电力设备运行状态大数据标签体系与关键技术

doi:10.11930/j.issn.1004-9649.202105034

中国电力 ›› 2022, Vol. 55 ›› Issue (1): 126-132.DOI: 10.11930/j.issn.1004-9649.202105034

• 人工智能在新型电力系统中的应用 • 上一篇下一篇

电力设备运行状态大数据标签体系与关键技术

刘文君¹, 董明¹, 徐元孚², 韩强², 王鑫², 许雷², 杜明³

1. 西安交通大学电力设备电气绝缘国家重点实验室, 陕西西安 710049;
2. 国网天津市电力公司电力科学研究院, 天津 300384;
3. 国网天津市电力公司, 天津 300143

收稿日期:2021-05-08 修回日期:2021-09-14 出版日期:2022-01-28 发布日期:2022-01-20
作者简介:刘文君(1997-),女,硕士研究生,从事电力设备状态监测与电力大数据分析研究,E-mail:490188310@qq.com;徐元孚(1964-),男,高级工程师,从事变电站监控运行分析及设备检修决策技术研究,E-mail:yuanfu.xu@tj.sgcc.com.cn
基金资助:
国家电网有限公司科技项目（GTJDK00 DWJS2000309）。

Structure and Key Technologies of Big Data Labeling System for Power Equipment Operation Status

LIU Wenjun¹, DONG Ming¹, XU Yuanfu², HAN Qiang², WANG Xin², XU Lei², DU Ming³

1. State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an 710049, China;
2. State Grid Tianjin Electric Power Research Institute, Tianjin 300384, China;
3. State Grid Tianjin Electric Power Company, Tianjin 300143, China

Received:2021-05-08 Revised:2021-09-14 Online:2022-01-28 Published:2022-01-20
Supported by:
This work is supported by Science and Technology Project of SGCC (NO.SGTJDK00 DWJS2000309)

摘要/Abstract

摘要： 随着大数据分析技术在电网中的快速发展与深度应用，数据标签技术提供了一种新的数据整合思路。电力设备大数据标签以灵活的方式从海量、离散的数据中实现对有用数据的快速识别和提取，在帮助调控人员实现对电力设备情况作出多维判断的同时，为后续电力数据挖掘建模提供了依据。围绕电力设备基础信息、运行信息和状态信息3个维度，提出了一种多维度电力设备标签体系的构建方法，并通过聚类、故障概率计算、模糊推理3个层次丰富了数据标签的内涵，为实现电网监控智能化奠定基础。

关键词: 大数据分析, 电力设备, 运行状态, 数据标签

Abstract: With the rapid development and deep application of big data analysis technology in power grid, data label technology provides people with a new idea of data integration. Big data labels for power equipment can realize rapid identification and extraction of useful data from massive and discrete data in a flexible way, which not only contributes to make multidimensional judgments on power equipment for power grid operators, but also provides a basis for subsequent power data mining modeling. A multi-dimensional data labeling system for power equipment is proposed, which lays a foundation for realizing intelligent monitoring of power grid.

Key words: big data analysis, power equipment, running state, data label

刘文君, 董明, 徐元孚, 韩强, 王鑫, 许雷, 杜明. 电力设备运行状态大数据标签体系与关键技术[J]. 中国电力, 2022, 55(1): 126-132.

LIU Wenjun, DONG Ming, XU Yuanfu, HAN Qiang, WANG Xin, XU Lei, DU Ming. Structure and Key Technologies of Big Data Labeling System for Power Equipment Operation Status[J]. Electric Power, 2022, 55(1): 126-132.

