Research on Processing the Feature Model of Converter Station Based on Machine Learning

Abstract

Abstract The current traditional converter station feature model processing method uses switching functions to model converter station equipment, which leads to poor processing results because it ignores the dynamic coupling characteristics between the second harmonics inside the converter station. In this regard, a machine-learning-based switching station feature model processing method is proposed. By combining different terminals to determine their port parameters, constructing the characteristic impedance model of the converter station, using time-domain recursive convolution to calculate the voltage levels at each key point of the AC system, and finally calculating the magnitude as well as the phase angle constants, the time-varying model of the multi-harmonic converter can be fixed. In the experiments, the computational accuracy of the proposed method is verified. The analysis of the experimental results shows that the proposed method has a high component amplitude and excellent computational performance when the characteristic model of the converter station is processed.