and operational control of large-scale hybrid UHV AC/DC power grids[J].Power System Technology,2016,40(4):985-991.
[20] ZHOU M,YAN J F.A new solution architecture for online power system analysis[J].CSEE Journal of Power and Energy Systems,2018,4(2):250-256.
[21] 闪鑫,陆晓,翟明玉,等.人工智能应用于电网调控的关键技术分析[J].电力系统自动化,2019,43(1):49-57.DOI:10.7500/AEPS20180629002.SHAN Xin,LU Xiao,ZHAI Mingyu,et al.Analysis of key technologies for artificial intelligence applied to power grid dispatch and control[J].Automation of Electric Power Systems,2019,43(1):49-57.DOI:10.7500/AEPS 20180629002.
[22] 黄天恩,郭庆来,孙宏斌,等.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[J].电力系统自动化,2019,43(1):95-101.DOI:10.7500/AEPS20180614002.HUANG Tianen,GUO Qinglai,SUN Hongbin,et al.Hybrid model and data driven concepts for power system security feature selection and knowledge discovery:key technologies and engineering application[J].Automation of Electric Power Systems,2019,43(1):95-101.DOI:10.7500/AEPS 20180614002.
[2] 中国电子技术标准化研究院.人工智能标准化白皮书[EB/OL].[2018-01-24].http://www.cesi.ac.cn/images/editor/2018 0124/20180124135528742.pdf.China Electronics Standardization Institute.White paper on AI standardization[EB/OL].[2018-01-24].http://www.cesi.ac.cn/images/editor/20180124/20180124135528742.pdf.
[3] DY-LIACCO T E.Enhancing power system security control[J].IEEE Computer Applications in Power,2002,10(3):38-41.
[4] EPRI.IntelliGrid:smart power for the 21st century[R/OL].http://my.epri.com/portal/server.pt?Product_id=00000000 0001012094.
[5] OTT A L.Development of smart dispatch tools in the PJM market[C]// IEEE Power & Energy Society General Meeting,July 24-29,2011,Detroit,USA:3p.
[6] 卢强.数字电力系统(DPS)[J].电力系统自动化,2000,24(9):1-4.LU Qiang.Digital power system,automation of electric power system[J].Automation of Electric Power Systems,2000,24(9):1-4.
[7] 卢强,戚晓耀,何光宇.智能电网与智能广域机器人[J].中国电机工程学报,2011,31(10):1-5.LU Qiang,QI Xiaoyao,HE Guangyu.Smart grid and smart wide area robot[J].Proceedings of the CSEE,2011,31(10):1-5.
[8] 孙宏斌,张伯明,吴文传,等.面向中国智能输电网的智能控制中心[J].电力科学与技术学报,2009,24(2):2-7.SUN Hongbin,ZHANG Boming,WU Wenchuan,et al.Smart control center for Chinese smart transmission grid[J].Journal of Electric Power Science and Technology,2009,24(2):2-7.
[9] 杨胜春,汤必强,姚建国,等.基于态势感知的电网自动智能调度架构及关键技术[J].电网技术,2014,38(1):3635-3641.YANG Shengchun,TANG Biqiang,YAO Jianguo,et al.Architecture and key technologies for situational awareness based automatic intelligent dispatching of power grid[J].Power System Technology,2014,38(1):3635-3641.
[10] 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.Cyber-physical-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.
[11] ZHU Lipeng,LU Chao,SUN Yuanzhang.Time series shapelet classification based online short-term voltage stability assessment[J].IEEE Transactions on Power Systems,2016,31(2):1430-1439.
[12] YU Tao,ZHOU Bin,CHAN K W,et al.Stochastic optimal relaxed automatic generation control in non-Markov environment based on multi-step Q(λ) learning[J].IEEE Transactions on Power Systems,2011,26(3):1272-1282.
[13] ERNST D,GLAVIC M,WEHENKEL L.Power systems stability control:reinforcement learning framework[J].IEEE Transactions on Power Systems,2004,19(1):427-435.
[14] GLAVIC M.Design of a resistive brake controller for power system stability enhancement using reinforcement learning[J].IEEE Transactions on Control Systems Technology,2005,13(5):743-751.
[15] GLAVIC M,ERNST D,WEHENKEL L.Combining a stability and a performance-oriented control in power systems[J].IEEE Transactions on Power Systems,2005,20(1):525-526.
[16] 韦化,龙丹丽,黎静华.求解大规模机组组合问题的策略迭代近似动态规划[J].中国电机工程学报,2014,34(25):4420-4429.WEI Hua,LONG Danli,LI Jinghua.Policy iteration-approximate dynamic programming for large scale unit commitment problems[J].Proceedings of the CSEE,2014,34(25):4420-4429.
[17] 辛耀中,石俊杰,周京阳,等.智能电网调度控制系统现状与技术展望[J].电力系统自动化,2015,39(1):2-8.XIN Yaozhong,DAN Junjie,ZHOU Jingyang,et al.Technology development trends of smart grid dispatching and control systems[J].Automation of Electric Power Systems,2015,39(1):2-8.
[18] 许洪强,姚建国,南贵林,等.未来电网调度控制系统应用功能的新特征[J].电力系统自动化,2018,42(1):1-7.DOI:10.7500/AEPS20170518001.XU Hongqiang,YAO Jianguo,NAN Guilin,et al.New features of application function for future dispatching and control systems[J].Automation of Electric Power Systems,2018,42(1):1-7.DOI:10.7500/AEPS20170518001.
[19] 李明节.大规模特高压交直流混联电网特性分析与运行控制[J].电网技术,2016,40(4):985-991.LI Mingjie.Characteristic analysis and operational control of large-scale hybrid UHV AC/DC power grids[J].Power System Technology,2016,40(4):985-991.
[20] ZHOU M,YAN J F.A new solution architecture for online power system analysis[J].CSEE Journal of Power and Energy Systems,2018,4(2):250-256.
[21] 闪鑫,陆晓,翟明玉,等.人工智能应用于电网调控的关键技术分析[J].电力系统自动化,2019,43(1):49-57.DOI:10.7500/AEPS20180629002.SHAN Xin,LU Xiao,ZHAI Mingyu,et al.Analysis of key technologies for artificial intelligence applied to power grid dispatch and control[J].Automation of Electric Power Systems,2019,43(1):49-57.DOI:10.7500/AEPS 20180629002.
[22] 黄天恩,郭庆来,孙宏斌,等.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[J].电力系统自动化,2019,43(1):95-101.DOI:10.7500/AEPS20180614002.HUANG Tianen,GUO Qinglai,SUN Hongbin,et al.Hybrid model and data driven concepts for power system security feature selection and knowledge discovery:key technologies and engineering application[J].Automation of Electric Power Systems,2019,43(1):95-101.DOI:10.7500/AEPS 20180614002.