A Classification Method for Transmission Line Icing Process Curve Based on Hierarchical K-Means Clustering

Yanpeng Hao, Zhaohong Yao, Junke Wang, Hao Li, Ruihai Li, Lin Yang and Wei Liang
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Yanpeng Hao: School of Electric Power, South China University of Technology, Guangzhou 510640, China
Zhaohong Yao: School of Electric Power, South China University of Technology, Guangzhou 510640, China
Junke Wang: Electric Power Research Institute, China Southern Power Grid, Guangzhou 510080, China
Hao Li: Electric Power Research Institute, China Southern Power Grid, Guangzhou 510080, China
Ruihai Li: Electric Power Research Institute, China Southern Power Grid, Guangzhou 510080, China
Lin Yang: School of Electric Power, South China University of Technology, Guangzhou 510640, China
Wei Liang: School of Electric Power, South China University of Technology, Guangzhou 510640, China

Energies, 2019, vol. 12, issue 24, 1-14

Abstract: Icing forecasting for transmission lines is of great significance for anti-icing strategies in power grids, but existing prediction models have some disadvantages such as application limitations, weak generalization, and lack of global prediction ability. To overcome these shortcomings, this paper suggests a new conception about a segmental icing prediction model for transmission lines in which the classification of icing process plays a crucial role. In order to obtain the classification, a hierarchical K-means clustering method is utilized and 11 characteristic parameters are proposed. Based on this method, 97 icing processes derived from the Icing Monitoring System in China Southern Power Grid are clustered into six categories according to their curve shape and the abstracted icing evolution curves are drawn based on the clustering centroid. Results show that the processes of ice events are probably different and the icing process can be considered as a combination of several segments and nodes, which reinforce the suggested conception of the segmental icing prediction model. Based on monitoring data and clustering, the obtained types of icing evolution are more comprehensive and specific, and the work lays the foundation for the model construction and contributes to other fields.

Keywords: icing process; classification; icing evolution curves; K-means clustering; centroid; characteristic parameter; icing forecasting (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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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