Abstract
To address the limitation of harmonic monitoring on the low-voltage side of distribution networks, this paper proposes a multi-source harmonic estimation method based on variational mode decomposition. The method integrates short-term test data with long-term power data. First, dominant harmonic users are identified through a strategy that combines Fisher optimal segmentation and derivative dynamic time warping. Second, an electrical data transformation approach is designed by combining variational mode decomposition with Gramian angular fields, which maps the power signals of dominant harmonic users and low-voltage side harmonic signals into pseudo-color Gramian power images and grayscale Gramian harmonic images, respectively. Finally, an improved PSRGAN (pix2pix-super-resolution generative adversarial network) model is constructed to train and learn from these images, establishing the mapping relationship between power data and low-voltage side harmonic data of the distribution network, thereby enabling the migration and generation of long-term low-voltage side harmonic monitoring data. Simulation cases and field measurements validate the effectiveness and accuracy of the proposed method in multi-source harmonic scenarios. Moreover, the required data are easily accessible, demonstrating strong potential for engineering applications.