Electricity larceny prevention analysis method based on Lasso analysis

Abstract

The invention discloses an electricity larceny prevention analysis method based on Lasso analysis. The method comprises the following steps: 1, obtaining all user electric quantity freezing data of awhole transformer area and total meter electric quantity freezing data of the transformer area from an acquisition system; step 2, carrying out data preprocessing on all data, and carrying out interpolation processing on missing data; 3, subtracting the sum of the power consumption data of all the users from the total table data of the transformer area to obtain the line loss value of each time period of the transformer area; 4, according to the Lasso regression model, calculating regression coefficients of the line loss of the transformer area and all the ammeters; 5, calculating the electricity stealing probability of each ammeter according to the line loss of the transformer area and the Lasso coefficient; 6, positioning a suspected electricity stealing user according to the electricitystealing probability. The method is easy to implement, only all user power consumption data and transformer area general table data of the whole transformer area need to be obtained, the influence ofinterference such as the needed data dimension is overcome, manual feature definition is not needed, and excessive additional equipment does not need to be added.

Description

technical field [0001] The invention relates to the field of distribution network automation systems, in particular to an anti-stealing analysis method based on Lasso analysis. Background technique [0002] Electric energy has become a necessity in social production and life. However, losses often occur in the process of power generation, transmission and distribution, especially the increasing phenomenon of power theft brings inestimable economic losses. Consequences of power theft include surges in power supply, overloading of the power system, substantial revenue loss for utilities, and threats to public safety such as fire and electric shock. Therefore, research on effective anti-stealing detection technology has very practical significance for economic and social development. [0003] Currently, data-driven electricity theft detection methods try to find anomalous patterns in the electricity usage information of all users. Usually a large set of labeled samples is req...

AI Technical Summary

Problems solved by technology

However, it is very difficult and time-consuming to establish a complete labeled normal and stealing data set, especially for the latter, the stealing behavior is difficult to effectively verify, making it impossible to accurately judge the collected data labels

Therefore, limited by the advantages and disadvantages of the data set, the application of traditional pattern recognition modeling or cluster analysis methods is also constrained

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