Residual Current Detection Method Based on Variational Modal Decomposition and Dynamic Fuzzy Neural Network

To further improve the detection ability of residual current in low-voltage distribution networks, an adaptive residual current detection method based on variational mode decomposition (VMD) and dynamic fuzzy neural network (DFNN) is proposed. First, using the general K-value selection method of VMD proposed in this study, the residual current signal is decomposed into K intrinsic mode functions (IMFs). By introducing the cross-correlation coefficient R and the time-domain energy entropy ratio E as two classification indexes, IMFs are divided into three categories: effective IMFs, noise IMFs and aliasing IMFs. Then, the aliasing IMFs are denoised by recursive least squares (RLS), and the denoised IMFs are superimposed with the effective IMFs to obtain the reconstructed signal. Finally, the dynamic fuzzy neural network (DFNN) is adjusted by the minimum output method to achieve the detection of the reconstructed residual current signal, and the network is used to predict the residual current according to the detection results. The detection results of the simulation and measured data show that the proposed algorithm has high detection accuracy and is superior to the wavelet neural network, empirical mode decomposition-thresholding, and wavelet entropy-auto encoder-back propagation neural network methods in terms of mean square error, goodness of fit and running time. This method provides a reference for further research on new adaptive residual current protection devices.

Automated summary

An adaptive residual current detection method based on VMD and DFNN was proposed to improve the detection ability in low-voltage distribution networks. The method achieves high detection accuracy and provides a reference for further research on new adaptive residual current protection devices.