Intelligent controller for maximum power extraction of wind generation systems using ANN
-
Driss Belkhiri
,
Lahoussine Elmahni
Abstract
The generation of clean energy from wind has recently received huge attention. Thanks to the current advances of adaptive algorithms due to their benefits and flexibility. The paper introduces a new smart radial basis function (RBF) neural network to extract the optimal energy from wind for wind energy conversion systems. This scheme uses the electrical energy of the doubly fed induction-generator (DFIG) as an input in wind turbines drives a DFIG to acquire maximum energy from the available wind under uncertainties and fast-changing wind conditions. Thus, to prove the quality of our proposed intelligent scheme, a comparative study with conventional optimum power is applied to a wind turbine driving a class of 1.5 MW DFIG during the transient operation. Furthermore, the analysis and the interpretation of raw and processed real measured data using the process of linear interpolation through Matlab/Simulink illustrate the relevance and the performance of the sensorless controller for the overall wind turbine system. Briefly, the numerical simulation studies show that a good efficiency and improved tracking of the smart RBF-neural network controller when implemented online below the real wind speed despite the unknown parameters.
Acknowledgments
The authors express sincere thanks to Dr. Mr. Sameh A. Eisa for helping to train and verify the intelligent controller by experimental data of Figure 7.
-
Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
-
Research funding: None declared.
-
Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
References
[1] B. Driss, S. Farhat, K. Abdelilah, and E. M. A. M. Rachid, “Adaptive control for variable-speed wind generation systems using advanced rbf neural network,” in 2020 1st International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), IEEE, 2020, pp. 1–5. https://doi.org/10.1109/IRASET48871.2020.9092153.Search in Google Scholar
[2] M. Yin, Z. Yang, Y. Xu, J. Liu, L. Zhou, and Y. Zou, “Aerodynamic optimization for variable-speed wind turbines based on wind energy capture efficiency,” Appl. Energy, vol. 221, pp. 508–521, 2018. https://doi.org/10.1016/j.apenergy.2018.03.078.Search in Google Scholar
[3] M. Cheng and Y. Zhu, “The state of the art of wind energy conversion systems and technologies: a review,” Energy Convers. Manag., vol. 88, pp. 332–347, 2014. https://doi.org/10.1016/j.enconman.2014.08.037.Search in Google Scholar
[4] C. Q. G. Muñoz and F. P. G. Márquez, “Wind energy power prospective,” in Renewable Energies, Springer, 2018, pp. 83–95.10.1007/978-3-319-45364-4_6Search in Google Scholar
[5] A. Pliego Marugán, F. P. Garcia Marquez, and B. Lev, “Optimal decision-making via binary decision diagrams for investments under a risky environment,” Int. J. Prod. Res., vol. 55, no. 18, pp. 5271–5286, 2017. https://doi.org/10.1080/00207543.2017.1308570.Search in Google Scholar
[6] D. Kumar and K. Chatterjee, “A review of conventional and advanced mppt algorithms for wind energy systems,” Renew. Sustain. Energy Rev., vol. 55, pp. 957–970, 2016. https://doi.org/10.1016/j.rser.2015.11.013.Search in Google Scholar
[7] G. Abad, J. Lopez, M. Rodriguez, L. Marroyo, and G. Iwanski, Doubly Fed Induction Machine: Modeling and Control for Wind Energy Generation, vol. 85, John Wiley & Sons, 2011.10.1002/9781118104965Search in Google Scholar
[8] R. Kot, M. Rolak, and M. Malinowski, “Comparison of maximum peak power tracking algorithms for a small wind turbine,” Math. Comput. Simulat., vol. 91, pp. 29–40, 2013. https://doi.org/10.1016/j.matcom.2013.03.010.Search in Google Scholar
[9] S. Ganjefar, A. A. Ghassemi, and M. M. Ahmadi, “Improving efficiency of two-type maximum power point tracking methods of tip-speed ratio and optimum torque in wind turbine system using a quantum neural network,” Energy, vol. 67, pp. 444–453, 2014. https://doi.org/10.1016/j.energy.2014.02.023.Search in Google Scholar
[10] C. Q. G. Muñoz, A. A. Jiménez, and F. P. G. Márquez, “Wavelet transforms and pattern recognition on ultrasonic guides waves for frozen surface state diagnosis,” Renew. Energy, vol. 116, pp. 42–54, 2018. https://doi.org/10.1007/978-3-319-45364-4-6.Search in Google Scholar
