Abstract
Atmosphere ion mobility is an important electrical parameter related to the filamentary ion flow field of high voltage direct current (HVDC) transmission lines and other characteristics of the glow and streamer corona discharge. Many machine learning (ML)-based algorithms have already been widely used in the prediction of air discharge. This article proposes a genetic algorithm (GA)-support vector regression (SVR) combined with kernel principal components analysis (PCA) to predict the ion mobility, involving dimensionality reduction, feature selection, parameter optimization of SVR. Kernel PCA could reduce the dimension of data and GA with adaptive probability parameters is employed to optimize the parameters of SVR model. An improved parallel-plates ion generator is employed to produce corona discharge and then measure the saturation ion current density and then obtain the training data and testing data of ion mobility. The prediction results show that the proposed algorithm outperforms the other methods in terms of mean relative error and mean squared error criteria. In addition, the parameters of model and data features have a major influence on the performance of the prediction algorithm. Based on the measured data and reference data, the prediction result obtained on ion mobility under different humidity shows a satisfactory generalization and effectiveness of proposed model for the prediction.
Highlights
Corona discharge occurs on the energized conductors of high voltage direct current (HVDC) power transmission lines
It is observed that prediction model has a high accuracy in the ion mobility estimation and effectiveness of genetic algorithm (GA)-support vector regression (SVR) combined with kernel principal components analysis (PCA) and numerical stability
The electric field features and space charge features of coaxial cylinder electrode model are extracted as the input of model and complicated physical process need not be considered
Summary
Corona discharge occurs on the energized conductors of high voltage direct current (HVDC) power transmission lines. The drift tube method was used to measure mobility spectra of atmospheric ions. Parallel plate ion current generator was employed to measure the ion mobility and a calibration method was proposed to calibrate the ion mobility [17,18]. Computational prediction research on the atmospheric ion mobility and the effect of humidity impacts appear sparse in literature. The involved charge carriers were organic macromolecules and their derivatizations, dimeric polymer, hydrates, etc The previous measurements of the ion mobility spectrometry were in the range of 100 °C to 300 °C, which was higher than normal temperature. Through comparing the predictions with the reference data, prediction results obtained on ion mobility show a satisfactory generalization and effectiveness of proposed model for the prediction
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