Study on smart distribution network operation and management methods in the context of big data

doi:10.3969/j.issn.1674-1951.2021.08.005

Abstract

Abstract:

Smart distribution network has developed new characteristics during its extension,such as a large quantity of distributed power supplies and supports for users demand interaction. Thus,the operation and management of smart distribution network is more complex.Big data technology is booming and provides technical support for improving the operation and management level.The application value of big data technology in smart distribution network is described. Then,four potential application scenarios of big data in smart distribution network,data preprocessing,transmission loss management,state estimation and health evaluation, are systematically analyzed.The network security problems and the corresponding protection measures of smart distribution network in the context of big data are expounded. Combining the problems in practical cases with feasible research areas provides an idea for building a smart distribution network operation management system based on big data technology.

Key words: big data, smart distribution network, operation and management, health evaluation, network security, IoT

CLC Number:

TM72

LI Wei, YAN Chuan, SHENG Qingbo, WANG Jianping, WANG Xiaodong, TAN Jia. Study on smart distribution network operation and management methods in the context of big data[J]. Huadian Technology, 2021, 43(8): 33-40.

Figures/Tables 4

References 39

[1]	刘灏, 毕天姝, 徐全, 等. 配电网高精度同步相量测量技术方案与展望[J]. 电力系统自动化, 2020, 44(18):23-29.
	LIU Hao, BI Tianshu, XU Quan, et al. High precision synchronous phasor measurement technique scheme and prospect of distribution network[J]. Automation of Electric Power Systems, 2020, 44(18):23-29.
[2]	祖国强, 肖峻, 穆云飞, 等. 计及分布式电源与需求响应的智能配电系统安全域[J]. 电力系统自动化, 2020, 44(8):100-107.
	ZU Guoqiang, XIAO Jun, MU Yunfei, et al. Security region for smart distribution system considering distributed generator and demand response[J]. Automation of Electric Power Systems, 2020, 44(8):100-107.
[3]	崔艳妍, 王军, 苏剑, 等. 配电网运营大数据挖掘与分析[J]. 供用电, 2017, 34(6):20-26.
	CUI Yanyan, WANG Jun, SU Jian, et al. Big data mining and analysis of distribution network operation[J]. Distribution & Utilization, 2017, 34(6):20-26.
[4]	赵海波. 电力行业大数据研究综述[J]. 电工电能新技术, 2020, 39(12):62-72.
	ZHAO Haibo. A survey of big data research in power industry[J]. Advanced Technology of Electrical Engineering and Energy, 2020, 39(12):62-72.
[5]	李宁, 薛少华, 马向林, 等. 基于大数据技术的配电网规划[J]. 青岛科技大学学报(自然科学版), 2020, 41(6):80-86.
	LI Ning, XUE Shaohua, MA Xianglin, et al. Distribution network planning method based on big data technologies[J]. Journal of Qingdao University of Science and Technology(Natural Science Edition), 2020, 41(6):80-86.
[6]	方鑫, 殷俊, 蒋苏, 等. 基于等距K-means和Apriori算法的配电网故障规律挖掘方法[J]. 智慧电力, 2020, 48(10):99-104.
	FANG Xin, YIN Jun, JIANG Su, et al. Data mining algorithm for fault rules of distribution network based on combination of isometric K-means and Apriori algorithm[J]. Intelligent Power, 2020, 48(10):99-104.
[7]	郑康霖, 耿增玮, 王红霞, 等. 基于同步量测大数据的配网单相接地故障检测及定位方法[J]. 电网与清洁能源, 2020, 36(9):50-56.
	ZHENG Kanglin, GENG Zengwei, WANG Hongxia, et al. Single-phase grounding fault detection and localization based on big data from synchronized phasor measurement[J]. Power System and Clean Energy, 2020, 36(9):50-56.
[8]	李正光, 钱锋强, 刘艾旺, 等. 基于多维特征量融合的配电网拓扑异常溯源与应用模型研究[J]. 浙江电力, 2020, 39(7):71-79.
