[1]彭红霞,文 艳,王 磊,等.基于两层知识架构的电力设备差异化运维技术[J].高压电器,2019,55(07):221-226.[doi:10.13296/j.1001-1609.hva.2019.07.032 ]
 PENG Hongxia,WEN Yan,WANG Lei,et al.Differential Operating Maintenance Technology of Power Equipment Based on Two-layer Knowledge Architecture[J].High Voltage Apparatus,2019,55(07):221-226.[doi:10.13296/j.1001-1609.hva.2019.07.032 ]
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基于两层知识架构的电力设备差异化运维技术()
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《高压电器》[ISSN:1001-1609/CN:61-11271/TM]

卷:
第55卷
期数:
2019年07期
页码:
221-226
栏目:
技术讨论
出版日期:
2019-07-15

文章信息/Info

Title:
Differential Operating Maintenance Technology of Power Equipment Based on Two-layer Knowledge Architecture
作者:
彭红霞1 文 艳2 王 磊3 闫 冬1 王智杰1 徐 珂1 李亚锦4 于大洋4
(1. 国网山东省电力公司菏泽供电公司, 山东 菏泽 274000; 2. 国网山东省电力公司, 济南 250001; 3. 国网山东省 电力公司寿光供电公司, 山东 寿光 262700; 4. 山东大学电气工程学院, 济南 250061)
Author(s):
PENG Hongxia1 WEN Yan2 WANG Lei3 YAN Dong1 WANG Zhijie1 XU Ke1 LI Yajin4 YU Dayang4
(1. Heze Power Supply Company of State Grid Shandong Electric Power Company, Shandong Heze 274000, China; 2. State Grid Shandong Electric Power Company, Jinan 250001, China; 3. Shouguang Power Supply Company of State Grid Shandong Electric Power Company, Shandong Shouguang 262700, China; 4. Shandong University, Jinan 250061, China)
关键词:
两层知识架构 相关关系 分块统计 知识生成 差异化运维
Keywords:
two-layer knowledge architecture distance correlation block statistics knowledge generated differential operating maintenance
DOI:
10.13296/j.1001-1609.hva.2019.07.032
摘要:
针对电力设备运维数据质量不高、信息隔离以及数据扭曲等信息问题,提出两层知识库架构,以不同类型缺陷率和因素之间相关系数、不同特征组合下的设备缺陷率作为知识表示,采用模型统一、分块统计和动态排序算法,构建运维知识生成数学模型,通过运维样本的不断积累,更新运维知识库中缺陷率和因素之间相关性和不同特征组合下的设备缺陷率,根据排序结果形成重点运维清单,提高现场运维缺陷识别的准确性。以电力公司运维缺陷数据为基础,分析和验证了基于两层架构的知识生成方法的有效性和正确性,可为设备运维决策提供理论依据。
Abstract:
Aiming at the problems of low quality, information isolation and distortion of power equipment operation and maintenance data, a two-layer knowledge base architecture is proposed. A mathematical model for the knowledge generation of operation and maintenance and defect rate of equipment under different characteristic combinations are constructed by using model unification, block statistics and dynamic sorting algorithm, with the correlation coefficient between defect rate and factors as the knowledge representation. Through the continuous accumulation of operation and maintenance samples, defect rate of equipment under different characteristic combinations and the correlation between the defect rate and factors in the operation and maintenance knowledge base is updated, so as to improve the accuracy of defect diagnosis. Based on the defect data of power company operation and maintenance, the validity of the knowledge generation method based on two-layer architecture are analyzed and verified, which can provide theoretical basis for equipment operation and maintenance decision.

参考文献/References:

