Strategy for the selection of reactive power in an industrial installation using K-means clustering

Abstract

This paper uses K-means clustering as a strategy to determine the minimum, average and maximum reactive power ranges required by an industrial plant for the design of its power factor (PF) solution. The strategy calculates three cluster points in the sensed power data set. The analysis is carried out by comparing the consumption of reactive power against active power during one week of plant operation. This data is used to analyze the behavior of the power factor, and to specify the reactive power for a 6-step mixed compensation bank. The strategy was successfully used to design a power factor corrector in which the specified capacitor bank demonstrated high performance.