	LI Zhengguang, Qian Fengqiang, LIU Aiwang, et al. An anomaly tracing and application model of distribution network topology based on multi-dimensional feature fusion[J]. Zhejiang Electric Power, 2020, 39(7):71-79.
[9]	张稳, 盛万兴, 杜松怀, 等. 基于海量数据的配电网运行分析系统架构与技术实现[J]. 电力系统自动化, 2020, 44(3):147-153.
	ZHANG Wen, SHENG Wanxing, DU Songhuai, et al. Architecture and technology implementation of massive data based distribution network operation analysis system[J]. Automation of Electric Power Systems, 2020, 44(3):147-153.
[10]	徐飞, 孙明柱, 凌松, 等. 基于电力大数据的配电网同期线损管理[J]. 安徽电力, 2019, 36(1):43-46.
	XU Fei, SUN Mingzhu, LING Song, et al. Synchronous line loss management of distribution network based on power big data[J]. Anhui Electric Power, 2019, 36(1):43-46.
[11]	句荣滨, 王巍, 孙畅岑, 等. 基于电力调度大数据的状态估计智能分析技术[J]. 东北电力技术, 2020, 41(3):1-4.
	JU Rongbin, WANG Wei, SUN Changcen, et al. Smart analysis technology of state estimation based on electric power big data[J]. Northeast Electric Power Technology, 2020, 41(3):1-4.
[12]	赵会群, 吴凯锋. 一种大数据估价算法[J]. 计算机科学, 2020, 47(9):110-116.
	ZHAO Huiqun, WU Kaifeng. Big data valuation algorithm[J]. Computer Science, 2020, 47(9):110-116.
[13]	徐艳萍. 试谈大数据技术在医疗信息化中的应用[J]. 信息记录材料, 2020, 21(12):167-168.
	XU Yanping. The application of big data technique in medical informatization[J]. Information Recording Material, 2020, 21(12):167-168.
[14]	孙自强, 于龙. 公安大数据时代金融犯罪预警防控研究[J]. 中国防伪报道, 2020(12):82-89.
	SUN Ziqiang, YU Long. Research on early warning and prevention of financial crimes in the era of big data of public security Security[J]. China Anti-Counterfeiting Report, 2020(12):82-89.
[15]	陆泽辉. 移动电子商务环境下基于数据分析的商品推荐算法[D]. 北京:北京邮电大学, 2017.
[16]	汪峰. 泛在电力物联网与大数据应用[J]. 中国电力企业管理, 2019(28):58-59.
	WANG Feng. Ubiquitous power internet of things and big data application[J]. China Power Enterprise Management, 2019(28):58-59.
[17]	朱有产, 梁玮轩, 王英姿. 基于时间序列的配电网数据清理和融合方法研究[J/OL]. 电网技术:1-8[2021-05-26].https://doi.org/10.13335/j.1000-3673.pst.2020.0886 .
	ZHU Youchan, LIANG Weixuan, WANG Yingzi. Research on data cleaning and fusion in distribution power grid based on time series technology[J/OL]. Power System Technology:1-8[2021-05-26].https://doi.org/10.13335/j.1000-3673.pst.2020.0886 .
[18]	雷峰津, 房俊. 基于相关分析的电网电能质量缺失数据填充方法[J]. 北方工业大学学报, 2020, 32(5):77-82.
	LEI Fengjin, FANG Jun. Imputation method for the missing data of power quality in power grid based on correlation analysis[J]. Journal of North China University of Industry, 2020, 32(5):77-82.
[19]	王守相, 陈海文, 潘志新, 等. 采用改进生成式对抗网络的电力系统量测缺失数据重建方法[J]. 中国电机工程学报, 2019, 39(1):56-64.
	WANG Shouxiang, CHEN Haiwen, PAN Zhixin, et al. A reconstruction method for missing data in power system measurement using an improved GAN[J]. Proceedings of the CSEE, 2019, 39(1):56-64.
[20]	刘云鹏, 许自强, 和家慧, 等. 基于条件式Wasserstein生成对抗网络的电力变压器故障样本增强技术[J]. 电网技术, 2020, 44(4):1505-1513.
	LIU Yunpeng, XU Ziqiang, HE Jiahui, et al. Data augmentation method for power transformer fault diagnosis based on conditional Wasserstein generative adversarial network[J]. Power System Technology, 2020, 44(4):1505-1513.
[21]	孙峰, 钱啸, 吕勤, 等. 多系统融合的线损大数据挖掘与应用[J]. 浙江电力, 2019, 38(1):111-116.
	SUN Feng, QIAN Xiao, LYU Qin, et al. Mining and application of line loss big data with multisystem fusion[J]. Zhejiang Electric Power, 2019, 38(1):111-116.
[22]	杨志淳, 沈煜, 杨帆, 等. 基于数据关联分析的低压配电网拓扑识别方法[J]. 电测与仪表, 2020, 57(18):5-11.
	YANG Zhichun, SHEN Yu, YANG Fan, et al. Topology identification method of low voltage distribution network based on data association analysis[J]. Electrical Measurement & Instrumentation, 2020, 57(18):5-11.
