Research on K-means Clustering of Distribution Network Voltage Nesting Based on Pearson Correlation Coefficient Method

With the development of economy and social progress, industrial production and people’s lives more need higher quality of electric energy, Power distribution area as a user-oriented end link, voltage quality is an important sign to measure power quality, this paper by combining Pearson correlation coefficient and nested K-means algorithm, the voltage data of Shanghai urban distribution transformer in 2022 cluster analysis, first use Newtonian interpolation method to fill in the missing values, and then eliminate abnormal data, and then algorithm to cluster the data, Therefore, the distribution voltage quality curve with different limit crossing conditions is obtained. The results show that the method used in this paper has a good effect on the voltage classification portrait in the Power distribution area, and can clearly display the voltage overrun and fluctuation trend of various distribution changes in the launch area.