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

Yunfa Wu, Bin Zhang, Anbo Meng, Yong-Hua Liu and Chun-Yi Su
Additional contact information
Yunfa Wu: School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Bin Zhang: School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Anbo Meng: School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Yong-Hua Liu: School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Chun-Yi Su: School of Automation, Guangdong University of Technology, Guangzhou 510006, China

Energies, 2022, vol. 15, issue 14, 1-25

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 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/14/5245/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/14/5245/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:14:p:5245-:d:866853

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().