A detection method for electricity theft by distribution network users based on a hybrid neural network

Abstract

Given the low accuracy of the traditional electricity theft detection method based on one-dimensional electricity consumption data mining and analysis， a detection method for electricity theft by distribution network users based on a hybrid neural network is proposed. Firstly， to enhance the characteristic difference between the electricity consumption of normal users and that of power theft users， the Markov transition field （MTF） is used to transform one-dimensional electricity consumption data into two-dimensional graphs. Moreover， to improve the accuracy and generalization of the model， profile data of users' electricity consumption is introduced. Then， the hybrid neural network is used to extract and fuse the feature quantities of the preprocessed two-dimensional electricity consumption graphs and profile data respectively to detect electricity theft by distribution network users. Finally， the effectiveness and accuracy of the proposed method are verified through two sets of comparison experiments. The experimental results show that the method based on a hybrid neural network is superior to other models in detection accuracy of electricity theft， recall rate， and AUROC （area under the receiver operating characteristics）， and has higher detection performance.

Keywords

• distribution network
• electricity theft detection
• mtf
• hybrid neural network