Study on Adaptive Fuzzy Control System Based on Gradient Descent Learning Algorithm

Residual current detection method based on improved VMD-BPNN

“…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. For example, X. Xiao et al proposed the adaptive filtering detection method [4], which achieves adaptive current detection by noise reduction and residual current separation of the measured current, but the N-LMS model has the disadvantages of too slow convergence and low detection accuracy; H. Yan et al proposed the EMD detection method [5], which uses the characteristics of the time-frequency domain change of residual current as the basis for determining the occurrence of electrocution faults, which is better improved accuracy, but the detection is prone to problems such as modal mixing and endpoint effects; C. Li et al performed the detection of residual current by combining VMD with LSTM [6], which has certain advantages due to LSTM in sequence modelling problems with long-time memory. Therefore, it can accurately handle large sample data and improve detection accuracy.…”