Identification and characterization of irregular consumptions of load data

Journal of Modern Power Systems and Clean Energy

Desh Deepak Sharma¹  S. N. Singh²  Jeremy Lin²  Elham Foruzan³ 

[1] Indian Institute of Technology, Kanpur,Kanpur,India;PJM Interconnection,Audubon,PA,USA;University of Nebraska-Lincoln,Department of Electrical Engineering,Lincoln,NE,USA;

关键词: Density based clustering;    Irregular consumption;    Local outlier factor;    Peak demand;    Valley demand;   

DOI  :  10.1007/s40565-017-0268-1

来源: DOAJ

【 摘 要 】

The historical information of loadings on substation helps in evaluation of size of photovoltaic (PV) generation and energy storages for peak shaving and distribution system upgrade deferral. A method, based on consumption data, is proposed to separate the unusual consumption and to form the clusters of similar regular consumption. The method does optimal partition of the load pattern data into core points and border points, high and less dense regions, respectively. The local outlier factor, which does not require fixed probability distribution of data and statistical measures, ranks the unusual consumptions on only the border points, which are a few percent of the complete data. The suggested method finds the optimal or close to optimal number of clusters of similar shape of load patterns to detect regular peak and valley load demands on different days. Furthermore, identification and characterization of features pertaining to unusual consumptions in load pattern data have been done on border points only. The effectiveness of the proposed method and characterization is tested on two practical distribution systems.

【 授权许可】

Unknown