基于灰靶理论与云模型的电压暂降事件数据挖掘分析方法
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摘要
对电压暂降事件记录中所蕴含的知识进行总结,挖掘不同故障场景与节点电压暂降影响程度之间的关系,可以为管理部门制定决策提供有价值的信息。采用自适应高斯云变换算法,对与电压暂降相关的各类连续量数据进行离散,通过扫描一遍原始事务库构建维度矩阵,以此代替传统Apriori Tid算法中的项目集,形成了空间和时间效率都有所提升的改进算法。随后结合灰靶理论,基于隶属度构建指标序列,建立实际场景与各强关联规则之间的匹配模型,在当前分析里筛选出最能反映实际场景中节点受电压暂降影响规律的知识。实例分析验证了所提方法的实用性和有效性。
This paper summarizes the knowledge contained in voltage sag event records and mines the relationship between different fault scenarios and site voltage sag severity, providing valuable information for management departments to make decisions. The adaptive Gaussian cloud transformation algorithm is used to discretize the continuous data related to voltage sag. A dimension matrix is constructed by once scanning original transaction database to replace the item sets in traditional AprioriTid algorithm. An algorithm improving both space and time efficiency is proposed. Then, combined with gray target theory, index sequences are built based on membership to construct the matching model between actual scenes and strong association rules. Finally, the knowledge best reflecting the law of the effect of voltage sag in actual analysis is selected. Practical example analysis validates practicability and effectiveness of the proposed method.
This paper summarizes the knowledge contained in voltage sag event records and mines the relationship between different fault scenarios and site voltage sag severity, providing valuable information for management departments to make decisions. The adaptive Gaussian cloud transformation algorithm is used to discretize the continuous data related to voltage sag. A dimension matrix is constructed by once scanning original transaction database to replace the item sets in traditional AprioriTid algorithm. An algorithm improving both space and time efficiency is proposed. Then, combined with gray target theory, index sequences are built based on membership to construct the matching model between actual scenes and strong association rules. Finally, the knowledge best reflecting the law of the effect of voltage sag in actual analysis is selected. Practical example analysis validates practicability and effectiveness of the proposed method.
引文
[1]袁媛,吴丹岳,林焱,等.电压暂降评估体系研究[J].电网技术,2010,34(6):128-133.Yuan Yuan,Wu Danyue,Lin Yan,et al.Study on assessment system of voltage sags[J].Power System Technology,2010,34(6):128-133(in Chinese).
[2]Goswami A K.Voltage sag assessment in a large chemical industry[J].IEEE Transactions on Industry Applications,2012,48(5):1739-1746.
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[4]赵会茹,欧大昌,李天友,等.基于模糊隶属函数的电压骤降损失估算[J].电力自动化设备,2012,32(11):51-55.Zhao Huiru,Ou Dachang,Li Tianyou,et al.Voltage sag loss estimation based on fuzzy membership function[J].Electric Power Automation Equipment,2012,32(11):51-55(in Chinese).
[5]刘旭娜,肖先勇,汪颖.电压暂降严重程度及其测度、不确定性评估方法[J].中国电机工程学报,2014,34(4):644-658.Liu Xuna,Xiao Xianyong,Wang Ying.Voltage sag severity and its measure and uncertainty evaluation[J].Proceedings of the CSEE,2014,34(4):644-658(in Chinese).
[6]章雪萌,李国栋,徐永海,等.基于预估的电压暂降技术经济评估[J].电网技术,2010,34(10):121-125.Zhang Xuemeng,Li Guodong,Xu Yonghai,et al.Technical and economic assessment of voltage sag based on stochastic prediction[J].Power System Technology,2010,34(10):121-125(in Chinese).
[7]周翔,王丰华,黄荣辉,等.考虑系统与敏感设备的变电站电压暂降综合评估[J].中国电机工程学报,2015,35(8):1940-1946.Zhou Xiang,Wang Fenghua,Huang Ronghui,et al.Assessment of voltage sags in substations based on power system and equipment sensitivity analysis[J].Proceedings of the CSEE,2015,35(8):1940-1946(in Chinese).
