Electricity consumption anomaly detection model with constraint conditions

Abstract

The invention discloses an electricity consumption anomaly detection model with constraint conditions. The constraint conditions are given by a line loss anomaly constraint model, and the line loss anomaly constraint model comprises judgment on a non-special line day line loss anomaly and judgment on a special line day line loss anomaly. The electricity consumption anomaly detection model comprises an electricity anomaly model, a power factor anomaly model, an AC voltage anomaly model and an AC anomaly model. The electricity anomaly model comprises judgment on an electricity deviation degree,calculation of the electricity at a load control terminal and electricity metering by a watt-hour meter. The electricity consumption anomaly detection model with line loss anomalies as constraint conditions has the advantages of little misjudgment, high accuracy, less demand for samples and the like.

Description

technical field [0001] The invention relates to an abnormal electricity consumption detection model, in particular to an abnormal electricity consumption detection model with constraint conditions. Background technique [0002] The electric power industry is the basic industry of the national economy, and it is a necessary condition to ensure the development of electric power to ensure that the power grid enterprises recover the electricity charges in time. However, due to various reasons, electricity theft still exists widely, and it is even rampant in some areas, causing huge losses to power supply companies. According to incomplete statistics, my country's annual electricity theft loss reaches 20 billion yuan. [0003] Big data technology has gradually been widely used in various industries. Big data refers to the data that cannot be perceived, acquired, managed, processed and analyzed by traditional IT technology, software and hardware tools and mathematical analysis me...

AI Technical Summary

Problems solved by technology

[0005] There are various manifestations of electricity theft behavior, but the traditional detection methods for abnormal electricity consumption for various specific ways of stealing electricity are relatively single-method applications, resulting in the collection of many abnormal electricity usage information applications that are not related to each other, so It will lead to a higher rate of false alarms and false negatives, resulting in a lower overall accuracy of abnormal power consumption detection

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