[11] A. Stetco, F. Dinmohammadi, X. Zhao, V. Robu, D. Flynn, M. Barnes, J. Keane, and G. Nenadic, “Machine learning methods for wind turbine condition monitoring: a review,” Renew. Energy, vol. 133, pp. 620–635, 2019. https://doi.org/10.1016/j.renene.2018.10.047.Search in Google Scholar
[12] E. Assareh and M. Biglari, “A novel approach to capture the maximum power from variable speed wind turbines using pi controller, rbf neural network and gsa evolutionary algorithm,” Renew. Sustain. Energy Rev., vol. 51, pp. 1023–1037, 2015. https://doi.org/10.1016/j.rser.2015.07.034.Search in Google Scholar
[13] M. S. Mahmoud and M. O. Oyedeji, “Adaptive and predictive control strategies for wind turbine systems: a survey,” IEEE/CAA J. Autom. Sin., vol. 6, no. 2, pp. 364–378, 2019. https://doi.org/10.1109/JAS.2019.1911375.Search in Google Scholar
[14] D. Petković, “Adaptive neuro-fuzzy approach for estimation of wind speed distribution,” Int. J. Electr. Power Energy Syst., vol. 73, pp. 389–392, 2015. https://doi.org/10.1016/j.ijepes.2015.05.039.Search in Google Scholar
[15] X.-S. Yang, Nature-inspired Optimization Algorithms, Academic Press, 2020.10.1016/B978-0-12-821986-7.00013-5Search in Google Scholar
[16] A. Arcos Jiménez, C. Q. Gómez Muñoz, and F. P. García Márquez, “Machine learning for wind turbine blades maintenance management,” Energies, vol. 11, no. 1, p. 13, 2018. https://doi.org/10.3390/en11010013.Search in Google Scholar
[17] Z. O. Olaofe, “A 5-day wind speed & power forecasts using a layer recurrent neural network (lrnn),” Sustain. Energy Technol. Assessments, vol. 6, pp. 1–24, 2014. https://doi.org/10.1016/j.seta.2013.12.001.Search in Google Scholar
[18] S. A. Eisa, “Modeling dynamics and control of type-3 dfig wind turbines: stability, q droop function, control limits and extreme scenarios simulation,” Elec. Power Syst. Res., vol. 166, pp. 29–42, 2019. https://doi.org/10.1016/j.epsr.2018.09.018.Search in Google Scholar
[19] S. Velpula and T. Rajaram, “A simple approach to modelling and control of dfig-based wecs in network reference frame,” Int. J. Ambient Energy, pp. 1–11, 2020. https://doi.org/10.1080/01430750.2020.1740784.Search in Google Scholar
[20] K. Bedoud, M. Ali-rachedi, T. Bahi, R. Lakel, and A. Grid, “Robust control of doubly fed induction generator for wind turbine under sub-synchronous operation mode,” Energy Proc., vol. 74, pp. 886–899, 2015. https://doi.org/10.1016/j.egypro.2015.07.824.Search in Google Scholar
[21] D.-C. Phan and S. Yamamoto, “Maximum energy output of a dfig wind turbine using an improved mppt-curve method,” Energies, vol. 8, no. 10, pp. 11718–11736, 2015. https://doi.org/10.3390/en81011718.Search in Google Scholar
[22] S. A. Eisa, K. Wedeward, and W. Stone, “Wind turbines control system: nonlinear modeling, simulation, two and three time scale approximations, and data validation,” Int. J. Dyn. Control, vol. 6, no. 4, pp. 1776–1798, 2018. https://doi.org/10.1007/s40435-018-0420-4.Search in Google Scholar
[23] A. O. Baba, G. Liu, and X. Chen, “Classification and evaluation review of maximum power point tracking methods,” Sustainable Futures, vol. 2, p. 100020, 2020.10.1016/j.sftr.2020.100020Search in Google Scholar
[24] L. Jinkun, Radial Basis Function Neural Network Control for Mechanical Systems, London, Springer, 2013.Search in Google Scholar
[25] S. A. Kalogirou, “Artificial neural networks in renewable energy systems applications: a review,” Renew. Sustain. Energy Rev., vol. 5, no. 4, pp. 373–401, 2001. https://doi.org/10.1016/S1364-0321(01)00006-5.Search in Google Scholar
[26] H. Li, K. Shi, and P. McLaren, “Neural-network-based sensorless maximum wind energy capture with compensated power coefficient,” IEEE Trans. Ind. Appl., vol. 41, no. 6, pp. 1548–1556, 2005. https://doi.org/10.1109/TIA.2005.858282.Search in Google Scholar
© 2022 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Original Research Articles
- Testing of logarithmic-law for the slip with friction boundary condition
- A new clique polynomial approach for fractional partial differential equations
- The modified Rusanov scheme for solving the phonon-Bose model
- Delta-shock for a class of strictly hyperbolic systems of conservation laws
- The Cădariu–Radu method for existence, uniqueness and Gauss Hypergeometric stability of a class of Ξ-Hilfer fractional differential equations
- Novel periodic and optical soliton solutions for Davey–Stewartson system by generalized Jacobi elliptic expansion method
