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

• Main Authors: Zhaoyang Qu, Miao Li, Zhenming Zhang, Mingshi Cui, Yuguang Zhou
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2021-02-01
• Series: Frontiers in Energy Research
• Subjects: transmission line parameters; strong influence feature selection; parameter correction; grey support vector regression; elastic net algorithm
• Online Access: https://www.frontiersin.org/articles/10.3389/fenrg.2021.634207/full
• ISSN: 2296-598X

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.