极端灾害下基于时空网格的配电网多源数据融合方法

doi:10.11930/j.issn.1004-9649.201812115

中国电力 ›› 2019, Vol. 52 ›› Issue (11): 77-84.DOI: 10.11930/j.issn.1004-9649.201812115

极端灾害下基于时空网格的配电网多源数据融合方法

陈彬¹, 倪明², 周霞³, 郁琛², 吴涵¹, 杨洲³, 林沪生³

1. 国网福建省电力公司电力科学研究院, 福建福州 350007;
2. 南瑞集团(国网电力科学研究院)有限公司, 江苏南京 211106;
3. 南京邮电大学先进技术研究院, 江苏南京 210023

收稿日期:2019-01-02 修回日期:2019-06-28 出版日期:2019-11-05 发布日期:2019-11-05
作者简介:陈彬(1982-),男,博士研究生,高级工程师,从事电力系统分析与控制研究,E-mail:cb_fz@163.com;倪明(1969-),男,博士,高级工程师(研究员级),从事电力系统规划、电力信息物理系统研究,E-mail:ni-ming@sgepri.sgcc.com.cn;周霞(1978-),女,博士,高级工程师,从事电力系统分析与控制研究,E-mail:zhouxia@njupt.edu.cn
基金资助:
国家电网有限公司总部科技项目（提升极端灾害下配电网动态恢复和现场指挥关键技术研究，52130418000L）

Multi-source Data Fusion Method for Distribution Network Based on Spatiotemporal Grid under Extreme Disasters

CHEN Bin¹, NI Ming², ZHOU Xia³, YU Chen², WU Han¹, YANG Zhou³, LIN Husheng³

1. Electric Power Research Institute of State Grid Fujian Electric Power Corporation, Fuzhou 350007, China;
2. NARI Group Corporation (State Grid Electric Power Research Institute), Nanjing 211106, China;
3. Institute of Advanced Technology, Nanjing University of Post and Telecommunications, Nanjing 210023, China

Received:2019-01-02 Revised:2019-06-28 Online:2019-11-05 Published:2019-11-05
Supported by:
This work is supported by Science and Technology Project of SGCC(Research on Key Technologies for Dynamic Recovery and Field Command of Distribution Network under Extreme Disasters, No.52130418000L)

摘要/Abstract

摘要： 极端灾害会对配电网造成一定的影响，严重时甚至破坏电网的安全稳定运行，灾害过程中会产生大量的配电网设备及运行方面的数据，这些数据信息量大且波动性强。为及时掌握极端灾害情况下对配电网设备的影响或破坏等信息，有效地协助配电网灾后指挥及修复工作，提出了一种基于时空地理网格的配电网多源数据融合方法。首先根据时空网格划分方法对配电网所在区域进行时间、空间上的划分并建立配电网时空网格编码数据库；然后对极端灾害下配电网的多源数据进行特征分析，提取关键时间特征和空间特征；通过计算时空特征关联度，关联匹配时空网格和配电网多源数据并储存于时空网格编码数据库中；进而根据融合后的配电网多源数据构建配电网灾情网格化视图；最后，通过算例验证了该方法的有效性和合理性。

关键词: 极端灾害, 配电网, 多源数据, 时空网格, 数据融合

Abstract: Extreme disasters will have a certain impact on the distribution network, and even disrupt the safe and stable operation of the power grid. A large number of data about distribution network equipment and operation are generated during the disaster events, which are large in information contents and high in volatility. In order to timely grasp the damage information of distribution network equipment under extreme disaster events and effectively assist the post-disaster repairing work of the distribution network, a spatiotemporal grid based distribution network multi-source data fusion method is proposed. Firstly, according to the spatiotemporal grid division method, the area where the distribution network is located is partitioned in time and space, and a spatiotemporal grid coding database of the distribution network is established. Then, the characteristic analysis of the multi-source data of the distribution network under extreme disaster events is carried out to extract key time features and spatial features. By calculating the correlation degree of spatiotemporal features, the spatiotemporal grid and the distribution network multi-source data are associated and stored in the spatiotemporal grid coding database. Further, the grid view of the distribution network damage is constructed according to the fused multi-source data of the distribution network. The results of the case study show that the proposed method is effective and reasonable.

Key words: extreme disasters, distribution network, multi-source data, spatiotemporal grid, data fusion

中图分类号:

TM711
TM769

陈彬, 倪明, 周霞, 郁琛, 吴涵, 杨洲, 林沪生. 极端灾害下基于时空网格的配电网多源数据融合方法[J]. 中国电力, 2019, 52(11): 77-84.

CHEN Bin, NI Ming, ZHOU Xia, YU Chen, WU Han, YANG Zhou, LIN Husheng. Multi-source Data Fusion Method for Distribution Network Based on Spatiotemporal Grid under Extreme Disasters[J]. Electric Power, 2019, 52(11): 77-84.

