基于μPMU的智能配电网预想故障集组合筛选方法  

Expected Fault Combination Screening Method for Smart Distribution Network Based onμPMU

作　　者：符杨 司马超 田书欣 蔡鹏程 顾吉平 刘舒 FU Yang;SIMA Chao;TIAN Shuxin;CAI Pengcheng;GU Jiping;LIU Shu(Electric Engineering College,Shanghai University of Electric Power,Shanghai 200090,China;State Grid Shanghai Electric Power Research Institute,Shanghai 200437,China)

机构地区：[1]上海电力大学电气工程学院,上海200090 [2]国网上海市电力公司电力科学研究院,上海200437

出　　处：《电气传动》2023年第1期59-65,73,共8页Electric Drive

基　　金：国家重点研发计划项目(2017YFB0902800);国家自然科学基金项目(52007112)。

摘　　要：智能配电网预想故障集筛选是系统安全态势评估的重要基础。为了全面精准感知智能配电网的安全风险,引入具有实时性、同步性、准确性和量测数据全面性的微型同步相量测量单元(μPMU),提出一种基于其高密集采样数据的融合类内与类间距离的加权K-means聚类方法(KICIC)和云理论的预想故障集组合筛选排序方法。首先遍历智能配电网各节点发生各故障类型的场景构建故障数据集;然后采用KICIC算法进行故障数据集聚类分析,进而基于云模型的云数字特征客观量化评估故障类严重度不确定性的危害并输出预想故障集;最后算例结果表明:融合KICIC聚类和云模型的预想故障集组合筛选排序方法从数据挖掘层面实现高风险预想故障集的可靠筛选。Expected fault screening of smart distribution network is an important basis for system security situation assessment.In order to comprehensively and accurately perceive the security risk of smart distribution network,a micro-synchronous phasor measurement unit(μPMU)with real-time,synchronicity,accuracy and comprehensiveness of measurement data was introduced.A new method for the combination screening and sorting of the expected fault combining based on high density sampling data weighting K-means clustering approach by integrating intra-cluster and inter-cluster distances(KICIC)and cloud theory was proposed.Firstly,the failure scenarios of each node of the smart distribution network were traversed,and the fault data sets were constructed.Then,KICIC algorithm was used to conduct clustering analysis.Based on cloud digital features of cloud model,the hazard of uncertainty of fault severity was quantitatively evaluated,and the expected fault set was outputed.Finally,the calculation results show that the expected fault screening method based on KICIC clustering and cloud model can reliably screen the high-risk expected fault sets in data mining level.

关 键 词：智能配电网 预想故障集组合筛选 融合类内与类间距离的加权K-means聚类方法(KICIC) 云理论 微型同步相量测量单元(μPMU) 

分 类 号：TM28[一般工业技术—材料科学与工程]