| With the access of distributed energy,energy storage and multiple loads,the construction of distribution network is becoming mature and its operation mode is flexible and changeable.With the continuous reform of electric power system at the same time,the user to improve power supply reliability requirements,how to get through the last mile of user reliability management needs,quantify the power supply requirements of end users,to satisfy different region,the differentiation of different types of user power supply reliability requirements,improve the level of the power supply reliability of distribution network,is the current needs to solve the problem.So this article combined with the end user electricity information data fusion and the power of the depth of mining methods to analyze the user behavior,the quantitative analysis of end user’s demand,big data clustering and grey correlation method was adopted to realize the end user attributes of power division,and through the fusion of multi-source data based on attribute partition area outage information to intelligence.The specific work is as follows:(1)The end-user power consumption data fusion technology combining the isolated forest theory and cubic spline interpolation is studied to achieve the location and interpolation of outliers in the user power consumption data and form excellent sample data;Then,in view of the features of high-dimensional data,feature extraction of sample data is realized by studying the segmentation aggregation approximation based on information entropy,and then the typical load curve of users is extracted by spectral clustering algorithm,which provides the data basis for the quantification of power supply demand of users in the following.(2)Based on the demand of different types of end user is analyzed,as well as to the end user to the power supply reliability,power quality,and economic loss quantitative requirements,building end-user demand model,the research based on large data clustering and grey correlation analysis method,realize the end user attributes blackouts and power supply demand hierarchy.Provide user attribute information for the following outage information analysis.(3)By combining multi-source system information,determine the blackout area and the nature of the blackout.Study forced outage,quantum genetic algorithm based on improved switch features and Hilbert huang transform to solve the electric parameters,by studying the different attributes blackout area user influence on power characteristics,determine the improved weighted weights assignment of Dempster-Shafer evidence theory,thus realize the fusion of multi-source data comprehensive decision-making power outage information,Combined with power supply demand level and power failure attribute of users in power failure area,information support for power failure emergency repair is provided. |
