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Improved Neural Network Algorithm Based Flow Characteristic Curve Fitting for Hydraulic Turbines

Author

Listed:
  • Hong Pan

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Chenyang Hang

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Fang Feng

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Yuan Zheng

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
    College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210003, China)

  • Fang Li

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

Abstract

One of the most important characteristic curves in the economic operation of hydropower plants is the turbine flow characteristic curve, which illustrates the law of fluctuation between the characteristic parameters of the turbine under various operating situations. This article proposes an IPSO-LSTM-based refinement method for fitting the turbine flow characteristic curve using deep learning methods, and verifies the effectiveness of the method by comparison to solve the problem that traditional mathematical fitting methods are difficult to meet the requirements of today’s many complex working conditions. Firstly, a deep LSTM network model is established based on the input and output quantities, and then the IPSO method is used to find the optimum number of neurons, the learning rate and the maximum number of iterations of the LSTM units in the network model and other key parameters to determine the relevant training parameters. The results show that the model can effectively improve the accuracy of fitting and predicting the turbine flow characteristics, which is of great significance to the study of the economic operation of hydropower plants and the non-linear characteristics of the turbine.

Suggested Citation

  • Hong Pan & Chenyang Hang & Fang Feng & Yuan Zheng & Fang Li, 2022. "Improved Neural Network Algorithm Based Flow Characteristic Curve Fitting for Hydraulic Turbines," Sustainability, MDPI, vol. 14(17), pages 1-15, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:17:p:10757-:d:900775
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    References listed on IDEAS

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    4. Zhe Yang & Kan Yang & Lyuwen Su & Hu Hu, 2020. "The Short-Term Economical Operation Problem for Hydropower Station Using Chaotic Normal Cloud Model Based Discrete Shuffled Frog Leaping Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(3), pages 905-927, February.
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