Monitoring and locating method based on abnormal electricity consumption detection module

Abstract

The invention relates to a monitoring and locating method based on an abnormal electricity consumption detection module of the deep noise reduction self-coding network-Gaussian process. Electricity consumption and meter event information of all the detected users in a transformer area is inputted to the abnormal electricity consumption detection module of the deep noise reduction self-coding network-Gaussian process, the features of the data are extracted from a time-frequency domain and classified, and the suspected abnormal electricity consumption users of the detected users are selected through screening by the model. The abnormal electricity consumption detection module outputs the suspected abnormal degree coefficient and orders the probability of the suspected abnormal degree of the users so as to obtain a suspected abnormal electricity consumption user list. The multiplatform electricity consumption data are analyzed by combining the artificial intelligence field leading-edge technology, the hidden user electricity consumption behavior mode in the mass data is deeply mined and the suspected abnormal electricity consumption users are located so that abnormal electricity consumption detection is enabled to be more intelligent and more efficient.

Description

technical field [0001] The invention relates to a monitoring and positioning method of an abnormal power consumption detection model based on a deep noise reduction self-encoding network-Gaussian process. Background technique [0002] Abnormal power consumption (electricity theft, abnormal metering device) is an important factor causing abnormal line loss, which will bring hidden dangers to power consumption and grid security. But at present, there are the following problems in the detection of abnormal power consumption: difficult to find, difficult to obtain evidence, and difficult to measure abnormal power consumption. Especially in the low-voltage station area, there are many and scattered users, and it is difficult to detect effectively, which brings difficulties to the management of low-voltage line loss and damages the interests of power supply companies. [0003] Specifically, under the background of the integration of operation, distribution and deployment, the deg...

AI Technical Summary

Problems solved by technology

The result is: the loss of the power supply company cannot be recovered quickly and in full; the power stealer has not received due sanctions

What's more serious is that the stealing of electricity is becoming more and more serious, which has a serious impact on the order of electricity use in the whole society.

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