Prediction of Kaplan turbine coordination tests based on least squares support vector machine with an improved grey wolf optimization algorithm

Journal title

Bulletin of the Polish Academy of Sciences: Technical Sciences








Kong, Fannie : School of Electrical Engineering, Guangxi University, Nanning, 530000, China ; Xia, Jiahui : School of Electrical Engineering, Guangxi University, Nanning, 530000, China ; Yang, Daliang : School of Electrical Engineering, Guangxi University, Nanning, 530000, China ; Luo, Ming : School of Electrical Engineering, Guangxi University, Nanning, 530000, China



Kaplan turbine ; coordination tests ; least squares support vector machine ; improved grey wolf optimization

Divisions of PAS

Nauki Techniczne




  1.  H.A. Menarin, H.A. Costa, G.L.M. Fredo, R.P. Gosmann, E.C. Finardi, and L.A. Weiss, “Dynamic Modeling of Kaplan Turbines Including Flow Rate and Efficiency Static Characteristics”, IEEE Trans. Power Syst. 34(4), 3026‒3034 (2019).
  2.  M.M. Shamsuddeen, J. Park, Y. Choi, and J. Kim, “Unsteady multi-phase cavitation analysis on the effect of anti-cavity fin installed on a Kaplan turbine runner”, Renew. Energy 162, 861‒876 (2020).
  3.  P. Pennacchi, P. Borghesani, and S. Chatterton, “A cyclostationary multi-domain analysis of fluid instability in Kaplan turbines”, Mech. Syst. Signal Process. 60‒61, 375‒390 (2015).
  4.  A. Javadi and H. Nilsson, “Detailed numerical investigation of a Kaplan turbine with rotor-stator interaction using turbulence-resolving simulations”, Int. J. Heat Fluid Flow 63, 1‒13 (2017).
  5.  D. Kranjcic and G. Štumberger, “Differential Evolution-Based Identification of the Nonlinear Kaplan Turbine Model”, IEEE Trans. Energy Convert. 29(1), 178‒187 (2014).
  6.  Z. Krzemianowski, “Engineering design of low-head Kaplan hydraulic turbine blades using the inverse problem method”, Bull. Pol. Acad. Sci. tech. Sci. 67(6), 1133–1147 (2019).
  7.  A.B. Janjua, M.S. Khalil, M. Saeed, F.S. Butt, and A.W. Badar, “Static and dynamic computational analysis of Kaplan turbine runner by varying blade profile”, Energy Sustain. Dev. 58, 90‒99 (2020).
  8.  Y. Wu, S. Liu, H. Dou, S. Wu, and T. Chen, “Numerical prediction and similarity study of pressure fluctuation in a prototype Kaplan turbine and the model turbine”, Comput. Fluids 56, 128‒142 (2012).
  9. S.J. Daniels, A.A.M. Rahat, G.R. Tabor, J.E. Fieldsend, and R.M. Everson, “Shape optimisation of the sharp-heeled Kaplan draft tube: Performance evaluation using Computational Fluid Dynamics”, Renew. Energy. 160, 112‒126 (2020).
  10.  F. Thiery, R. Gustavsson, and J.O. Aidanpää, “Dynamics of a misaligned Kaplan turbine with blade-to-stator contacts”, Int. J. Mech. Sci. 99, 251‒261 (2015).
  11.  H. Quan, D. Srinivasan, and A. Khosravi, “Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals”, IEEE Trans. Neural Netw. Learn. Syst. 25(2), 303‒315 (2014).
  12.  V. Marano, G. Rizzo, and F.A. Tiano, “Application of dynamic programming to the optimal management of a hybrid power plant with wind turbines, photovoltaic panels and compressed air energy storage”, Appl. Energy. 97, 849‒859 (2012).
  13.  N. Yang and H.Chen, “Decomposed Newton algorithm-based three-phase power-flow for unbalanced radial distribution networks with distributed energy resources and electric vehicle demands”, Int. J. Electr. Power Energy Syst. 96, 473‒483 (2018).
  14.  J. Park and K.H. Law, “Layout optimization for maximizing wind farm power production using sequential convex programming”, Appl. Energy. 151, 320‒334 (2015).