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参考文献

[1] 季学纯, 徐春雷, 杨志宏, 等. 电网调控模型中心体系架构与关键技术[J]. 电力系统自动化, 2018, 42(16): 127–132,154
JI Xuechun, XU Chunlei, YANG Zhihong, et al. Structure and key technologies of power grid dispatching and control model center[J]. Automation of Electric Power Systems, 2018, 42(16): 127–132,154
[2] 李鹏, 毕建刚, 于浩, 等. 变电设备智能传感与状态感知技术及应用[J]. 高电压技术, 2020, 46(9): 3097–3113
LI Peng, BI Jiangang, YU Hao, et al. Technology and application of intelligent sensing and state sensing for transformation equipment[J]. High Voltage Engineering, 2020, 46(9): 3097–3113
[3] 范士雄, 李立新, 王松岩, 等. 人工智能技术在电网调控中的应用研究[J]. 电网技术, 2020, 44(2): 401–411
FAN Shixiong, LI Lixin, WANG Songyan, et al. Application analysis and exploration of artificial intelligence technology in power grid dispatch and control[J]. Power System Technology, 2020, 44(2): 401–411
[4] 程乐峰, 余涛, 张孝顺, 等. 机器学习在能源与电力系统领域的应用和展望[J]. 电力系统自动化, 2019, 43(1): 15–31
CHENG Lefeng, YU Tao, ZHANG Xiaoshun, et al. Machine learning for energy and electric power systems: state of the art and prospects[J]. Automation of Electric Power Systems, 2019, 43(1): 15–31
[5] 吕辉, 许道强, 仲春林, 等. 基于电力大数据的标签画像技术与应用研究[J]. 电力信息与通信技术, 2017, 15(2): 43–48
LV Hui, XU Daoqiang, ZHONG Chunlin, et al. Study on tag portrait technology based on electric power big data and its application[J]. Electric Power Information and Communication Technology, 2017, 15(2): 43–48
[6] 严英杰. 基于大数据分析技术的输变电设备状态评估方法研究[D]. 上海: 上海交通大学, 2018
YAN Yingjie. Research on condition evaluation of power equipment based on big data analysis technology[D]. Shanghai: Shanghai Jiao Tong University, 2018.
[7] 李阿勇, 税雪, 宋志伟. 数据标签研究与应用[J]. 电力大数据, 2020, 23(6): 69–74
LI Ayong, SHUI Xue, SONG Zhiwei. Research and application of data tag[J]. Power Systems and Big Data, 2020, 23(6): 69–74
[8] 赵锡艺, 曾茜. 电力大数据在大客户信用评级服务中的应用研究[J]. 电力大数据, 2017, 20(10): 65–68
ZHAO Xiyi, ZENG Qian. The application exploration of big power data in key account credit rating service[J]. Power Systems and Big Data, 2017, 20(10): 65–68
[9] 关守姝, 孟岩, 韩伟, 等. 基于电力大数据的用户用电感知研究[J]. 电力大数据, 2019, 22(10): 58–64
GUAN Shoushu, MENG Yan, HAN Wei, et al. Research on user electricity consumption perception based on power big data[J]. Power Systems and Big Data, 2019, 22(10): 58–64
[10] 叶康, 冷喜武, 肖飞, 等. 基于大数据标签技术的电网监控智能分析方法[J]. 电测与仪表, 2019, 56(4): 75–79
YE Kang, LENG Xiwu, XIAO Fei, et al. Intelligent analysis method of power grid monitoring based on big data label technology[J]. Electrical Measurement & Instrumentation, 2019, 56(4): 75–79
[11] 许金玲, 孟庆福, 刘婧. 基于大数据的网络公开课标签库建设[J]. 现代电子技术, 2016, 39(3): 101–103,108
XU Jinling, MENG Qingfu, LIU Jing. Construction of network public class label database based on big data[J]. Modern Electronics Technique, 2016, 39(3): 101–103,108
[12] 王青松, 姜富山, 李菲. 大数据环境下基于关联规则的多标签学习算法[J]. 计算机科学, 2020, 47(5): 90–95
WANG Qingsong, JIANG Fushan, LI Fei. Multi-label learning algorithm based on association rules in big data environment[J]. Computer Science, 2020, 47(5): 90–95
[13] 周祺. 基于统计与词典相结合的中文分词的研究与实现[D]. 哈尔滨: 哈尔滨工业大学, 2015
ZHOU Qi. Research and implementation of Chinese word segmentation based on the combination of statistics and dictionary[D]. Harbin: Harbin Institute of Technology, 2015.
[14] 杨超, 张霖. 基于多维度特征参量的输变电设备故障概率计算[J]. 电气开关, 2020, 58(5): 32–38
