Dynamic Optimization Method of Transmission Line Parameters Based on Grey Support Vector Regression

Aiming at the problem of insufficient accuracy and timeliness of transmission line parameters in the grid energy management system (EMS) parameter library, a dynamic optimization method of transmission line parameters based on grey support vector regression is proposed. Firstly, the influence of operating conditions and meteorological factors on the changes of parameters is analyzed. Based on this, the correlation quantification method of transmission line parameters is designed based on Pearson coefficient, and the influence coefficient value is obtained. Then, with the influence coefficient as the constraint condition, a method for selecting strong influence characteristics of line parameters based on improved Elastic Net is proposed. Finally, based on the grey prediction theory, a grey support vector regression (GM-SVR) parameter optimization model is constructed to realize the dynamic optimization of line parameter values under the power grid operation state. The effectiveness and feasibility of the proposed method is verified through the commissioning of the reactance parameters of the actual local loop network transmission line.

Automated summary

A dynamic optimization method of transmission line parameters based on grey support vector regression was proposed in this study to address the issue of insufficient accuracy and timeliness in the grid energy management system parameter library. The method analyzes the impact of operating conditions and meteorological factors on parameter changes, designs a correlation quantification method based on Pearson coefficient, and proposes a method for selecting strong influence characteristics of line parameters based on improved Elastic Net. The effectiveness and feasibility of the method was verified through the commissioning of reactance parameters of an actual local loop network transmission line.