(Publisher of Peer Reviewed Open Access Journals)

International Journal of Advanced Technology and Engineering Exploration (IJATEE)

ISSN (Print):2394-5443    ISSN (Online):2394-7454
Volume-9 Issue-86 January-2022
Full-Text PDF
Paper Title : Modelling design of wind turbine generator
Author Name : Thaker Nayl, Mohammed Q. Mohammed and Saif Q. Muhamed
Abstract :

The development of control systems to improve efficiency requires accurate mathematical models. This article deals with the modelling of two-mass variable speed wind turbine generators. A model design of a 3.5 MW vertically axial wind generator and a mathematical model of an electromechanical system is considered in this article. Wind turbine generators behave to have the most significant uncertainty, specified the possibility for nonlinear behaviour. The main focus is on structural aerodynamics, including the forcing and motion of the rotating parts of the turbine. The turbine’s critical structural aerodynamics and mechanical components are the blade pitch actuators, drive shaft actuators, and turbine specifications. With an accurate wind turbine model, the control engineers will design control systems to reduce loads, increase the operating lifetime, and increase electrical power. Methods of linearization about operating points have been proposed to enable an efficient control system design. The results show that the model can be used in different strategies evaluation.

Keywords : Wind turbine model, Linearization strategies, Blade pitch angle, Mechanical torque.
Cite this article : Nayl T, Mohammed MQ, Muhamed SQ. Modelling design of wind turbine generator. International Journal of Advanced Technology and Engineering Exploration. 2022; 9(86):82-93. DOI:10.19101/IJATEE.2021.874732.
References :
[1]Schmitz S. Aerodynamics of wind turbines: a physical basis for analysis and design. John wiley & sons; 2020.
[Google Scholar]
[2]Mardoude Y, Hilali A, Rahali A. Modeling and control of a wind power system based on doubly fed induction machine by aerodynamic power coefficient neural network approximation. In international conference on digital age & technological advances for sustainable development 2021 (pp. 188-92). IEEE.
[Crossref] [Google Scholar]
[3]Beniss MA, El MH, Lamhamdi T, El MH. Performance analysis and enhancement of direct power control of DFIG based wind system. International Journal of Power Electronics and Drive Systems. 2021; 12(2):1034-44.
[Crossref] [Google Scholar]
[4]Hur SH. Modelling and control of a wind turbine and farm. Energy. 2018; 156:360-70.
[Crossref] [Google Scholar]
[5]Abro KA. Numerical study and chaotic oscillations for aerodynamic model of wind turbine via fractal and fractional differential operators. Numerical Methods for Partial Differential Equations. 2020.
[Crossref] [Google Scholar]
[6]Ahmad RT, Abdul-Hussain MA. Modeling and simulation of wind turbine generator using matlab-simulink. Journal of Al Rafidain University College. 2017:282-99.
[Google Scholar]
[7]Fandi G, Igbinovia FO, Ahmad I, Svec J, Muller Z. Modeling and simulation of a gearless variable speed wind turbine system with PMSG. In PES powerafrica 2017 (pp. 59-64). IEEE.
[Crossref] [Google Scholar]
[8]Wei L, Liu Z, Zhao Y, Wang G, Tao Y. Modeling and control of a 600 kW closed hydraulic wind turbine with an energy storage system. Applied Sciences. 2018; 8(8):1-18.
[Crossref] [Google Scholar]
[9]Ammar A. Performance improvement of direct torque control for induction motor drive via fuzzy logic-feedback linearization: simulation and experimental assessment. COMPEL-The International Journal for Computation and Mathematics in Electrical and Electronic Engineering. 2019; 38(2): 672-92.
[Crossref] [Google Scholar]
[10]Pliego MA, García MFP. Advanced analytics for detection and diagnosis of false alarms and faults: a real case study. Wind Energy. 2019; 22(11):1622-35.
[Crossref] [Google Scholar]
[11]Porté-agel F, Bastankhah M, Shamsoddin S. Wind-turbine and wind-farm flows: a review. Boundary-Layer Meteorology. 2020; 174(1):1-59.
[Crossref] [Google Scholar]
[12]Hafiz F, Abdennour A. Optimal use of kinetic energy for the inertial support from variable speed wind turbines. Renewable Energy. 2015; 80:629-43.
[Crossref] [Google Scholar]
[13]Stetco A, Dinmohammadi F, Zhao X, Robu V, Flynn D, Barnes M, et al. Machine learning methods for wind turbine condition monitoring: a review. Renewable Energy. 2019; 133:620-35.
[Crossref] [Google Scholar]
[14]He X, Geng H, Mu G. Modeling of wind turbine generators for power system stability studies: a review. Renewable and Sustainable Energy Reviews. 2021.
[Crossref] [Google Scholar]
[15]Yuvaraja T, Ramya K. Analysis of wind turbine modelling using TSMC techniques. COMPEL-The International Journal for Computation and Mathematics in Electrical and Electronic Engineering. 2018; 37(6):1981-92.
[Crossref] [Google Scholar]
[16]Modukpe G, Diei D. Modeling and simulation of a 10 KW wind energy in the coastal area of southern Nigeria: case of ogoja. Wind Solar Hybrid Renewable Energy System. 2020.
[Google Scholar]
[17]Karthik R, Hari AS, Kumar YP, Pradeep DJ. Modelling and control design for variable speed wind turbine energy system. In international conference on artificial intelligence and signal processing 2020 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[18]Sedaghatizadeh N, Arjomandi M, Kelso R, Cazzolato B, Ghayesh MH. Modelling of wind turbine wake using large eddy simulation. Renewable Energy. 2018; 115:1166-76.
[Crossref] [Google Scholar]
[19]Sun H, Qiu C, Lu L, Gao X, Chen J, Yang H. Wind turbine power modelling and optimization using artificial neural network with wind field experimental data. Applied Energy. 2020.
[Crossref] [Google Scholar]
[20]Golnary F, Moradi H. Dynamic modelling and design of various robust sliding mode controls for the wind turbine with estimation of wind speed. Applied Mathematical Modelling. 2019; 65:566-85.
[Crossref] [Google Scholar]
[21]Mokhtari Y, Rekioua D. High performance of maximum power point tracking using ant colony algorithm in wind turbine. Renewable Energy. 2018; 126:1055-63.
[Crossref] [Google Scholar]
[22]Pao LY, Johnson KE. Control of wind turbines. IEEE Control Systems Magazine. 2011; 31(2):44-62.
[Crossref] [Google Scholar]
[23]Ren H, Hou B, Zhou G, Shen L, Wei C, Li Q. Variable pitch active disturbance rejection control of wind turbines based on BP neural network PID. IEEE Access. 2020; 8:71782-97.
[Crossref] [Google Scholar]
[24]Bianchi FD, De BH, Mantz RJ. Wind turbine control systems: principles, modelling and gain scheduling design. London: Springer; 2007.
[Google Scholar]