Fault-assisted Decision-making Method for Flexible DC Converter Valve Based on CNN-KELM with Improved DBO Optimization
In order to improve the correct rate of flexible DC converter valve fault classification,a flexible DC converter valve fault classification method based on the convolutional neural network fused kernel-extreme learning machine(CNN-KELM)optimized by the Improved dung beetle optimization algorithm(IDBO)is proposed.The flexible DC converter valve fault feature library is normalized,the CNN network is used for fault feature extraction,and IDBO is used to optimize the kernel parameters and penalty factors of KELM.IDBO-CNN-KELM is used as a classifier to classify the extracted flexible DC converter valve fault library.Through experiments,the IDBO-CNN-KELM model achieves a classification correctness of 97.727％in the fault test set,which improves 1.136％and 0.577％compared with the traditional KELM and PSO-CNN-KELM,proving the accuracy of the IDBO-CNN-KELM model.The method effectively improves the accuracy and efficiency of fault classification of flexible DC converter valves,and enhances the reliability of DC transmission in power grids.
flexible DC converter valvedung beetle optimisation algorithmconvolutional neural networknuclear limit learning machinefault-assisted decision making
万方数据
维普
