基于重要度排序与全寿命周期的配网故障治理措施优化方法
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摘要
为优化配网故障治理措施,提升配网供电可靠性,针对配网故障治理措施优化问题,以故障、投诉、措施实施空间为约束条件,以综合效益优化为目标函数,基于Prato最优原则和Well-bing模型,提出一种基于设备重要度排序和全寿命周期的配网优选设备的治理措施。基于Well-bing模型的配网设备重要度排序综合考虑了环境的恶劣度、网络的复杂度和设备的重要度,与状态检修不同,基于大数据的重要度评价,评估输入的底层基础数据更丰富、评估结果更合理可靠。基于Prato最优原则,以设备全寿命周期内的综合经济性最优为目标,按照设备重要度排序进行差异化治理措施优选,治理措施更精准。基于上述方法,对某重过载配变及其所处的区域进行了治理措施优化,实际应用效果验证了该方法的有效性。
In order to optimize the network fault management measures,improve the reliability of distribution network power supply,optimize the problem of distribution network fault management measures,take the fault,complaint,and measure implementation space as the constraint conditions,and comprehensive benefit optimization as the objective function,based on the Prato optimal principle and The Well-bing model proposes a governance measure for distribution network optimization equipment based on equipment importance ranking and life cycle. Based on the Well-bing model,the ranking of the importance of the distribution network equipment takes into account the harshness of the environment,the complexity of the network and the importance of the equipment. Unlike the state maintenance,the evaluation of the importance of the equipment based on big data evaluates the underlying basic data of the input. More abundant,the evaluation results are more reasonable and reliable. Based on Prato 's optimal principle,aiming at the comprehensive economical optimality in the whole life cycle of equipment,the differentiated control measures are optimized according to the order of equipment importance,and the control measures are more precise. Based on the above method,the control measures of a heavy overload distribution and its area are optimized. The practical application results verify the effectiveness of the method.
In order to optimize the network fault management measures,improve the reliability of distribution network power supply,optimize the problem of distribution network fault management measures,take the fault,complaint,and measure implementation space as the constraint conditions,and comprehensive benefit optimization as the objective function,based on the Prato optimal principle and The Well-bing model proposes a governance measure for distribution network optimization equipment based on equipment importance ranking and life cycle. Based on the Well-bing model,the ranking of the importance of the distribution network equipment takes into account the harshness of the environment,the complexity of the network and the importance of the equipment. Unlike the state maintenance,the evaluation of the importance of the equipment based on big data evaluates the underlying basic data of the input. More abundant,the evaluation results are more reasonable and reliable. Based on Prato 's optimal principle,aiming at the comprehensive economical optimality in the whole life cycle of equipment,the differentiated control measures are optimized according to the order of equipment importance,and the control measures are more precise. Based on the above method,the control measures of a heavy overload distribution and its area are optimized. The practical application results verify the effectiveness of the method.
引文
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[3]韩笑,狄方春,刘广一,等.应用智能电网统一数据模型的大数据应用架构及其实践[J].电网技术,2016,40(10):3206-3212.HAN Xiao,DI Fangchun,LIU Guangyi,et al.A big data application structure based on smart grid data model and its practice[J].Power System Technology,2016,40(10):3206-3212.
[4]贺建章,王海波,季知祥,等.面向智能电网的配网变压器重过载影响因素分析[J].电网技术,2017,41(01):279-284.HE Jianzhang,WANG Haibo,JI Zhixiang,et al.Analysis of factors affecting distribution transformer overload in smart grid[J].Power System Technology,2017,41(01):279-284.
[5]吴润泽,张保健,唐良瑞.双网耦合模型中基于级联失效的节点重要度评估[J].电网技术,2015,39(04):1053-1058.WU Runze,ZHANG Baojian,TANG Liangrui.A cascading failure based nodal importance evaluation method applied in dual Network coupling model[J].Power System Technology,2015,39(04):1053-1058..
[6]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(01):265-271.HU Lijuan,DIAO Yinglong,LIU Keyan,et al.Operational reliability analysis of distribution network based on big data technology[J].Power System Technology,2017,41(01):265-271.
[7]MING LI,QIN ZHOU.Distribution transformer midterm heavy and over load pre-warning based on logistic regression[C]//2015 IEEEEindhoven Power Tech.Eindhoven,Netherlands:IEEE,2015:1-6.
[8]何治安,廖宇峰,王荣威,等.基于LCC的重过载小容量配变改造策略[J].机电工程技术,2016,45(05):53-57.HE Zhian,LIAO Yufeng,WANG Rongwei,et al.Reform strategy of heavy and overload transformer of small capacity based on LCC[J].Mechanical&Electrical Engineering Technology,2016,45(05):53-57.
[9]赵庆周,李勇,田世明,等.基于智能配电网大数据分析的状态监测与故障处理方法[J].电网技术,2016,40(03):774-780.ZHAO Qingzhou,LI Yong,TIAN Shiming,et al.A State estimation and fault processing method based on big data analysis of smart distribution network[J].Power System Technology,2016,40(03):774-780.
[10]张友强,寇凌峰,盛万兴,等.配电变压器运行状态评估的大数据分析方法[J].电网技术,2016,40(03):768-773.ZHANG Youqiang,KOU Lingfeng,SHENG Wanxing,et al.Urban big data analytical method for operating state assessment of distribution transformer[J].Power System Technology,2016,40(03):768-773.
[11]张恒,王建学,曹晓宇.采用全寿命周期成本与风险分析的农村配电变压器更换投资策略[J].西安交通大学学报,2015,49(08):133-140.ZHANG Heng,WANG Jianxue,CAO Xiaoyu.Replacement and investment strategy for distribution transformers in rural area based on life cycle cost theory and risk assessment[J].Journal of Xi'an Jiaotong University,2015,49(08):133-140.
[12]杨楠,李宏圣,袁景颜,等.计及不确定性和全寿命周期成本的配电变压器规划方法[J].电力系统自动化,2017,41(21):94-104.YANG Nan,LI Hongsheng,YUAN Jingyan,et al.Planning method of distribution transformer considering uncertainty and life cycle cost[J].Automation of Electric Power Systems,2017,41(21):94-104.
[13]JAN BEYEA.The smart electricity grid and scientific research[J].Science,2016,328(5981):979-980.
[14]KIUHUAHUANG,MIKE ZHOU,YAO ZHANG,et al.Exploiting cloud computing for power system analysis[C]//International Conference on Power System Technology.Guangzhou:2016:1-6.
[15]ARMANDO FOX.Cloud computing---What’s in it for me as a scientist[J].Science,2016,331(6016):406-407.
[16]JAYANT BALIGA,ROBERT W.A.AYRE,RODNEY S.TUCKER,et al.Green cloud computing:balancing energy in processing,storage,and transport[J].Proceeding of the IEEE,2016,99(01):149-167.