Method for analyzing distribution and transformation daily load typical curves on basis of structure features

Abstract

The invention discloses a method for analyzing distribution and transformation daily load typical curves on the basis of structure features. The method focuses on the peak / valley temporal consistency of similar daily load curves when the distribution and transformation daily load typical curves are acquired. The method includes selecting curves without abnormal data from distribution and transformation historical daily load curve databases and utilizing the curves without the abnormal data as to-be-analyzed objects; utilizing categories with high duty cycles as daily load curve typical categories by the aid of a classification process; solving the distribution and transformation daily load typical curves. The method has the advantages that peak / valley typical periods which are acquired statically are utilized as main bases in daily load curve classification procedures, and accordingly distribution and transformation daily load peak / valley occurrence time can be controlled advantageously.

Description

technical field [0001] The invention relates to a load curve analysis method, in particular to an analysis method for a typical daily load curve of a distribution transformer based on structural features, and belongs to the technical field of power system load analysis. Background technique [0002] Whether it is for the production arrangement of the power production department or the power distribution of the power transmission department, it is necessary to grasp the load change characteristics of a region. The study of the regional daily load curve has always been an important content of the power system research. With the continuous advancement of smart grids, the research on the daily load curves of cities and other large areas can no longer meet the requirements of fine management of demand-side loads. The distribution transformer load curve can better reflect the power consumption characteristics of users in local areas, and with the widespread installation of the dis...

AI Technical Summary

Problems solved by technology

The existing clustering methods can better classify the daily load curves with large differences, but there are also deficiencies, mainly in the following: after classification, there may be differences in the peak / valley value changes of the same type of load curves, which is not good enough. Clustering curves from load peak / valley changes is not conducive to load peak / valley trend analysis

The experience direct selection method mainly selects the typical day directly as the typical load curve based on experience, which requires high experience and is prone to omissions.

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