In modern power grids, the detection of electricity theft is crucial for ensuring the safety and stability of the power system and reducing revenue losses. Current electricity theft detection methods do not take into account the spectral space features contained in the original time series data sequences, and thus are unable to adapt to the complex and ever-changing scenarios of electricity theft. This paper proposes an electricity theft detection model TSFPTD that integrates time series signals and their synchronous spectral features. The multi-modal model constructs the synchronous spectral modal space corresponding to the time series data through a deep wavelet network. It is found that this newly generated synchronous modal space contains implicit features that cannot be revealed by the original time series data. The explicit features of the time series data space and the implicit features of the synchronous spectral modal space are fused and aligned for the detection of power theft behavior. The performance verification experiment of the model was completed on the real dataset released by State Grid Corporation of China. The electricity theft detection accuracy of the TSFPTD model reached over 96.83%, and its performance is superior to the existing electricity theft detection methods.
Gao et al. (Tue,) studied this question.