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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Yanpeng Hao & Zhaohong Yao & Junke Wang & Hao Li & Ruihai Li & Lin Yang & Wei Liang, 2019. "A Classification Method for Transmission Line Icing Process Curve Based on Hierarchical K-Means Clustering," Energies, MDPI, vol. 12(24), pages 1-14, December.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:24:p:4786-:d:298350
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    References listed on IDEAS

    as
    1. Yanpeng Hao & Jie Wei & Xiaolan Jiang & Lin Yang & Licheng Li & Junke Wang & Hao Li & Ruihai Li, 2018. "Icing Condition Assessment of In-Service Glass Insulators Based on Graphical Shed Spacing and Graphical Shed Overhang," Energies, MDPI, vol. 11(2), pages 1-12, February.
    2. Lijun Zhang & Kai Liu & Yufeng Wang & Zachary Bosire Omariba, 2018. "Ice Detection Model of Wind Turbine Blades Based on Random Forest Classifier," Energies, MDPI, vol. 11(10), pages 1-15, September.
    3. Sudhakar Gantasala & Narges Tabatabaei & Michel Cervantes & Jan-Olov Aidanpää, 2019. "Numerical Investigation of the Aeroelastic Behavior of a Wind Turbine with Iced Blades," Energies, MDPI, vol. 12(12), pages 1-24, June.
    4. Weijun Wang & Dan Zhao & Liguo Fan & Yulong Jia, 2019. "Study on Icing Prediction of Power Transmission Lines Based on Ensemble Empirical Mode Decomposition and Feature Selection Optimized Extreme Learning Machine," Energies, MDPI, vol. 12(11), pages 1-21, June.
    5. Sudhakar Gantasala & Jean-Claude Luneno & Jan-Olov Aidanpää, 2016. "Influence of Icing on the Modal Behavior of Wind Turbine Blades," Energies, MDPI, vol. 9(11), pages 1-14, October.
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