High-loss line electricity stealing detection method based on vector autoregression model

Abstract

The invention relates to a high-loss line electricity stealing detection method based on a vector autoregression model. The method utilizes a long-term dynamic interaction relationship between line loss electric quantity of a line and electricity consumption of each subordinate user, and comprises the following steps of: firstly, analyzing a long-term equilibrium relationship between the line loss electric quantity and the electricity consumption of the user by applying a boundary co-integration test; then, constructing a vector autoregression model of the line loss electric quantity and the electricity consumption of each subordinate user, and calculating an impulse response function; and finally, quantitatively analyzing the fluctuation contribution degree between the line loss electric quantity and the user electricity consumption through variance decomposition, and identifying the user who has significant influence on the line loss electric quantity and has the maximum fluctuation contribution degree as an electricity stealing suspected user. According to the method, the user and the line are not required to have the same single order, and a quantitative result of the contribution degree of the user to the line loss can be given.

Description

technical field [0001] The invention belongs to the field of power grid line loss analysis, and relates to a method for analyzing, detecting and locating users with abnormal power consumption by establishing a vector autoregressive model based on abnormal line loss, so as to locate and identify users suspected of stealing electricity (users with abnormal power consumption). Background technique [0002] Under the condition of market economy, some unscrupulous operators use various means to steal electric energy, which directly causes the loss of income of power supply enterprises. Traditionally, the abnormal detection of electricity consumption mainly relies on manual investigation. Due to the lack of data and the lack of directionality in abnormal detection, it often consumes a lot of manpower and material resources but has little effect. At present, my country's power grid enterprises have basically realized the complete collection of electricity consumption information, c...

AI Technical Summary

Problems solved by technology

Although the integrated line loss management system records the detailed power consumption data of all sub-areas of the distribution line, due to the lack of effective data mining and analysis methods, marketing personnel can only check power theft one by one after selecting high-loss lines based on experience For users, it is urgent to study the applicable high-loss line stealing user location recognition algorithm to improve the efficiency of electricity inspection

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