[8]胡军,尹立群,李振,等.基于大数据挖掘技术的输变电设备故障诊断方法[J].高电压技术,2017,43(11):3690-3697.Hu Jun,Yin Liqun,Li Zhen,et al.Fault diagnosis method of transmission and transformation equipment based on big data mining technology[J].High Voltage Engineering,2017,43(11):3690-3697(in Chinese).
[9]李黎,张登,谢龙君.采用关联规则综合分析和变权重系数的电力变压器状态评估方法[J].中国电机工程学报,2013,33(24):152-159.Li Li,Zhang Deng,Xie Longjun.A condition assessment method of power transformers based on association rules and variable weight coefficients[J].Proceedings of the CSEE,2013,33(24):152-159(in Chinese).
[10]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 defect[J].Automation of Electric Power Systems,2017,41(19):147-151(in Chinese).
[11]徐特威,鲁宗相,乔颖,等.基于典型故障与环境场景关联识别的城市配电网运行风险预警方法[J].电网技术,2017,41(8):2577-2584.Xu Tewei,Lu Zongxiang,Qiao Ying,et al.A risk warning method for urban distribution network based on associated recognition of typical fault and environment scenario[J].Power System Technology,2017,41(8):2577-2584(in Chinese).
[12]曲广龙,杨洪耕.基于梯形云模型的电能质量数据关联性挖掘方法[J].电力系统自动化,2015,39(7):145-150.Qu Guanglong,Yang Honggeng.A correlation mining method for power quality data based on trapezoidal cloud model[J].Automation of Electric Power Systems,2015,39(7):145-150(in Chinese).
[13]李德仁,王树良,李德毅.空间数据挖掘理论与应用[M].北京:科学出版社,2010:141-144.
[14]杨洁,王国胤,刘群,等.正态云模型研究回顾与展望[N].计算机学报,2017-08-19(1).
[15]刘玉超.一种自适应的多粒度概念提取方法:高斯云变换[J].计算机工程与应用,2015,51(9):1-8.Liu Yuchao.Adaptive concept abstraction method on multigranularity:Gaussian cloud transformation[J].Computer Engineering and Applications,2015,51(9):1-8(in Chinese).
[16]李爱国,厍向阳.数据挖掘原理、算法及应用[M].西安:西安电子科技大学出版社,2012:29-36.
[17]刘树鑫,曹云东,侯春光,等.改进的灰靶理论在固体环网柜绝缘状态评估中应用的研究[J].电网技术,2013,37(12):3577-3583.Liu Shuxin,Cao Yundong,Hou Chunguang,et al.Application of improved gray target theory based assessment on insulation state of solid-insulated ring main unit[J].Power System Technology,2013,37(12):3577-3583(in Chinese).
[18]李德毅,杜鹢.不确定性人工智能[M].北京:国防工业出版社,2014:50-53.
[2]Goswami A K.Voltage sag assessment in a large chemical industry[J].IEEE Transactions on Industry Applications,2012,48(5):1739-1746.
[3]储佳伟,袁晓冬,陈兵,等.结合小波分析和改进型DTW距离的配电网电压暂降源辨识方法[J].电网技术,2018,42(2):637-643.Chu Jiawei,Yuan Xiaodong,Chen Bing,et al.A method for distribution network voltage sag source identification combining wavelet analysis and modified DTW distance[J].Power System Technology,2018,42(2):637-643(in Chinese).
[4]赵会茹,欧大昌,李天友,等.基于模糊隶属函数的电压骤降损失估算[J].电力自动化设备,2012,32(11):51-55.Zhao Huiru,Ou Dachang,Li Tianyou,et al.Voltage sag loss estimation based on fuzzy membership function[J].Electric Power Automation Equipment,2012,32(11):51-55(in Chinese).