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https://www.electricpower.com.cn/CN/Y2019/V52/I11/77

参考文献

[1] Executive Office of the President. Benefits of increasing electric grid resilience to weather outages[R]. Washington DC, USA:the White House, 2013.
[2] TOND T, WANG W P. A more resilient grid[J]. IEEE Power and Energy Magazine, 2015, 13(3):26-34.
[3] Electric Power Research Institute (EPRI). Enhancing distribution resiliency-opportunities for applying innovative technologies[R]. Palo Alto, California:Electric Power Research Institute (EPRI), 2013.
[4] 张恒旭, 刘玉田. 极端冰雪灾害对电力系统运行影响的综合评估[J]. 中国电机工程学报, 2011, 31(10):52-58 ZHANG Hengxu, LIU Yutian. Comprehensive assessment of extreme ice disaster affecting power system operation[J]. Proceedings of the CSEE, 2011, 31(10):52-58
[5] 罗剑波, 郁琛, 谢云云, 等. 关于自然灾害下电力系统安全稳定防御方法的评述[J]. 电力系统保护与控制, 2018, 46(6):158-170 LUO Jianbo, YU Chen, XIE Yunyun, et al. A review on risk assessment of power grid security and stability under natural disasters[J]. Power System Protection and Control, 2018, 46(6):158-170
[6] LIU Y, SINGH C. A methodology for evaluation of hurricane impact on composite power system reliability[J]. IEEE Transactions on Power Systems, 2011, 26(1):145-152.
[7] 陈彬, 于继来. 强台风环境下配电线路故障概率评估方法[J]. 中国电力, 2019, 52(5):89-95 CHEN Bin, YU Jilai. Outage probability evaluation of the distribution corridors under strong typhoon environment[J]. Electric Power, 2019, 52(5):89-95
[8] ESKANDARPOUR R, KHODAEI A. Machine learning based power grid outage prediction in response to extreme events[J]. IEEE Transactions on Power Systems, 2017, 32(4):3315-3316.
[9] 薛禹胜, 吴勇军, 谢云云, 等. 停电防御框架向自然灾害预警的拓展[J]. 电力系统自动化, 2013, 37(16):18-26 XUE Yusheng, WU Yongjun, XIE Yunyun, et al. Extension of blackout defense scheme to natural disasters early-warning[J]. Automation of Electric Power Systems, 2013, 37(16):18-26
[10] 谢云云, 薛禹胜, 王昊昊, 等. 电网雷击故障概率的时空在线预警[J]. 电力系统自动化, 2013, 37(17):44-51 XIE Yunyun, XUE Yusheng, WANG Haohao, et al. Space-time early-warning of power grid fault probability by lightning[J]. Automation of Electric Power Systems, 2013, 37(17):44-51
[11] 齐郑, 张首魁, 李志, 等. 考虑时间尺度的含DG配电网故障动态恢复策略[J]. 电力系统保护与控制, 2017, 45(16):31-38 QI Zheng, ZHANG Shoukui, LI Zhi, et al. Dynamic service restoration strategy considering time scale for distribution network with DGs[J]. Power System Protection and Control, 2017, 45(16):31-38
[12] 张瑶瑶. 智能配电网的灾害评估及应灾恢复方法研究[D]. 沈阳:东北大学, 2014. ZHANG Yaoyao. Research on disaster assessment and emergency recovery method of smart distribution network[D]. Shenyang:Northeastern University, 2014.
[13] 沈鑫, 曹敏, 薛武, 等. 基于多源异构数据聚合技术的电力物联网设备全景信息构架模型研究与应用[J]. 南方电网技术, 2016, 10(2):48-55 SHEN Xin, CAO Min, XUE Wu, et al. Research and application for panoramic information architecture model of power internet of things devices based on multi-source heterogeneous data aggregation technologies[J]. Southern Power System Technology, 2016, 10(2):48-55
[14] 冯颖, 谭勇桂, 何安宏. 基于实时数据库的电网信息综合监测分析平台[J]. 电气应用, 2015, 34(增刊2):623−628.
[15] 程承旗, 任伏虎, 濮国梁. 空间信息剖分组织导论[M]. 北京:科学出版社, 2012:7−11.
[16] 廖永丰, 李博, 吕雪锋, 等. 基于GeoSOT编码的多元灾害数据一体化组织管理方法研究[J]. 地理与地理信息科学, 2013, 29(5):36-40 LIAO Yongfeng, LI Bo, LV Xuefeng, et al. Method of multi-type disaster data organization and management based on GeoSOT[J]. Geography and Geo-Information Science, 2013, 29(5):36-40
[17] 胡璐锦, 蔡俊, 李海生. 基于时空地理格网的空间数据融合方法[J]. 测绘与空间地理信息, 2018, 41(8):4-7 HU Lujin, CAI Jun, LI Haisheng. Spatial data fusion based on spatiotemporal geographic grid[J]. Geomatics & Spatial Information Technology, 2018, 41(8):4-7
[18] 仇林遥. 面向自然灾害应急任务的时空数据智能聚合方法[D].武汉:武汉大学, 2017. QIU Linyao. A smart aggregation method of spatial-temopral data for natural disaster emergency tasks[D]. Wuhan:Wuhan University, 2017.
[19] PHADKE A C, MARTINO C D, CHEUNG K F, et al. Modeling of tropical cyclone winds and waves for emergency management[J]. Ocean Engineering, 2003, 30(4):553-578.
[20] 江志辉, 华锋, 曲平. 一个新的热带气旋参数调整方案[J]. 海洋科学进展, 2008, 26(1):1-7 JIANG Zhihui, HUA Feng, QU Ping. A new scheme for adjusting the tropical cyclone parameters[J]. Advances in Marine Science, 2008, 26(1):1-7

极端灾害下基于时空网格的配电网多源数据融合方法

Multi-source Data Fusion Method for Distribution Network Based on Spatiotemporal Grid under Extreme Disasters

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极端灾害下基于时空网格的配电网多源数据融合方法

Multi-source Data Fusion Method for Distribution Network Based on Spatiotemporal Grid under Extreme Disasters

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