Time-frequency information-based variational mode decomposition and its application in prediction models

Abstract In the field of power systems, the variational mode decomposition (VMD) algorithm is widely utilized in the data preprocessing stage of prediction models for wind power, photovoltaic power, load, and other related areas. However, if the two key parameters of VMD—mode number and penalty factor—are not properly set, the decomposition results may suffer from mode mixing and mode repetition. Moreover, the boundary effect issue in VMD causes numerical oscillations at the boundaries of the decomposition results, thereby leading to numerical distortion. To address these problems, this paper proposes a novel time-frequency information-based variational mode decomposition (TFI-VMD) algorithm. Firstly, TFI-VMD clusters the time-frequency information of the data using an improved density peak clustering algorithm, with the number of cluster centers serving as the mode numbers in VMD. Then, by minimizing the mode mixing energy, the optimal value for the penalty factor is determined. Finally, TFI-VMD uses decomposition results free from boundary effects to train the broad learning neural network, thereby correcting the decomposition data that contain boundary effects. Case study results indicate that the proposed algorithm can effectively identify the parameters in the original VMD and address the boundary effect issues in the decomposition results.