基于非线性独立成分估计的分布式光伏窃电数据增强方法

Due to the strong concealment of distributed photovoltaic electricity theft and the limitation of inspection resources, the number of electricity theft samples mastered by the power utility is insufficient, which limits the accuracy of electricity theft detection. Therefore, a data augmentation method for distributed photovoltaic electricity theft based on the non-linear independent components estimation (NICE) is proposed. First, the multiple inversible functions are used to map the electricity theft samples into latent variables that obey the Gaussian distribution, and they are reconstructed into new electricity theft samples by inverse transformations. Then, the three typical photovoltaic electricity theft models are proposed, and the convolutional neural network is constructed as a classifier according to the data characteristics of electricity theft samples. Finally, the effectiveness and adaptability of the proposed method are verified by a practical example and a simulation example. Simulation results show that NICE is able to take into account both the shape and distribution characteristics of the samples, and the generated electricity theft samples can significantly improve the performance of different classifiers.