A Parameter Optimized Variational Mode Decomposition Method for Harmonic and Inter-harmonic Detection

An variational mode decomposition (VMD) has been applied in the field of harmonic detection, but the error will be large if the decomposition parameters are set artificially. To improve the accuracy of VMD in inter-harmonics detection, we need to determine the number of wolves, maximum number of iterations, convergence factor and other parameters, and then select component sample entropy function as the fitness function of the Grey Wolf algorithm. The variational mode decomposition can be utilized to extract the harmonic signal and choose a minimum envelope entropy weight as the best component. The Fourier transform is used to obtain the amplitude and frequency information of interharmonic signals. The simulation results show that the proposed method can effectively optimize the parameters and reduce the VMD decomposition error. Compared with empirical mode decomposition (EMD), complementary ensemble empirical mode decomposition (CEEMD) and empirical wavelet transform (EWT), the VMD with optimized parameters can significantly improve the accuracy of interharmonic detection and improve the accurate trace of accident source.