[5]刘旭娜,肖先勇,汪颖.电压暂降严重程度及其测度、不确定性评估方法[J].中国电机工程学报,2014,34(4):644-658.Liu Xuna,Xiao Xianyong,Wang Ying.Voltage sag severity and its measure and uncertainty evaluation[J].Proceedings of the CSEE,2014,34(4):644-658(in Chinese).
[6]章雪萌,李国栋,徐永海,等.基于预估的电压暂降技术经济评估[J].电网技术,2010,34(10):121-125.Zhang Xuemeng,Li Guodong,Xu Yonghai,et al.Technical and economic assessment of voltage sag based on stochastic prediction[J].Power System Technology,2010,34(10):121-125(in Chinese).
[7]周翔,王丰华,黄荣辉,等.考虑系统与敏感设备的变电站电压暂降综合评估[J].中国电机工程学报,2015,35(8):1940-1946.Zhou Xiang,Wang Fenghua,Huang Ronghui,et al.Assessment of voltage sags in substations based on power system and equipment sensitivity analysis[J].Proceedings of the CSEE,2015,35(8):1940-1946(in Chinese).
[8]胡军,尹立群,李振,等.基于大数据挖掘技术的输变电设备故障诊断方法[J].高电压技术,2017,43(11):3690-3697.Hu Jun,Yin Liqun,Li Zhen,et al.Fault diagnosis method of transmission and transformation equipment based on big data mining technology[J].High Voltage Engineering,2017,43(11):3690-3697(in Chinese).
[9]李黎,张登,谢龙君.采用关联规则综合分析和变权重系数的电力变压器状态评估方法[J].中国电机工程学报,2013,33(24):152-159.Li Li,Zhang Deng,Xie Longjun.A condition assessment method of power transformers based on association rules and variable weight coefficients[J].Proceedings of the CSEE,2013,33(24):152-159(in Chinese).
[10]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 defect[J].Automation of Electric Power Systems,2017,41(19):147-151(in Chinese).
[11]徐特威,鲁宗相,乔颖,等.基于典型故障与环境场景关联识别的城市配电网运行风险预警方法[J].电网技术,2017,41(8):2577-2584.Xu Tewei,Lu Zongxiang,Qiao Ying,et al.A risk warning method for urban distribution network based on associated recognition of typical fault and environment scenario[J].Power System Technology,2017,41(8):2577-2584(in Chinese).
[12]曲广龙,杨洪耕.基于梯形云模型的电能质量数据关联性挖掘方法[J].电力系统自动化,2015,39(7):145-150.Qu Guanglong,Yang Honggeng.A correlation mining method for power quality data based on trapezoidal cloud model[J].Automation of Electric Power Systems,2015,39(7):145-150(in Chinese).
[13]李德仁,王树良,李德毅.空间数据挖掘理论与应用[M].北京:科学出版社,2010:141-144.
[14]杨洁,王国胤,刘群,等.正态云模型研究回顾与展望[N].计算机学报,2017-08-19(1).
[15]刘玉超.一种自适应的多粒度概念提取方法:高斯云变换[J].计算机工程与应用,2015,51(9):1-8.Liu Yuchao.Adaptive concept abstraction method on multigranularity:Gaussian cloud transformation[J].Computer Engineering and Applications,2015,51(9):1-8(in Chinese).
[16]李爱国,厍向阳.数据挖掘原理、算法及应用[M].西安:西安电子科技大学出版社,2012:29-36.
[17]刘树鑫,曹云东,侯春光,等.改进的灰靶理论在固体环网柜绝缘状态评估中应用的研究[J].电网技术,2013,37(12):3577-3583.Liu Shuxin,Cao Yundong,Hou Chunguang,et al.Application of improved gray target theory based assessment on insulation state of solid-insulated ring main unit[J].Power System Technology,2013,37(12):3577-3583(in Chinese).
[18]李德毅,杜鹢.不确定性人工智能[M].北京:国防工业出版社,2014:50-53.
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