A power grid power supply reliability level clustering method and system

Abstract

The invention relates to a power grid power supply reliability level clustering method and system. The method comprises the steps of selecting factors influencing power supply reliability to establishan index set; Determining a final clustering index from the index set by adopting correlation coefficient significance verification; Performing clustering analysis on the final clustering index to obtain a clustering result of power supply reliability level classification; And optimizing the basic data describing the power supply reliability level according to the clustering result. According tothe method, a series of processing is carried out on the index set before clustering, correlation coefficient significance verification is carried out after data correlation coefficient calculation, and auxiliary judgment, namely principal component analysis, is added, so that the dimension is reduced, and the credibility is also improved to a certain extent.

Description

technical field [0001] The invention relates to a clustering method, in particular to a grid power supply reliability level clustering method and system. Background technique [0002] The K-means algorithm is a hard clustering algorithm, which is a representative of a typical prototype-based objective function clustering method. It uses a certain distance from the data point to the prototype as the optimized objective function, and uses the method of finding the extreme value of the function to obtain an iterative operation. adjustment rules. The K-means algorithm uses the Euclidean distance as the similarity measure, which seeks the optimal classification corresponding to an initial cluster center vector V, so that the evaluation index J is the smallest. The algorithm uses the error sum of squares criterion function as the clustering criterion function. [0003] The K-means algorithm belongs to the unsupervised learning method. This algorithm takes k as a parameter and d...

AI Technical Summary

Problems solved by technology

The selection of the initial clustering center has a great influence on the clustering results. Once the initial value is not well selected, effective clustering results may not be obtained, which has become a major problem of the K-means algorithm.

To solve this problem, many algorithms use genetic algorithm (GA). For example, in the literature, genetic algorithm (GA) is used for initialization, and internal clustering criteria are used as evaluation indicators, but the effect is not satisfactory.

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