基于大数据及人工智能的大电网智能调控系统框架
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  • 英文篇名:Intelligent Control System Framework Based on Big Data and Artificial Intelligence for Large Power Grid
  • 作者:刘道伟 ; 李柏青 ; 邵广惠 ; 李泽宇 ; 高德宾 ; 徐兴伟 ; 赵高尚 ; 李宗翰 ; 李京
  • 英文作者:LIU Daowei;LI Baiqing;SHAO Guanghui;LI Zeyu;GAO Debin;XU Xingwei;ZHAO Gaoshang;LI Zonghan;LI Jing;China Electric Power Research Institute;Northeast Branch,State Grid Corporation of China;
  • 关键词:大数据技术 ; 人工智能技术 ; 智能调度 ; 态势评估
  • 英文关键词:big data technology;;artificial intelligence;;intelligent scheduling;;situation assessment
  • 中文刊名:DXXH
  • 英文刊名:Electric Power Information and Communication Technology
  • 机构:中国电力科学研究院有限公司;国家电网公司东北分部;
  • 出版日期:2019-03-15
  • 出版单位:电力信息与通信技术
  • 年:2019
  • 期:v.17;No.187
  • 基金:国家电网公司总部科技项目资助“数据驱动的大电网全景运行态势智能评估与优化控制关健技术研究”(B442XT170063)
  • 语种:中文;
  • 页:DXXH201903003
  • 页数:8
  • CN:03
  • ISSN:10-1164/TK
  • 分类号:18-25
摘要
为应对大电网日趋复杂的运行环境,全面提升新形势下电网安全稳定智能监控能力,文章深度融合新一代信息、计算与控制技术,设计了基于大数据及人工智能的大电网智能调控系统框架,对实现大电网"即测-即辨-即控"的在线智能调度具有重要的工程价值。首先,结合电网调控系统的发展现状,分析了新一代智能调控系统的功能需求;然后以大电网全局监控的视角,设计其大数据与人工智能顶层功能框架,并对该系统涉及的基本理论及关键技术进行系统概述;最后,以东北电网为背景,搭建了基于大数据技术的智能调控系统环境,实现东北电网稳定态势评估与智能决策。该系统可全面提升大电网在线智能监控能力,为大电网安全稳定运行提供技术支撑。
        To deal with the increasingly complex operation environment of large power grid, improving the intelligent monitoring ability of power grid security and stability under the new situation, this paper deeply integrated the new generation of information, computing and control technology, then designed an intelligent control system framework based on big data and artificial intelligence for large power grid.It is of great engineering value to realize the on-line intelligent dispatching of large power grid. First of all, this paper analyzes the functional requirements of the new generation intelligent control system considering the current situation of power grid regulation and control system. Then, from the perspective of global monitoring of large power grid, we design the top functional framework based big data and artificial intelligence, and summarize the basic theory and key technologies involved in the system.Finally, based on the background of Northeast Power Grid, an intelligent control system environment based on big data technology is built to realize the stability situation assessment and intelligent decisionmaking of Northeast Power Grid. The system can enhance the online intelligent monitoring ability of large power grid and provide technical support for the safe and stable operation of large power grid.
引文
[1]李柏青,刘道伟,秦晓辉,等.信息驱动的大电网全景安全防御概念及理论框架[J].中国电机工程学报,2016, 36(21):5796-5805.LI Baiqing, LIU Daowei, QIN Xiaohui, et al. Concept and theory framework of panoramic security defense for bulk power system driven by information[J]. Proceedings of the CSEE, 2016, 36(21):5796-5805.
    [2]刘道伟,张东霞,孙华东,等.时空大数据环境下的大电网稳定态势量化评估与自适应防控体系构建[J].中国电机工程学报,2015, 35(2):268-276.LIU Daowei, ZHANG Dongxia, SUN Huadong, et al.Construction of stability situation quantitative assessment and adaptive control system for large-scale power grid in the spatiotemporal big data environment[J]. Proceedings of the CSEE,2015, 35(2):268-276.
    [3]王继业,刘道伟,马世英,等.信息驱动的全球能源互联网全景安全防御系统[J].电力信息与通信技术,2016,14(3):13-19.WANG Jiye, LIU Daowei, MA Shiying, et al. Information-driven global energy internet panoramic security defense system[J].Electric Power Information and Communication Technology,2016, 14(3):13-19.
    [4]章锐,刘道伟,陈树勇,等.信息驱动的大电网全景安全防御系统可视化设计[J].电力信息与通信技术,2016, 14(12):46-51.ZHANG Rui, LIU Daowei, CHEN Shuyong, et al. Design of information-driven panoramic security defense system visualization for large-scale power grid[J]. Electric Power Information and Communication Technology, 2016, 14(12):46-51.
