Invalid Data Rejection of Audible Noise on AC Transmission Lines Based on Moving Window Kernel Principal Component Analysis

The statistical characteristics of the nighttime noise data of 1000kV AC transmission lines were investigated, and the noise data of the Huainan-Shanghai 1000kV AC transmission line collected at night (0:00 to 6:00) from September 25, 2015 to February 16, 2016 were statistically analyzed using the nonparametric statistical K-S test, the outliers were detected using the moving window kernel principal component analysis (MWKPCA). The results show that after the ineffective data are removed by MWKPCA, the 5%, 50% and 95% values of the data are basically unchanged. To a certain extent, the method proposed in this paper can remove the invalid audible noise data of 1000kV AC transmission lines without affecting the subsequent study of audible noise, and we use various machine learning algorithms to predict the A weight sound level(Awsl) before and after the invalid data rejection, and the results show that the invalid data rejection has contributed to the improvement of the transmission line audible noise Awsl prediction accuracy.