Transformer area line loss data processing method and device based on Canopy-Kmedoids algorithm, and medium

Abstract

The invention provides a transformer area line loss data processing method and device based on a Canopy-Kmedoids algorithm, and a medium, and the method comprises the steps: collecting basic data information of a power distribution network transformer area, wherein the basic data information comprises static parameters of the transformer area, and screening a transformer area sample set with a stable line loss rate based on the static parameters; and analyzing transformer area line loss rate influence factors based on the basic data information, selecting transformer area static parameters with a large influence degree, adopting a Canpoy algorithm to carry out coarse clustering on a data set, taking a central point of a Canpoy subset as an initial central point of a K-medoids algorithm to carry out fine clustering, calculating the sum of squared errors (SSE) of each clustering scheme, making an elbow diagram according to the sum of squares of errors of each clustering scheme, selecting a K value at an inflection point of the elbow diagram as an optimal K value as an optimal result, performing line loss calculation of the transformer area by using the optimal result, and controlling a power grid according to a line loss calculation result. The method improves the clustering effect, and enables the line loss calculation to be accurate and objective.

Description

technical field [0001] The invention relates to the technical field of smart grid data processing, in particular to a method, device and medium for processing line loss data in a station area based on the Canopy-Kmedoids algorithm. Background technique [0002] In the existing technology, at present, power grid enterprises generally adopt a one-size-fits-all approach to the line loss management of the station area. The daily management work is carried out by manually setting the reasonable line loss rate of the station area, without considering the selection of conductors, power supply radius, load distribution, and users in each station area. The differences in categories, load levels, electricity seasons, and operating years lack scientific basis and lack practical reference significance in guiding the implementation of specific energy-saving and consumption-reducing work. In view of the increasing requirements of power grid enterprises for line loss management in the stat...

AI Technical Summary

Problems solved by technology

[0003] However, the clustering division of station areas in the prior art is not accurate enough, and sometimes it is divided based on human subjective factors, and the division results are not objective enough. It is a difficult problem of damage control, and when there are multiple clustering schemes after clustering, it is difficult to choose the best clustering scheme

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