  15.  T. Ding, R. Bo, F. Li, Y. Gu, Q. Guo, and H. Sun, “Exact Penalty Function Based Constraint Relaxation Method for Optimal Power Flow Considering Wind Generation Uncertainty”, IEEE Trans. Power Syst. 30(3), 1546‒1547 (2015).
  16.  H. Kebriaei, B.N. Araabi, and A. Rahimi-Kian, “Short-Term Load Forecasting With a New Nonsymmetric Penalty Function”, IEEE IEEE Trans. Power Syst. 26 (4), 1817‒1825 (2011).
  17.  A.T. Eseye, J. Zhang, and D. Zheng, “ Short-term photovoltaic solar power forecasting using a hybrid Wavelet-PSO-SVM model based on SCADA and Meteorological information”, Renew. Energy. 118, 357‒367 (2018).
  18.  Y. Li and X. Wnag, “Improved dolphin swarm optimization algorithm based on information entropy”, Bull. Pol. Acad. Sci. Tech. Sci. 67(4), 679–685 (2019).
  19.  H. Koyuncu and R. Ceylan, “A PSO based approach: Scout particle swarm algorithm for continuous global optimization problems”, J. Comput. Des. Eng. 6, 129‒142 (2019).
  20.  H. Liu, H.P. Wu, Y.F. Li, “Smart wind speed forecasting using EWT decomposition, GWO evolutionary optimization, RELM learning and IEWT reconstruction”, Energy Conv. Manag. 161, 266‒283 (2018).
  21.  M. Gratza, R. Witzmann, Ch.J. Steinhart, M. Finkel, M. Becker, T. Nagel, T. Wopperer, and H. Wackerl, “Frequency Stability in Island Networks: Development of Kaplan Turbine Model and Control of Dynamics”, in 2018 Power Systems Computation Conference (PSCC), Dublin, Ireland, 2018, pp. 1‒7, doi: 10.23919/PSCC.2018.8442445.
  22.  M. Malvoni, M.G. D. Giorgi, and P.M. Congedo, “Photovoltaic forecast based on hybrid PCA–LSSVM using dimensionality reducted data”, Neurocomputing 211, 72‒83 (2016).
  23.  Y. Sun, Y. Liu, and H. Liu, “Temperature Compensation for a Six-Axis Force/Torque Sensor Based on the Particle Swarm Optimization Least Square Support Vector Machine for Space Manipulator”, IEEE Sensors Journal. 16(3), 798‒805 (2016).
  24.  X. Yan and N.A. Chowdhury, “Mid-term electricity market clearing price forecasting: A hybrid LSSVM and ARMAX approach”, Int. J. Electr. Power Energy Syst. 53, 20‒26 (2013)
  25.  S. Mirjalili, S.M. Mirjalili, and A. Lewis, “Grey Wolf Optimizer”, Adv. Eng. Softw. 69, 46‒61 (2014).
  26.  I.B.M. Taha and E.E. Elattar, “Optimal reactive power resources sizing for power system operations enhancement based on improved grey wolf optimiser”, IET Gener. Transm. Distrib. 12(14), 3421‒3434 (2018).
  27.  W. Long, J.J. Jiao, X.M. Liang, and M.Z. Tang, “Inspired grey wolf optimizer for solving large-scale function optimization problems”, Appl. Math. Model. 60, 112‒126 (2018).
  28.  Y. Li, B. Zhang, and X. Xu, “Decoupling control for permanent magnet in-wheel motor using internal model control based on back- propagation neural network inverse system”, Bull. Pol. Acad. Sci. Tech. Sci. 66(6), 961–972 (2018).
  29.  D. Huang, S. He, X. He, and X. Zhu, “Prediction of wind loads on high-rise building using a BP neural network combined with POD”, J. Wind Eng. Ind. Aerodyn. 170, 1‒17 (2017).
  30.  A.L. Yang, W.D. Li, and X. Yang, “Short-term electricity load forecasting based on feature selection and Least Squares Support Vector Machines”, Knowledge-Based Syst. 163, 159‒173 (2019).
  31.  N.A. Menad, Z. Noureddine, A. Hemmati-Sarapardeh, and S. Shamshirband, “Modeling temperature-based oil-water relative permeability by integrating advanced intelligent models with grey wolf optimization: Application to thermal enhanced oil recovery processes”, Fuel 242, 649‒663 (2019).






DOI: 10.24425/bpasts.2021.137124


Bulletin of the Polish Academy of Sciences: Technical Sciences; Early Access; e137124