<p>This paper investigates a multi-source data fusion method for smart substations based on digital twin technology to eliminate the disparity and heterogeneity of multi-source data and enhance operational maintenance capabilities. Sensors at the perception layer collect multi-source data from the smart substation and transmit it to the platform layer. The virtual-physical mapping module at the platform layer employs edge-folding technology to achieve lightweight processing of the smart substation digital twin model, reducing the number of mesh faces while preserving key geometric features. The multi-source data fusion module performs consistency checks using the Wilcoxon rank-sum test to determine data fusibility. The validated data is then fused using the Joint Kalman Filter algorithm. The fusion results are transmitted to the application layer, where they are converted into various operational applications and update the digital twin model of the substation. Experimental results demonstrate that the digital twin model accurately reproduces the physical layout and equipment composition, with the information entropy of the multi-source data fusion consistently exceeding 0.85. The proposed method was benchmarked against particle filtering, Extended Kalman Filtering (EKF), and Unscented Kalman Filtering (UKF), showing a 5% improvement in fusion accuracy (entropy), achieving 0.85 compared to 0.82 for particle filtering and 0.83 for EKF, indicating enhanced accuracy and computational efficiency.</p>