SVM在中压配网停电事件补全中的应用研究
|
摘要
应用大数据平台深入挖掘计量数据对配电网的运行支撑是当前电网重要研究方向,文中应用支持向量机(SVM)算法研究中压配网停电事件补全方法,解决停电事件准确统计难题。首先总结中压配电网的5类停电事件,接着重点研究了SVM补全方法,给出停电事件补全思路,5类停电事件的SVM补全模型构建方法,并提出了涵盖配电网模型构建、SVM模型构建、SVM求解及故障类型判断的补全流程,然后从工程应用角度,设计了补全模块与用电信息采集等各相关系统间的业务关系框架并进行数据分析架构设计。最后以安徽黄山等4家地市公司为例进行了实践应用分析,验证了文中研究方法可极大提升停电事件统计的及时性和准确性。
Deeply research metering data with the big data analysis platform for the distribution network operation is one of the key research directions in the power grid currently. Power outage event completion method based on SVM algorithm to solve accurate statistical problems for MV distribution network was researched in this paper. Firstly,based on summarizing the five types of power outages in the MV distribution network,the SVM completion method is mainly researched. And the power outage event completion idea based on SVM and a full-process of SVM completion model construction method for the five types of power outage events are given. Besides,the completion process of distribution network model construction,vector machine SVM construction,vector machine SVM solution and judgment of fault type are proposed. Then,the business relationship framework among the complementary modules and other related systems,the data analysis architecture based on the big data platform are designed from the perspective of engineering application. Finally,take the application of Anhui Huangshan and other three cities as an example,it verifies that the completion method can greatly improve the timeliness and accuracy of power failure event collection.
Deeply research metering data with the big data analysis platform for the distribution network operation is one of the key research directions in the power grid currently. Power outage event completion method based on SVM algorithm to solve accurate statistical problems for MV distribution network was researched in this paper. Firstly,based on summarizing the five types of power outages in the MV distribution network,the SVM completion method is mainly researched. And the power outage event completion idea based on SVM and a full-process of SVM completion model construction method for the five types of power outage events are given. Besides,the completion process of distribution network model construction,vector machine SVM construction,vector machine SVM solution and judgment of fault type are proposed. Then,the business relationship framework among the complementary modules and other related systems,the data analysis architecture based on the big data platform are designed from the perspective of engineering application. Finally,take the application of Anhui Huangshan and other three cities as an example,it verifies that the completion method can greatly improve the timeliness and accuracy of power failure event collection.
引文
[1]杨文丽.计及可靠性评估的中压配电网规划方案比选[J].电力工程技术,2018,37(2):13-19,43.YANG Wenli. Comparison and selection of mv distribution network planning schemes considering reliability assessment[J].Electric Power Engineering Technology,2018,37(2):13-19,43.
[2]欧林,赵晓龙,段祥骏,等.计及信息系统故障的配电网可靠性分析[J].电力系统保护与控制,2017,45(15):104-111.OU Lin,ZHAO Xiaolong,DUAN Xiangjun,et al. Reliability analysis of distribution network considering the fault of information system[J]. Power System Protection and Control,2017,45(15):104-111.
[3]文东山,暴英凯,章禹,等.电力系统操作人因可靠性分析及其数据库系统研究[J].电力系统保护与控制,2017,45(11):35-42.WEN Dongshan,BAO Yingkai,ZHANG Yu,et al. Analysis of human reliability in power system operation and research on its database system[J]. Power System Protection and Control,2017,45(11):35-42.
[4]余文辉,王凯琳,高松川.供电可靠性关键指标分析方法及应用[J].广东电力,2017,30(7):121-125.YU Wenhui,WANG Kailin,GAO Songchuan. Analysis method for critical indicators of power supply reliability and its application[J]. Guangdong Electric Power,2017,30(7):121-125.
[5]胡江溢,祝恩国,杜新纲,等.用电信息采集系统应用现状及发展趋势[J].电力系统自动化,2014(2):131-135.HU Jiangyi,ZHU Enguo,DU Xingang,et al. Application status and development trend of power consumption information collection system[J]. Automation of Electric Power Systems,2014(2):131-135.
[6]钱立军,李新家.用电信息采集系统中数据比对功能的实现及应用[J].江苏电机工程,2013(2):64-65,70.QIAN Lijun,LI Xinjia. Power usage information acquisition data accuracy automatic confirmation technology and its application[J]. Jiangsu Electrical Engineering,2013(2):64-65,70.
