A Hybrid Framework Combining Data-Driven and Catenary-Based Methods for Wide-Area Powerline Sag Estimation

Abstract

This paper is concerned with the airborne-laser-data-based sag estimation for wide-area transmission lines. A systematic data processing framework is established for multi-source data collected from power lines, which is applicable to various operating conditions. Subsequently, a k-means-based clustering approach is employed to handle the spatial heterogeneity and sparsity of powerline corridor data after comprehensive performance comparisons. Furthermore, a hybrid model of the catenary and XGBoost (HMCX) method is proposed for sag estimation, which improves the accuracy of sag estimation by integrating the adaptability of catenary and the sparsity awareness of XGBoost. Finally, the effectiveness of HMCX is verified by using power data from 116 actual lines.

Keywords

• sag estimation
• hybrid model
• ensemble learning
• spatial data