Invalid Data Cleaning Method of Audible Noise for UHV HVDC Transmission Lines Based on Attention Mechanism and LSTM-LightGBM
During the audible noise test of UHV HVDC transmission lines,the sudden interference of the external environment will make the experimental data doped with more invalid data,which seriously affects the subsequent data analysis.In this paper,a method based on attention mechanism(AM)and long short-term memory network-light gradient boosting machine(LSTM-LightGBM)is proposed to clean the invalid data of the transmission lines with audible noise.Firstly,feature extraction is carried out based on LSTM neural network,aiming at the characteristics of nonlinear and high-dimensional temporal redundancy of audible noise data.At the same time,the feature dimension attention mechanism is introduced,and the weights are allocated adaptively to describe the expressive ability of key feature information.Then,LightGBM is used to classify the extracted features and detect invalid data.Then,the measured audible noise data of an UHV HVDC transmission line is analyzed experimentally.The results show that the detection accuracy rate of this method is 95.55%,the recall rate is 97.73%,and the score of F1 is 0.9663,which are superior to the comparison experimental model.Finally,the invalid data is deleted and filled with the mean interpolation method.After the invalid data is cleaned,the 50%value and 95%value of the data remain basically unchanged.Only the maximum value and 5%value of the invalid data are reduced.This method has certain reference significance for improving the reliability of audible noise data of transmis-sion lines.
万方数据
维普
