Fault Distance Measurement Method Based on Wavelet Energy Spectrum and BWO Algorithm Optimized CNN-GRU Hybrid Neural Network

Abstract

Aiming at the problems such as noise interference in the current fault ranging methods for flexible DC transmission lines, a single-ended intelligent fault ranging method is proposed as a hybrid neural network based on wavelet energy spectrum and BWO algorithm to optimize the multi-head attention mechanism of CNN combined with gated recurrent unit GRU. First, the correlation between wavelet spectrum energy and fault distance is analyzed, and wavelet packet decomposition is used to extract the wavelet packet energy spectrum feature vector as the model input of the neural network. Second, the hybrid neural network model is constructed and trained to mine the deep fault information in the time series, and the parameters of the hybrid neural network model with added multi-head attention are optimized using the iterative optimization search of the beluga algorithm to achieve fast and accurate positioning of the fault distance. Finally, the network is trained using the constructed four-terminal Zhangbei flexible DC transmission system model to collect data, and the experimental results prove that the method has high distance measurement accuracy, strong anti-interference ability and generalization ability, and a certain degree of resistance to transition resistance.