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

Abstract

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 decompositionthresholding, 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.

Enhancing Low-Voltage Distribution Network Safety through Advanced Residual Current Protection Techniques

“…Referring to the method of differential protection setting [3][4][5][6][7][8], Equation ( 6) gives a distributed residual current protection criterion.…”

“…By isolating the electric shock current from the residual current and making judgments based on the electric shock current, this method successfully eliminates the protection dead zone. Current separation stands out as a primary research approach for electric shock protection [8][9][10][11]. During leakage faults in electrical equipment, the residual current exhibits non-sinusoidal characteristics, impacting the performance of Residual Current Devices (RCDs) [12,13].…”

Residual current detection method based on improved VMD-BPNN

“…while the size of α will affect the bandwidth size of the components and affect the balance of the VMD process. Literature [ 1 , 10 ] proposed the instantaneous power averaging method and the interrelationship number judgment method to determine the k-value, respectively, However, all of the above methods have the disadvantage of being computationally intensive and are not suitable for time-sensitive signal processing processes. In recent years, metaheuristic optimisation algorithms have attracted much attention by virtue of their ability to quickly and accurately solve optimisation problems with multivariate functions [ 17 , 18 ], such as the beetle antennae search approach [ 19 ], Hybrid Moth Flame Optimization [ 20 ], etc.…”

“…However, the presence of numerous interfering signals mixed with the residual current during grid faults can significantly reduce the detection accuracy of RCD, leading to frequent instances of false tripping and non-tripping. [ 1 ]. In recent years, with the rapid development of modern signal processing technology, many domestic and foreign scholars have proposed a variety of methods to improve the accuracy and speed of residual current detection, such as wavelet analysis, Empirical Mode Decomposition (EMD), neural networks [ 2 , 3 ] and other algorithms have been applied to residual current detection, combining these emerging technologies with RCD to further improve the reliability of RCD.…”

Adaptive Leakage Protection for Low-Voltage Distribution Systems Based on SSA-BP Neural Network

“…The standard considers the influence of humidity on leakage current, but not the temperature, load current, and line aging. In addition, with the development of modern signal processing techniques, some related techniques, such as variable mode decomposition (VMD), big data analysis, neural network algorithms, and machine learning approaches [17][18][19][20][21], have been applied to the detection of residual current. In [17], an adaptive residual current detection method based on VMD and a dynamic fuzzy neural network (DFNN) was proposed.…”