Abnormal power consumption detection method and system and storage medium

Abstract

The invention discloses an abnormal power consumption detection method and system and a storage medium, and belongs to the technical field of intelligent power grid abnormal power consumption behaviorrecognition. The abnormal power consumption detection method comprises the following steps: collecting data of a to-be-detected intelligent electric meter; converting the intelligent electric meter data into multi-time sequence data; inputting the multi-time sequence data into a trained abnormal power consumption detection model to obtain a predicted power consumption type label; and taking the power consumption type with the maximum probability in the predicted power consumption type label as an abnormal power consumption detection result of the to-be-detected intelligent electric meter data. According to the invention, big power utilization data can be fully and comprehensively utilized, advanced features of abnormal power utilization behaviors are deeply mined, the generalization performance of abnormal power utilization detection is improved, the misjudgment ratio is reduced, and abnormal power utilization is efficiently and accurately detected.

Description

technical field [0001] The invention relates to a method, system and storage medium for detecting abnormal power consumption, and belongs to the technical field of abnormal power consumption behavior identification of smart grids. Background technique [0002] For a long time, abnormal power consumption such as power theft, meter failure and installation errors have brought huge economic losses to grid operators every year. Moreover, due to the huge error in the collected electricity consumption data, it will also affect the dispatching and management of the power grid, as well as the operation safety. Therefore, abnormal power consumption detection is one of the important supports for safe power consumption in the operation and maintenance process of the smart grid, and it is of great significance. Through the screening of whether the electricity consumption behavior is normal or not, it is possible to make up for the under-metered electricity bill caused by abnormal elect...

AI Technical Summary

Problems solved by technology

[0004] 1) The data phenomenon of abnormal power consumption behavior has great diversity, and the data scale of the smart meter is extremely large. When manually designing data characteristics, it is impossible to traverse all possible data phenomena

Artificially designed data features are often based on a small amount of data, resulting in the gradual degradation of the accuracy of the detection model over time;

[0005] 2) The data characteristics of abnormal power consumption are multifaceted, including short-term characteristics and long-term characteristics; both local characteristics and global characteristics; it is difficult to manually design a characteristic model with excellent performance in a short period of time;

[0007] 4) Artificially designed data features often have strong limitations, which are directly related to the technical level and experience of human experts, resulting in strong subjectivity and poor scalability of the feature model, which cannot meet application requirements

[0008] It can be seen that the traditional abnormal power consumption detection method has been unable to meet the operation and maintenance and development needs of the smart grid.

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