The diverse connection methods and flexible operation modes of distributed photovoltaic systems make it difficult to detect electricity theft, effective electricity theft detection methods are crucial for curbing electricity theft.
In response to the problems of poor model generalization ability and low detection accuracy in supervised classification and unsupervised regression methods for electricity theft detection, this paper proposes a hybrid semi supervised model combining convolutional neural networks and Transformers for electricity theft detection based on the analysis of the principle of photovoltaic electricity theft. The convolutional neural network model integrates bidirectional long short-term memory networks and attention mechanisms, and uses the whale algorithm with Tent chaotic mapping, nonlinear convergence factor, average position vector, and adaptive inertia weight optimization to optimize hyperparameters. The Transformer model is optimized by combining multi-scale perception layers and linear mapping layers. Verify the superiority of the proposed model through experiments.The experimental results indicate that, proposed semi supervised learning photovoltaic electricity theft detection model can accurately determine whether distributed photovoltaic users have electricity theft behavior with a small amount of labeled data and a large amount of unlabeled data. It has high detection accuracy and area under the curve, with an accuracy of 96.67% and an area under the curve of 99.05%. Compared to conventional methods, the accuracy of the proposed method has increased by 4.98% and 0.44%, respectively, and the area under the curve has increased by 3.12% and 0.74%, respectively. The research provides new ideas for anti electricity theft technology in the new power system, which helps promote the sustainable development of clean energy.