Power grid abnormal power consumption detection method based on attention mechanism and residual network

Abstract

The invention discloses a power grid abnormal electricity utilization detection method based on an attention mechanism and a residual network, and the method comprises the following steps: S1, data collection: carrying out the high-frequency collection of massive electricity utilization information of a user through a large number of intelligent ammeters, and collecting a large number of power data; s2, preprocessing data; S3, selecting a training data set and a test data set; s4, setting initial parameters of the model; s5, training a classification algorithm based on the attention mechanism and the residual network, and performing model training on the classification algorithm based on the attention mechanism and the residual network by using the training sample; s6, predicting power consumption data; and S7, analyzing a result. According to the method, abnormal power utilization detection of the power grid is achieved with high precision, in practical application, the algorithm capacity is quite stable, and the method has good processing capacity for complex environments and data.

Description

technical field [0001] The invention relates to the technical field of electricity consumption detection, in particular to a power grid abnormal electricity consumption detection method based on an attention mechanism and a residual network. Background technique [0002] With the rapid development of new-generation information technologies such as the Internet of Things and big data, various industries are undergoing major changes. In recent years, the country has attached great importance to the sustainable development of energy, which has led to a sharp increase in the demand for electric energy in the industrial production of the society and the daily life of users. However, the traditional power network structure is relatively simple and can only meet the basic electricity demand of some users. Therefore, countries are currently committed to the development of the next-generation power system capable of distributed energy and other new energy sources, mainly by integrat...

AI Technical Summary

Problems solved by technology

The proposal of the residual network is mainly used to solve the degradation problem in deep learning, that is, as the number of layers of the deep neural network increases, the performance of the model decreases instead.

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