[7]武文广,王朝亮,叶方彬,等.用电信息采集系统多业务高效协同处理关键技术[J].智慧电力,2017,45(11):78-84.WU Wenguang,WANG Chaoliang,YE Fangbin,et al. Key technologies in high efficiency coprocessing of multi-service for power consumption information acquisition system[J]. Smart Power,2017,45(11):78-84.
[8]范开俊,徐丙垠,董俊,等.基于智能终端逐级查询的馈线拓扑识别方法[J].电力系统自动化,2015,39(11):180-185.FAN Kaijun,XU Binggen,DONG Jun,et al. Identification method for feeder topology based on successive polling of smart terminal unit[J]. Automation of Electric Power Systems,2015,39(11):180-185.
[9]应俊,梅军,王勇,等.配电主站馈线拓扑模型校核方法研究[J].电力系统保护与控制,2018,46(7):83-89.YING Jun,MEI Jun,WANG Yong,et al. Research on verification method of feeder topology model for distribution main station[J]. Power System Protection and Control,2018,46(7):83-89.
[10]刘科研,吴心忠,石琛,等.基于数据挖掘的配电网故障风险预警[J].电力自动化设备,2018,38(5):148-153.LIU Keyan,WU Xinzhong,SHI Chen,et al. Fault risk early warning of distribution network based on data mining[J]. Electric Power Automation Equipment,2018,38(5):148-153.
[11]宋杰,谢海宁,杨增辉,等.基于多源异构数据挖掘的配电网故障信息统计分析[J].电力系统保护与控制,2016,44(3):141-147.SONG Jie,XIE Haining,YANG Zenghui,et al. Statistical analysis of the distribution fault based on multiple source and isomerism data mining technology[J]. Power System Protection&Control,2016,44(3):141-147.
[12]胡伟,李勇,曹一家,等.基于LOF和SVM的智能配电网故障辨识方法[J].电力自动化设备,2016,36(6):7-12.HU Wei,LI Yong,CAO Yijia,et al. Fault identification based on LOF and SVM for smart distribution network[J]. Electric Power Automation Equipment,2016,36(6):7-12.
[13]边莉,边晨源.采用交叉熵支持向量机和模糊积分的电网故障诊断[J].电机与控制学报,2016,20(2):112-120.BIAN Li,BIAN Chenyuan. Fault diagnosis of power networks applying CE-SVM and fuzzy integral fusion[J]. Electric Machines&Control,2016,20(2):112-120.
[14]韩世军,朱菊,毛吉贵,等.基于粒子群优化支持向量机的变压器故障诊断[J].电测与仪表,2014,51(11):71-75,90.HAN Shijun,ZHU Ju,MAO Jigui,et. al. Fault diagnosis of transformer based on particle swarm optimization-based support vector machine[J]. Electrical Measurement&Instrumentation,2014,51(11):71-75,90.
[15]郭创新,朱承治,张琳,等.应用多分类多核学习支持向量机的变压器故障诊断方法[J].中国电机工程学报,2010,30(13):128-134.GUO Chuangxin,ZHU Chengzhi,ZHANG Lin,et al. A fault diagnosis method for power transformer based on multiclass multiple-kernel learning support vector machine[J]. Proceedings of the CSEE,2010,30(13):128-134.
[16]吕干云,程浩忠,董立新,等.基于多级支持向量机分类器的电力变压器故障识别[J].电力系统及其自动化学报,2005,17(1):19-22.LYU Ganyun,CHENG Haozhong,DONG Lixin,et al. Fault diagnosis of power transformer based on multi-layer SVM classifier[J]. Proceedings of the CSU-EPSA,2005,17(1):19-22.
[17]游大宁,瞿寒冰,霍健,等.配网抢修指挥故障研判策略研究[J].电力系统保护与控制,2018(13):84-91.YOU Daning,QU Hanbing,HUO Jian,et al. Research on fault analysis and identification strategy for distribution network[J]. Power System Protection&Control,2018(13):84-91.
[18]文乐斌,张小易,袁宇波.基于SCADA实时数据的配电网故障智能诊断统计系统研究[J].华东电力,2013,41(12):2514-2517.WEN Lebin,ZHANG Xiaoyi,YUAN Yubo. Statistics system for distribution network intelligent fault diagnosis based on scada real-time data[J]. East China Electric Power,2013,41(12):2514-2517.