[23]	李彦君, 裴玮, 肖浩, 等. 基于深度学习的微网需求响应特性封装与配电网优化运行[J]. 电力系统自动化, 2021, 45(10):157-165.
	LI Yanjun, PEI Wei, XIAO Hao, et al. Deep learning based micro grid demand response feature encapsulation and distribution network optimization operation[J]. Automation of Electric Power Systems, 2021, 45(10):157-165.
[24]	刘喆林, 王成山, 李鹏, 等. 多源量测数据融合的配电网状态估计及应用[J]. 中国电机工程学报, 2021, 41(8):2605-2615.
	LIU Zhelin, WANG Chengshan, LI Peng, et al. State estimation of distribution networks based on multi-source measurement data and its applications[J]. Proceedings of the CSEE, 2021, 41(8):2605-2615.
[25]	高正男. 多信息源数据融合的电网运行状态估计[D]. 大连:大连理工大学, 2019.
[26]	王雅琪, 陈堂贤, 王俊, 等. 含分布式电源的配电网健康评价研究[J]. 电力科学与工程, 2019, 35(8):36-41.
	WANG Yaqi, CHEN Tangxian, WANG Jun, et al. Research on health evaluation of distribution network with distributed generation[J]. Electric Power Science and Engineering, 2019, 35(8):36-41.
[27]	周炳华, 王洋, 李峰, 等. 城市能源互联网视角下的主动配电网规划设计与策略研究[J]. 华电技术, 2021, 43(1):59-65.
	ZHOU Binghua, WANG Yang, LI Feng, et al. Research on active distribution network planning and design strategy from the perspective of urban energy internet[J]. Huadian Technology, 2021, 43(1):59-65.
[28]	胡丽娟, 刁赢龙, 刘科研, 等. 基于大数据技术的配电网运行可靠性分析[J]. 电网技术, 2017, 41(1):265-271.
	HU Lijuan, DIAO Yinglong, LIU Keyan, et al. Operation reliability analysis of distribution network based on big data technology[J]. Power System Technology, 2017, 41(1):265-271.
[29]	姚海燕, 吴金荣, 徐辉, 等. 语义物联视角下的电力设计公司数据管理研究[J]. 华电技术, 2021, 43(1):38-44.
	YAO Haiyan, WU Jinrong, XU Hui, et al. Research on data management of power design companies from the perspective of semantic interconnection[J]. Huadian Technology, 2021, 43(1):38-44.
[30]	马钊, 周莉梅, 袁海文, 等. 现代配电网健康指数理论及其应用研究[J]. 中国电机工程学报, 2019, 39(1):130-140.
	MA Zhao, ZHOU Limei, YUAN Haiwen, et al. Health index methodology of modern distribution network and its application[J]. Proceedings of the CSEE, 2019, 39(1):130-140.
[31]	魏立斐, 陈聪聪, 张蕾, 等. 机器学习的安全问题及隐私保护[J]. 计算机研究与发展, 2020, 57(10):2066-2085.
	WEI Lifei, CHEN Congcong, ZHANG Lei, et al. Security issue and privacy preserving in machine learning[J]. Computer Research and Development, 2020, 57(10):2066-2085.
[32]	陈锐. 基于大数据的计算机网络安全研究[J]. 黑龙江科学, 2020, 11(24):124-125.
	CHEN Rui. Research of computer network security based on big data[J]. Heilongjiang Science, 2020, 11(24):124-125.
[33]	张徐亮, 万里冰, 钱伟中, 等. 基于区块链的电力大数据安全保障体系[J]. 华电技术, 2020, 42(8):68-74.
	ZHANG Xuliang, WAN Libing, QIAN Weizhong, et al. Security assurance system for electric power big data based on blockchain technology[J]. Huadian Technology, 2020, 42(8):68-74.
[34]	任奎, 孟泉润, 闫守琨, 等. 人工智能模型数据泄露的攻击与防御研究综述[J]. 网络与信息安全学报, 2021, 7(1):1-10.
	REN Kui, MENG Quanrun, YAN Shoukun, et al. Survey of artificial intelligence data security and privacy protection[J]. Journal of Network and Information Security, 2021, 7(1):1-10.
[35]	鄢晶, 高天露, 张俊, 等. 边云链协同技术在能源互联网数据管理中的应用及展望[J]. 华电技术, 2020, 42(8):41-47.
	YAN Jing, GAO Tianlu, ZHANG Jun, et al. Application and prospect of edge-cloud-chain collaboration technologies for energy internet data management[J]. Huadian Technology, 2020, 42(8):41-47.
[36]	李盼. 针对机器学习算法的投毒及其防御技术研究[D]. 长沙:国防科技大学, 2018.
[37]	何伟超. 基于同态加密隐私保护的机器学习关键技术研究[D]. 成都:电子科技大学, 2019.
[38]	李启飞. 面向隐私保护的多方联合学习方法研究[D]. 哈尔滨:哈尔滨工业大学, 2020.
[39]	谢荣, 温蜜. 基于差分隐私的敏感数据挖掘技术研究[J]. 上海电力大学学报, 2020, 36(4):401-407.
	XIE Rong, WEN Mi. Research on sensitive data mining technology based on differential privacy[J]. Journal of Shanghai Power University, 2020, 36(4):401-407.