Research on Automatic Recognition System of Abnormal Behavior of Big Data Technology Distribution Network

At present, the identification of abnormal power consumption behaviors in low- and medium-sized distribution networks has problems such as low efficiency and low accuracy. For this reason, we need to apply big data mining technology to a large amount of electricity consumption data to realize the location of abnormal behavior. Based on this research background, the article proposes an abnormal power consumption recognition model based on the improved K-means algorithm. The model classifies user load curves, extracts characteristic curves and analyzes typical characteristics of their electricity consumption behavior. In this way, the abnormal behavior of electricity consumption in the distribution network is identified. Through experimental analysis, it is found that the optimized K-means clustering algorithm can accurately realize the classification and recognition function of different user types. At the same time, the algorithm can more accurately and effectively analyze the abnormal behavior of users’ electricity consumption.