[19]向驰,石文娟,于伟,等.新型城镇配电网形态特征及典型供电模式[J].江苏电机工程,2016,35(3):64-67,70.XIANG Chi,SHI Wenjuan,YU Wei,et al. The distribution network morphological characteristics and typical power supply modes for the new-type towns[J]. Jiangsu Electrical Engineering,2016,35(3):64-67,70.
[20]李林峰,孙长银.基于FCM聚类与SVM的电力系统短期负荷预测[J].江苏电机工程,2007(3):47-50.LI Linfeng,SUN Changyin. Power system short-term load forecasting based on fuzzy-c mean clustering algorithm and support vector machines[J]. Jiangsu Electrical Engineering,2007(3):47-50.
[21]蒋浩,洪丽,张国江.主成分分析结合神经网络的光伏发电量预测[J].电力系统及其自动化学报,2013,25(6):101-105.JIANG Hao,HONG Li,ZHANG Guojiang. PV generation system forecasting model based on neutral network and principal components analysis[J]. Proceedings of the CSU-EPSA,2013,25(6):101-105.
[22]王冬,杨永标,高辉,等.基于主成分分析法的电力用户节能减排因素相关性分析[J].电器与能效管理技术,2015(12):66-69.WANG Dong,YANG Yongbiao,GAO Hui,et al. Correlation analysis on energy conservation and emission reduction for electricity consumers based on principal component analysis[J].Electrical&Energy Management Technology,2015(12):66-69.
[2]欧林,赵晓龙,段祥骏,等.计及信息系统故障的配电网可靠性分析[J].电力系统保护与控制,2017,45(15):104-111.OU Lin,ZHAO Xiaolong,DUAN Xiangjun,et al. Reliability analysis of distribution network considering the fault of information system[J]. Power System Protection and Control,2017,45(15):104-111.
[3]文东山,暴英凯,章禹,等.电力系统操作人因可靠性分析及其数据库系统研究[J].电力系统保护与控制,2017,45(11):35-42.WEN Dongshan,BAO Yingkai,ZHANG Yu,et al. Analysis of human reliability in power system operation and research on its database system[J]. Power System Protection and Control,2017,45(11):35-42.
[4]余文辉,王凯琳,高松川.供电可靠性关键指标分析方法及应用[J].广东电力,2017,30(7):121-125.YU Wenhui,WANG Kailin,GAO Songchuan. Analysis method for critical indicators of power supply reliability and its application[J]. Guangdong Electric Power,2017,30(7):121-125.
[5]胡江溢,祝恩国,杜新纲,等.用电信息采集系统应用现状及发展趋势[J].电力系统自动化,2014(2):131-135.HU Jiangyi,ZHU Enguo,DU Xingang,et al. Application status and development trend of power consumption information collection system[J]. Automation of Electric Power Systems,2014(2):131-135.
[6]钱立军,李新家.用电信息采集系统中数据比对功能的实现及应用[J].江苏电机工程,2013(2):64-65,70.QIAN Lijun,LI Xinjia. Power usage information acquisition data accuracy automatic confirmation technology and its application[J]. Jiangsu Electrical Engineering,2013(2):64-65,70.
[7]武文广,王朝亮,叶方彬,等.用电信息采集系统多业务高效协同处理关键技术[J].智慧电力,2017,45(11):78-84.WU Wenguang,WANG Chaoliang,YE Fangbin,et al. Key technologies in high efficiency coprocessing of multi-service for power consumption information acquisition system[J]. Smart Power,2017,45(11):78-84.
[8]范开俊,徐丙垠,董俊,等.基于智能终端逐级查询的馈线拓扑识别方法[J].电力系统自动化,2015,39(11):180-185.FAN Kaijun,XU Binggen,DONG Jun,et al. Identification method for feeder topology based on successive polling of smart terminal unit[J]. Automation of Electric Power Systems,2015,39(11):180-185.
[9]应俊,梅军,王勇,等.配电主站馈线拓扑模型校核方法研究[J].电力系统保护与控制,2018,46(7):83-89.YING Jun,MEI Jun,WANG Yong,et al. Research on verification method of feeder topology model for distribution main station[J]. Power System Protection and Control,2018,46(7):83-89.
[10]刘科研,吴心忠,石琛,等.基于数据挖掘的配电网故障风险预警[J].电力自动化设备,2018,38(5):148-153.LIU Keyan,WU Xinzhong,SHI Chen,et al. Fault risk early warning of distribution network based on data mining[J]. Electric Power Automation Equipment,2018,38(5):148-153.
