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Graph neural network based hydraulic turbine data stream prediction
[Variational mode decomposition]

Author

Listed:
  • Ning Li
  • Jing Ren
  • Xin Zhou
  • Jun Li
  • Chen Xue

Abstract

As a kind of green energy with mature technology, hydropower energy is more and more widely used in our real life. As the core equipment of a hydropower station, hydraulic turbine units will experience varying degrees of vibration and aging during the process of power generation. Due to the complex internal structure and the interaction between different components, the analysis and prediction of the relevant operating data of the water turbine unit has important application value. This paper proposes a graph neural network framework for multivariate data stream prediction. In this method, a graph learning module is designed to automatically extract the one-way relationship between different components of the turbine unit. In addition, the mix-hop propagation layer and expansion layer are designed to capture the spatial and temporal correlations in hydraulic turbine data stream. Experiments show that the proposed method has higher accuracy comparing with the existing methods.

Suggested Citation

  • Ning Li & Jing Ren & Xin Zhou & Jun Li & Chen Xue, 2022. "Graph neural network based hydraulic turbine data stream prediction [Variational mode decomposition]," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 17, pages 140-146.
    • Handle: RePEc:oup:ijlctc:v:17:y:2022:i::p:140-146.
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    File URL: http://hdl.handle.net/10.1093/ijlct/ctab082
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    References listed on IDEAS

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    1. Ali Seyed Shirkhorshidi & Saeed Aghabozorgi & Teh Ying Wah, 2015. "A Comparison Study on Similarity and Dissimilarity Measures in Clustering Continuous Data," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-20, December.
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