    [5]朱方,赵红光,刘增煌,等.大区电网互联对电力系统动态稳定性的影响[J].中国电机工程学报,2007, 27(1):1-7.ZHU Fang, ZHAO Hongguang, LIU Zenghuang, et al. The influence of large power grid interconnected on power system dynamic stability[J]. Proceedings of the CSEE, 2007, 27(1):1-7.
    [6]童晓阳,叶圣永.数据挖掘在电力系统暂态稳定评估中的应用综述[J].电网技术,2009,33(20):88-93.TONG Xiaoyang, YE Shengyong. A survey on application of data mining in transient stability assessment of power system[J].Power System Technology, 2009, 33(20):88-93.
    [7]中国电机工程学会电力信息化专委会.中国电力大数据发展白皮书[R]. 2013.
    [8]闫湖,黄碧斌,刘龙珠.人工智能在新一代电力系统中的应用前景分析[J].电力信息与通信技术,2018, 16(11):7-11.YAN Hu, HUANG Bibin, LIU Longzhu. Artificial intelligenceapplication prospect in the new generation of electric power system[J]. Electric Power Information and Communication Technology, 2018, 16(11):7-11.
    [9]曲朝阳,陈帅,杨帆,等.基于云计算技术的电力大数据预处理属性约简方法[J].电力系统自动化,2014, 38(8):67-71.QU Zhaoyang, CHEN Shuai, YANG Fan, et al. An attribute reducing method for electric power Big Data preprocessing based on cloud computing technology[J]. Automation of Electric Power Systems, 2014, 38(8):67-71.
    [10]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, SHI 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.
    [11]陈亮,王震,王刚.深度学习框架下LSTM网络在短期电力负荷预测中的应用[J].电力信息与通信技术,2017, 15(5):8-11.CHEN Liang, WANG Zhen, WANG Gang. Application of LSTM networks in short-term power load forecasting under the deep learning framework[J]. Electric Power Information and Communication Technology, 2017, 15(5):8-11.
    [12]刘俊勇,沈晓东,田立峰,等.智能电网下可视化技术的展望[J].电力自动化设备,2010, 30(1):7-13.LIU Junyong, SHEN Xiaodong, TIAN Lifeng, et al. Prospects of visualization under smart grid[J]. Electric Power Automation Equipment, 2010, 30(1):7-13.
    [13]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.
    [14]张宇航,邱才明,贺兴,等.一种基于LSTM神经网络的短期用电负荷预测方法[J].电力信息与通信技术,2017, 15(9):19-25.ZHANG Yuhang, QIU Caiming, HE Xing, et al. A short-term load forecasting based on LSTM neural network[J]. Electric Power Information and Communication Technology, 2017, 15(9):19-25.
    [15]黄彦浩.基于数据认知的电网仿真数据智能分析系统架构研究[J].电力信息与通信技术,2017, 15(12):59-64.HUANG Yanhao. Study of power grid simulation data intelligence analysis system framework based on data cognition[J]. Electric Power Information and Communication Technology, 2017, 15(12):59-64.
    [16]郑超,侯俊贤,严剑峰,等.在线动态安全评估与预警系统的功能设计与实现[J].电网技术,2010, 34(3):55-60.ZHENG Chao, HOU Junxian, YAN Jianfeng, et al. Functional design and implementation of online dynamic security assessment and early warning system[J]. Power System Technology, 2010,34(3):55-60.
    [17]刘道伟,马世英,李柏青,等.基于响应的电网暂态稳定态势在线量化评估方法[J].中国电机工程学报,2013, 33(4):85-95.LIU Daowei, MA Shiying, LI Baiqing, et al. Quantitative method for on-line power system transient stability assessment based on response information[J]. Proceedings of the CSEE, 2013, 33(4):85-95.
    [18]朱朝阳,王继业,邓春宇.电力大数据平台研究与设计[J].电力信息与通信技术,2015,13(6):1-7.ZHU Chaoyang, WANG Jiye, DENG Chunyu. Research anddesign of electric power big data platform[J]. Electric Power Information and Communication Technology, 2015, 13(6):1-7.
    [19]刘东,盛万兴,王云,等.电网信息物理系统的关键技术及其进展[J].中国电机工程学报,2015, 35(14):3522-3531.LIU Dong, SHENG Wanxing, WANG Yun, et al. Key technologies and trends of cyber physical system for power grid[J]. Proceedings of the CSEE, 2015, 35(14):3522-3531.
    [20]王中杰,谢璐璐.信息物理融合系统研究综述[J].自动化学报,2011,37(10):1157-1166.WANG Zhongjie, XIE Lulu. Cyber-physical systems:a survey[J].Acta Automatica Sinica, 2011, 37(10):1157-1166.
    [21]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):33-39.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):33-39.

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