Multi-scale representation and identification of High Voltage Direct Current interference events in time-varying geomagnetic observation data
Efficient and accurate identification of geomagnetic waveform disturbances caused by High Voltage Direct Current(HVDC)transmission is crucial to improve the quality of time-varying geomagnetic observation data.However,identifying these disturbances is challenging due to their varying duration and degree of disturbance.The present study introduces a novel approach for the automatic detection of HVDC transmission disturbance events of varying durations.A multi-scale representation and identification method is proposed,incorporating wavelet technology with multi-scale properties and convolutional neural network(CNN)known for their ability to extract features automatically.By integrating these techniques,a multi-input CNN model is devised to effectively identify HVDC transmission disturbance events in time-varying geomagnetic observation data.Firstly,discrete wavelet technique is used to decompose multi-scale geomagnetic samples into a multi-scale representation.Next,these decomposed samples are input into a convolutional neural network with multiple input branches,where each branch automatically extracts features at different scales.The features are then combined and the attention mechanism is added to calculate the weights of each scale feature adaptively.Then the fully connected layer and SoftMax layer are used for classification,and the proposed model is named CBAM-MCNN.This paper conducted experiments on HVDC transmission disturbance samples provided by the China Earthquake Precursor Network Center.The accuracy of the CBAM-MCNN model reached 97.14％on 5271 test samples,which is much higher than the existing Full Convolutional Neural Networks,Residual Network,Multi-input Convolutional Neural Network,and IICM-HVDCT-CNN-LSTM models.
万方数据
维普
