Power theft identification method based on MTF dimension promotion and residual network
To address the problem of electricity theft detection in smart grids,this paper proposes a method based on Markov transfer field for one-dimensional to two-dimensional image conversion.By analyzing daily electricity us-age of clients,it is found that normal clients have seasonal fluctuation and holiday correlation characteristics,while electricity theft clients show disordered states.The proposed method mines electricity usage features from multiple time scales,and then uses convolutional neural networks with residual modules for electricity theft client identifica-tion.Experiments are conducted on a dataset provided by the State Grid Corporation of China,and the proposed method achieves an accuracy of 94.31%,which verifies its effectiveness and feasibility.
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