Statistical and classifying method for loads of large regional power grid

Abstract

The invention discloses a statistical and classifying method for loads of a large regional power grid and belongs to the field of regional power grid load modeling. According to the technical scheme, the method comprises the following steps: firstly, performing first-layer clustering calculation on a transformer substation group on the basis of a bubble sorting method, thereby realizing the rough classifying of the transformer substation group, and then, performing second-layer clustering calculation on each class of transformer substation which is roughly classified via a kernal-based layer-by-layer clustering method, thereby realizing the fine classifying on the basis of the rough classifying. According to the method, on the basis of the load structure of the transformer substation, a kernal-based layer-by-layer clustering technique is utilized to naturally classify according to sample characteristics, so that the transformer substation group which cannot be linearly classified can be linearly classified in a higher space through characteristic mapping. Meanwhile, an application of a kernel function is combined for effectively preventing the curse of dimensionality of the higher space, the problem of difficulty in transformer substation classifying is accurately solved, and the effective statistics and classification for the loads of the large regional power grid are realized.

Description

technical field [0001] The invention belongs to the field of regional power grid load modeling, and in particular relates to a large regional power grid load statistics and classification method. Background technique [0002] Large-area power grid load modeling is one of the unresolved technical problems in the domestic and foreign electric power field. The main reason is that the large-area power grid load has characteristics such as distribution, complexity, time-varying, and randomness. Statistical synthesis method and overall test-and-determination method are the two main methods for building load models. Since the statistical synthesis method requires detailed investigation of a large number of power load components and distribution network structural parameters, not only the workload is large but also it is difficult to obtain accurate results, which affects the accuracy of the established load model; The measurement device collects the dynamic characteristic data of ...

AI Technical Summary

Problems solved by technology

[0004] The traditional linear learner mentioned in the background technology is difficult to apply to the division of high-dimensional large sample set data, resulting in rough classification and high-dimensional space "dimension disaster" problems; at the same time, when dealing with a large number of high-dimensional sample data When assembling, FCM and transitive closure method can not get good clustering results, easily fall into local optimum and cannot handle bad data. The present invention proposes a large-area power grid load statistics and classification method

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