[1] 王思齐. 基于物联网的智能电网监控系统研究[J]. 电源技术, 2018,42(1):125-127. WANG Siqi. Research on smart grid monitoring system based on internet of things[J]. Chinese Journal of Power Sources, 2018,42(1):125-127.
[2] 吕 军,栾文鹏,刘日亮,等.基于全面感知和软件定义的配电物联网体系架构[J]. 电网技术,2018,42(10):3108-3115. LYU Jun, LUAN Wenpeng, LIU Riliang, et al. Architecture of distribution internet of things based on widespread sensing & software defined technology[J]. Power System Technology, 2018,42(10):3108-3115.
[3] 戴 彦, 王刘旺, 李 媛,等. 新一代人工智能在智能电网中的应用研究综述[J]. 电力建设, 2018, 39(10): 1-11. DAI Yan, WANG Liuwang, LI Yuan, et al. A brief survey on applications of new generation artificial intelligence in smart grids[J]. Electric Power Construction,2018,39(10):1-11.
[4] 刘知远,孙茂松,林衍凯,等. 知识表示学习研究进展[J]. 计算机研究与发展, 2016,53(2):247-261. LIU Zhiyuan,SUN Maosong,LIN Yankai,et al. Knowledge representation learning: A review[J]. Journal of Computer Research and Development, 2016,53(2): 247-261.
[5] 刘 峤,韩明皓,杨晓慧, 等. 基于表示学习和语义要素感知的关系推理算法[J]. 计算机研究与发展, 2017, 54(8): 1682-1692. LIU Qiao, HAN Minghao, YANG Xiaohui,et al. Representation learning based relational inference algorithm with semantical aspect awareness[J]. Journal of Computer Research and Development, 2017, 54(8): 1682-1692.
[6] 王耀辉, 李越阳. 电网隐性知识的概念图构建方法[J]. 电测与仪表, 2013, 50(10):10-13. WANG Yaohui,LI Yueyang. Construction method for the concept maps of power grid tacit-knowledge[J]. Electrical Measurement & Instrumentation, 2013, 50(10):10-13.
[7] 徐增林, 盛泳潘, 贺丽荣,等. 知识图谱技术综述[J]. 电子科技大学学报, 2016, 45(4):589-606. XU Zenglin,SHENG Yongpan,HE Lirong, et al. Review on knowledge graph techniques[J]. Journal of University of Electronic Science and Technology of China, 2016, 45(4):589-606.
[8] 刘梓权,王慧芳. 基于知识图谱技术的电力设备缺陷记录检索方法[J]. 电力系统自动化,2018,42(14):158-164. LIU Ziquan,WANG Huifang.Retrieval method for defect records of power equipment based on knowledge graph technology[J]. Automation of Electric Power Systems,2018,42(14):158-164.
[9] 张延旭,胡春潮,黄 曙,等.基于Apriori算法的二次设备缺陷数据挖掘与分析方法[J]. 电力系统自动化,2017,41(19):147-151. ZHANG Yanxu,HU Chunchao,HUANG Shu, et al. Apriori algorithm based data mining and analysis method for secondary device defects[J]. Automation of Electric Power Systems,2017,41(19):147-151.
[10] 黄天恩, 孙宏斌, 郭庆来, 等. 基于电网运行仿真大数据的知识管理和超前安全预警[J]. 电网技术, 2015, 39(11):3080-3087. HUANG Tian’en, SUN Hongbin, GUO Qinglai, et al. Knowledge management and security early warning based on big simulation data in power grid operation[J]. Power System Technology, 2015, 39(11):3080-3087.
[11] 王新刚, 祝恩国, 朱彬若, 等. 基于“多表合一”系统的智能表异常诊断及处理方法研究[J]. 电测与仪表, 2018,55(2):86-91. WANG Xingang, ZHU Enguo, ZHU Binruo,et al. Study on abnormity diagnosis and treatment method of smart meters based on multiple metering system[J]. Electrical Measurement & Instrumentation, 2018,55(2):86-91.
[12] 王 磊, 陈 青, 高湛军. 输电网故障诊断的知识表示方法及其应用[J]. 中国电机工程学报, 2012,32(4):85-92. WANG Lei CHEN Qing GAO Zhanjun. Representation and application of fault diagnosis knowledge in power transmission grids[J]. Proceeding of the CSEE, 2012,32(4):85-92.
[13] 张 欣,李高扬,黄荣辉,等. 不同运行年限的GIS缺陷率统计分析与运维建议[J]. 高压电器,2016,52(3):184-188. ZHANG Xin,LI Gaoyang,HUANG Ronghui,et al.Statistical analysis of defects and maintenance advice for gis in different operating years above 110 kV[J]. High Voltage Apparatus,2016,52(3):184-188.
[14] 程建登,吴 斌,毛文俊,等.特高压换流站故障统计与反措[J]. 高压电器,2018,54(12):292-304. CHENG Jiandeng,WU Bin,MAO Wenjun,et al. Failure statistics and countermeasures of UHVDC converter stations[J]. High Voltage Apparatus,2018,54(12):292-304.
[15] 曾令男, 丁建伟, 赵 炯, 等. 基于互信息的复杂装备高维状态监测数据相关性发现与建模[J]. 计算机集成制造系统, 2013, 19(12): 3018-3025. ZENG Lingnan,DING Jianwei,ZHAO Jiong, et al. Detecting and modeling for associations between high-dimension condition monitoring data of complex equipment based on mutual information[J]. Computer Integrated Manufacturing Systems, 2013, 19(12): 3018-3025.
[16] 代杰杰, 宋 辉, 盛戈皞,等. 考虑复杂关联关系深度挖掘的变压器状态参量预测方法[J]. 中国电机工程学报, 2019, 39(2): 621-628. DAI Jiejie, SONG Hui, SHENG Gehao,et al. A prediction method for power transformers state parameters based on deep association relation mining[J]. Proceedings of the CSEE, 2019, 39(2): 621-628.
[17] 谢荣斌,张 登,林福昌,等.基于关联规则与变权重的变压器状态评估方法[J]. 高压电器,2014,50(1):133-135. XIE Rongbin,ZHANG Deng,LIN Fuchang,et al.Transformer condition assessment using association rules and variable weight[J]. High Voltage Apparatus,2014,50(1):133-135.
[18] 徐祥海,杨 翾,时 锐,等.一种基于输变电设备集中监控信息的试运行变电站风险评估方法[J].高压电器,2018,54(4):245-249. XU Xianghai,YANG Xuan,SHI Rui,et al. Risk assessment method of substation in trial stage based on centralized monitoring of transmission and transformation equipment[J]. High Voltage Apparatus,2018,54(4):245-249.
[19] SZéKELY G J, RIZZO M L, BAKIROV N K. Measuring and testing dependence by correlation of distances[J]. Annals of Statistics, 2007, 35(6):2769-2794.
[20] RESHEF D N , RESHEF Y A , FINUCANE H K , et al. Detecting novel associations in large data sets[J]. Science, 2011, 334(60-62):1518-1524.

备注/Memo

备注/Memo:
彭红霞(1977—) ,女,本科,高级工程师,主要从事变电运维检修研究工作。 文 艳 (1975—),女,硕士,高级工程师,主要从事电力系统及其自动化方面的研究。 于大洋(1979—),男,工学博士,副教授,研究方向为电力系统建模与优化。收稿日期:2019-03-05; 修回日期:2019-04-17 基金项目:国网山东省电力公司科技项目(SGSDHZ00BDJS1800441)。 Project Supported by Science and Technology Project of State Grid Shandong Electric Power Company(SGSDHZ00BDJS1800441).
更新日期/Last Update: 2019-07-15