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

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The optimum combination of blade angle of the runner and guide vane opening with Kaplan turbine can improve the hydroelectric generating the set operation efficiency and the suppression capability of oscillations. Due to time and cost limitations and the complex operation mechanism of the Kaplan turbine, the coordination test data is insufficient, making it challenging to obtain the whole curves at each head under the optimum coordination operation by field tests. The field test data is employed to propose a least-squares support vector machine (LSSVM)-based prediction model for Kaplan turbine coordination tests. Considering the small sample characteristics of the test data of Kaplan turbine coordination, the LSSVM parameters are optimized by an improved grey wolf optimization (IGWO) algorithm with mixed non-linear factors and static weights. The grey wolf optimization (GWO) algorithm has some deficiencies, such as the linear convergence factor, which inaccurately simulates the actual situation, and updating the position indeterminately reflects the absolute leadership of the leader wolf. The IGWO algorithm is employed to overcome the aforementioned problems. The prediction model is simulated to verify the effectiveness of the proposed IGWO-LSSVM. The results show high accuracy with small samples, a 2.59% relative error in coordination tests, and less than 1.85% relative error in non-coordination tests under different heads.
Rocznik
Strony
art. no. e137124
Opis fizyczny
Bibliogr. 31 poz., rys., tab.
Twórcy
autor
  • School of Electrical Engineering, Guangxi University, Nanning, 530000, China
autor
  • School of Electrical Engineering, Guangxi University, Nanning, 530000, China
autor
  • School of Electrical Engineering, Guangxi University, Nanning, 530000, China
autor
  • School of Electrical Engineering, Guangxi University, Nanning, 530000, China
Bibliografia
  • [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).
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-ed930d99-0458-4c4c-8d89-b327eecb3f78
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