[11]宋杰,谢海宁,杨增辉,等.基于多源异构数据挖掘的配电网故障信息统计分析[J].电力系统保护与控制,2016,44(3):141-147.SONG Jie,XIE Haining,YANG Zenghui,et al. Statistical analysis of the distribution fault based on multiple source and isomerism data mining technology[J]. Power System Protection&Control,2016,44(3):141-147.
[12]胡伟,李勇,曹一家,等.基于LOF和SVM的智能配电网故障辨识方法[J].电力自动化设备,2016,36(6):7-12.HU Wei,LI Yong,CAO Yijia,et al. Fault identification based on LOF and SVM for smart distribution network[J]. Electric Power Automation Equipment,2016,36(6):7-12.
[13]边莉,边晨源.采用交叉熵支持向量机和模糊积分的电网故障诊断[J].电机与控制学报,2016,20(2):112-120.BIAN Li,BIAN Chenyuan. Fault diagnosis of power networks applying CE-SVM and fuzzy integral fusion[J]. Electric Machines&Control,2016,20(2):112-120.
[14]韩世军,朱菊,毛吉贵,等.基于粒子群优化支持向量机的变压器故障诊断[J].电测与仪表,2014,51(11):71-75,90.HAN Shijun,ZHU Ju,MAO Jigui,et. al. Fault diagnosis of transformer based on particle swarm optimization-based support vector machine[J]. Electrical Measurement&Instrumentation,2014,51(11):71-75,90.
[15]郭创新,朱承治,张琳,等.应用多分类多核学习支持向量机的变压器故障诊断方法[J].中国电机工程学报,2010,30(13):128-134.GUO Chuangxin,ZHU Chengzhi,ZHANG Lin,et al. A fault diagnosis method for power transformer based on multiclass multiple-kernel learning support vector machine[J]. Proceedings of the CSEE,2010,30(13):128-134.
[16]吕干云,程浩忠,董立新,等.基于多级支持向量机分类器的电力变压器故障识别[J].电力系统及其自动化学报,2005,17(1):19-22.LYU Ganyun,CHENG Haozhong,DONG Lixin,et al. Fault diagnosis of power transformer based on multi-layer SVM classifier[J]. Proceedings of the CSU-EPSA,2005,17(1):19-22.
[17]游大宁,瞿寒冰,霍健,等.配网抢修指挥故障研判策略研究[J].电力系统保护与控制,2018(13):84-91.YOU Daning,QU Hanbing,HUO Jian,et al. Research on fault analysis and identification strategy for distribution network[J]. Power System Protection&Control,2018(13):84-91.
[18]文乐斌,张小易,袁宇波.基于SCADA实时数据的配电网故障智能诊断统计系统研究[J].华东电力,2013,41(12):2514-2517.WEN Lebin,ZHANG Xiaoyi,YUAN Yubo. Statistics system for distribution network intelligent fault diagnosis based on scada real-time data[J]. East China Electric Power,2013,41(12):2514-2517.
[19]向驰,石文娟,于伟,等.新型城镇配电网形态特征及典型供电模式[J].江苏电机工程,2016,35(3):64-67,70.XIANG Chi,SHI Wenjuan,YU Wei,et al. The distribution network morphological characteristics and typical power supply modes for the new-type towns[J]. Jiangsu Electrical Engineering,2016,35(3):64-67,70.
[20]李林峰,孙长银.基于FCM聚类与SVM的电力系统短期负荷预测[J].江苏电机工程,2007(3):47-50.LI Linfeng,SUN Changyin. Power system short-term load forecasting based on fuzzy-c mean clustering algorithm and support vector machines[J]. Jiangsu Electrical Engineering,2007(3):47-50.
[21]蒋浩,洪丽,张国江.主成分分析结合神经网络的光伏发电量预测[J].电力系统及其自动化学报,2013,25(6):101-105.JIANG Hao,HONG Li,ZHANG Guojiang. PV generation system forecasting model based on neutral network and principal components analysis[J]. Proceedings of the CSU-EPSA,2013,25(6):101-105.
[22]王冬,杨永标,高辉,等.基于主成分分析法的电力用户节能减排因素相关性分析[J].电器与能效管理技术,2015(12):66-69.WANG Dong,YANG Yongbiao,GAO Hui,et al. Correlation analysis on energy conservation and emission reduction for electricity consumers based on principal component analysis[J].Electrical&Energy Management Technology,2015(12):66-69.