Abstract:
The explosive growth of electricity user data has laid the foundation for data-driven identification of abnormal electricity consumption. However, the ' data barriers ' between different power companies and the low frequency of electricity data collection will affect the performance of such methods. In order to solve the problem that data heterogeneity and low acquisition frequency affect the identification performance in the distributed identification of abnormal power consumption behavior, this paper proposes a federal learning abnormal power consumption identification method based on interpolation optimization. Firstly, linear interpolation and FFT processing are performed on the data set of abnormal electricity consumption to be identified. Secondly, a distributed federated learning and training system based on LSTM mechanism is constructed. Finally, using the constructed training model, the distributed abnormal power consumption behavior identification under the heterogeneous training system is realized. The proposed method is tested on the real data of a large area of a power grid. The experimental results show that the method can realize the data enhancement of the data set, and effectively identify the abnormal electricity consumption behavior of electricity users under the condition that the distributed training and training data are not independent and identically distributed, effectively assist the electricity stealing behavior audit, and improve the operation and maintenance efficiency of the power grid.