LES based power quality data feature extraction and Thompson Tau data cleaning

Abstract

As power system see increasing application of big data and artificial intelligence, the new generation of power quality data requires cross-platform interaction, larger database support volume, and higher data processing speed, which makes traditional power quality data collection and processing methods difficult to adapt to the power grid development. This paper proposes a power quality Thompson tau algorithm data cleaning strategy based on LES feature extraction, first extract LES feature value of power quality data and cluster partition of the eigenvalues, and then, according to the power quality characteristics of the data partition, divide the sub-interval step flexibly for Thompson tau data cleaning and multipoint interpolation data filling. Finally, power quality data from a practical power grid is taken as an example and the simulation results verify the feasibility and accuracy of the proposed method.