YANG Chao, ZHANG Lin. Fault probability calculation of power transmission and transformation equipments based on multi-dimensionality-characteristic parameters[J]. Electric Switchgear, 2020, 58(5): 32–38
[15] 白一鸣. 基于数据挖掘技术的模糊推理系统设计[D]. 大连: 大连海事大学, 2013
BAI Yiming. Designing fuzzy inference system based on data mining technology[D]. Dalian Dalian Maritime University, 2013.
[16] 肖建, 白裔峰, 于龙. 模糊系统结构辨识综述[J]. 西南交通大学学报, 2006, 41(2): 135–142
XIAO Jian, BAI Yifeng, YU Long. Overview of fuzzy system structure identification[J]. Journal of Southwest Jiaotong University, 2006, 41(2): 135–142
[17] 李泽文, 邓拓夫, 曾祥君, 等. 智能电网能量流的时空多尺度大数据探讨[J]. 电力科学与技术学报, 2015, 30(1): 22–27
LI Zewen, DENG Tuofu, ZENG Xiangjun, et al. Study on big data with time-space multi-scale for smart grid power flow[J]. Journal of Electric Power Science and Technology, 2015, 30(1): 22–27
[18] 吴瞻宇, 董明, 王健一, 等. 基于模糊关联规则挖掘的电力变压器故障诊断方法[J]. 高压电器, 2019, 55(8): 157–163
WU Zhanyu, DONG Ming, WANG Jianyi, et al. Fault diagnosis of power transformer based on fuzzy association rules mining[J]. High Voltage Apparatus, 2019, 55(8): 157–163
[19] RAUCH J. Expert deduction rules in data mining with association rules: a case study[J]. Knowledge and Information Systems, 2019, 59(1): 167–195.
[20] 王德文, 杨力平. 智能电网大数据流式处理方法与状态监测异常检测[J]. 电力系统自动化, 2016, 40(14): 122–128
WANG Dewen, YANG Liping. Stream processing method and condition monitoring anomaly detection for big data in smart grid[J]. Automation of Electric Power Systems, 2016, 40(14): 122–128
[21] 李富鹏, 沈秋英, 王森, 等. 基于大数据和多因素组合分析的单元制配电网精细化负荷预测[J]. 智慧电力, 2020, 48(1): 55–62
LI Fupeng, SHEN Qiuying, WANG Sen, et al. Refined load forecasting method for unit distribution network based on big data and multiple factors[J]. Smart Power, 2020, 48(1): 55–62
[22] 王赛一, 余建平, 孙丰杰, 等. 电力大数据的价值密度评价及结合改进k-means的提升方法研究[J]. 智慧电力, 2019, 47(3): 8–15
WANG Saiyi, YU Jianping, SUN Fengjie, et al. Evaluation and promotion methods with improved k-means for value density of electric power big data[J]. Smart Power, 2019, 47(3): 8–15
[23] 郑倩, 李刚, 盛妍, 等. 基于社会感知的电力服务渠道网点推荐系统[J]. 智慧电力, 2018, 46(10): 49–54
ZHENG Qian, LI Gang, SHENG Yan, et al. Electric power service network recommendation system based on social perception computation[J]. Smart Power, 2018, 46(10): 49–54
[24] 李清. 基于改进PSO-PFCM聚类算法的电力大数据异常检测方法[J]. 电力系统保护与控制, 2021, 49(18): 161–166
LI Qing. Power big data anomaly detection method based on an improved PSO-PFCM clustering algorithm[J]. Power System Protection and Control, 2021, 49(18): 161–166
[25] 方静, 彭小圣, 刘泰蔚, 等. 电力设备状态监测大数据发展综述[J]. 电力系统保护与控制, 2020, 48(23): 176–186
FANG Jing, PENG Xiaosheng, LIU Taiwei, et al. Development trend and application prospects of big data-based condition monitoring of power apparatus[J]. Power System Protection and Control, 2020, 48(23): 176–186

电力设备运行状态大数据标签体系与关键技术

Structure and Key Technologies of Big Data Labeling System for Power Equipment Operation Status

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[8]	刘文杰, 吴峰, 金宇清, 鞠平. 双馈风力发电机组的分群方法研究[J]. 中国电力, 2014, 47(6): 111-116.
[9]	余萍,董保国. 基于SIFT特征匹配的电力设备图像变化参数识别[J]. 中国电力, 2012, 45(11): 60-64.

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Structure and Key Technologies of Big Data Labeling System for Power Equipment Operation Status

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