- The simulation of two-dimensional plane problems using ordinary state-based peridynamics
- Reduced basis method for the nonlinear Poisson–Boltzmann equation regularized by the range-separated canonical tensor format
- Simulation of the crystallization processes by population balance model using a linear separation method
- PS and GW optimization of variable sliding gains mode control to stabilize a wind energy conversion system under the real wind in Adrar, Algeria
- Characteristics of internal flow of nozzle integrated with aircraft under transonic flow
- Magnetogasdynamic shock wave propagation using the method of group invariance in rotating medium with the flux of monochromatic radiation and azimuthal magnetic field
- The influence pulse-like near-field earthquakes on repairability index of reversible in mid-and short-rise buildings
- Intelligent controller for maximum power extraction of wind generation systems using ANN
- A new self-adaptive inertial CQ-algorithm for solving convex feasibility and monotone inclusion problems
- Existence and Hyers–Ulam stability of solutions for nonlinear three fractional sequential differential equations with nonlocal boundary conditions
- A study on solvability of the fourth-order nonlinear boundary value problems
- Adaptive control for position and force tracking of uncertain teleoperation with actuators saturation and asymmetric varying time delays
- Framing the hydrothermal significance of water-based hybrid nanofluid flow over a revolving disk
- Catalytic surface reaction on a vertical wavy surface placed in a non-Darcy porous medium
- Carleman framework filtering of nonlinear noisy phase-locked loop system
- Corrigendum
- Corrigendum to: numerical modeling of thermal influence to pollutant dispersion and dynamics of particles motion with various sizes in idealized street canyon
Articles in the same Issue
- Frontmatter
- Original Research Articles
- Testing of logarithmic-law for the slip with friction boundary condition
- A new clique polynomial approach for fractional partial differential equations
- The modified Rusanov scheme for solving the phonon-Bose model
- Delta-shock for a class of strictly hyperbolic systems of conservation laws
- The Cădariu–Radu method for existence, uniqueness and Gauss Hypergeometric stability of a class of Ξ-Hilfer fractional differential equations
- Novel periodic and optical soliton solutions for Davey–Stewartson system by generalized Jacobi elliptic expansion method
- The simulation of two-dimensional plane problems using ordinary state-based peridynamics
- Reduced basis method for the nonlinear Poisson–Boltzmann equation regularized by the range-separated canonical tensor format
- Simulation of the crystallization processes by population balance model using a linear separation method
- PS and GW optimization of variable sliding gains mode control to stabilize a wind energy conversion system under the real wind in Adrar, Algeria
- Characteristics of internal flow of nozzle integrated with aircraft under transonic flow
- Magnetogasdynamic shock wave propagation using the method of group invariance in rotating medium with the flux of monochromatic radiation and azimuthal magnetic field
- The influence pulse-like near-field earthquakes on repairability index of reversible in mid-and short-rise buildings
- Intelligent controller for maximum power extraction of wind generation systems using ANN
- A new self-adaptive inertial CQ-algorithm for solving convex feasibility and monotone inclusion problems
- Existence and Hyers–Ulam stability of solutions for nonlinear three fractional sequential differential equations with nonlocal boundary conditions
- A study on solvability of the fourth-order nonlinear boundary value problems
- Adaptive control for position and force tracking of uncertain teleoperation with actuators saturation and asymmetric varying time delays
- Framing the hydrothermal significance of water-based hybrid nanofluid flow over a revolving disk
- Catalytic surface reaction on a vertical wavy surface placed in a non-Darcy porous medium
- Carleman framework filtering of nonlinear noisy phase-locked loop system
- Corrigendum
- Corrigendum to: numerical modeling of thermal influence to pollutant dispersion and dynamics of particles motion with various sizes in idealized street canyon
