A harmonic detection algorithm based on improved wavelet threshold function and variational mode decomposition

Variational Mode Decomposition (VMD) has been widely used for harmonic detection. However, it is sensitive to noise, requires prior knowledge of the number of decomposition modes K, and suffers from end point effects. To address noise interference, an improved wavelet threshold function is applied to denoise the original signal. For the predetermined K requirement, the property of minimal inter-mode correlation during optimal VMD decomposition is utilized to achieve adaptive K selection. Regarding end point effects, a fixed-window-length waveform matching extension method is implemented to extend the signal, which effectively suppresses end point effects. Upon completion of optimal signal decomposition, both the frequency and amplitude of each harmonic component can be precisely extracted using the Hilbert transform. Simulation results demonstrate that the proposed algorithm achieves three key improvements: effective noise reduction, adaptive selection of the optimal number of decomposition modes K, and successful suppression of end point effects. These characteristics show that the algorithm has high application value in harmonic detection scenarios.