Document Type: Research Paper

Authors

1 Mechanical Engineering department,azad university,Dezful,Iran

2 Electrical Engineering Department,azad university,Dezful,Iran

3 Mechanical Engineering Department,shahid chamran university,ahvaz,iran

Abstract

A common method utilized in wind turbines is pitch angle control whereby via varying the angle of wind turbine blades around their own axis, power generated at high speeds of wind is held around maximum amount and is kept away from the severe mechanical stress on wind turbine. In current study, in order to control pitch angle, a control method based on using PI controller is suggested. Therefore, gains of the PI controller are regulated through combining the Firefly evolutionary algorithm and MLP neural network in such a way that the controller at its output sends a suitable controlling signal to the pitch actuator to set the pitch angle and so by varying the blades pitch angle suitably at high speeds of wind, the produced generator power remains around its nominal value. A wind turbine 5MW made by NREL (National Renewable Energy Laboratory) has been utilized based on FAST software code to simulate and analyze the results. The simulation results show that proposed method has a good performance.

Graphical Abstract

Keywords

Main Subjects

 [1]    A.S. Yilmas and Z. Ozer, “Pitch angle   control in wind turbines above the rated wind speed by multi-layer perceptron and radial basic function neural networks”, Expert Systems with Applications,Vol. 36, No. 6, pp. 9767–75, (2009).

[2]  Y. Oguz and I. Guney, “Adaptive neuro-fuzzy inference system to improve the power quality of variable-speed wind power generation system”, Turkish Journal of Electrical Engineering and Computer Sciences,Vol.18,No.4, pp. 625–46, (2010).

[3]    L. Zhang, E. Chunliang, H. Li and H. Xu, “A new pitch control strategy for wind turbinesbased on quasi-sliding mode”, Sustainable Power Generation and Supply International Conference, pp.1–4, (2009).

[4]     X. Yao, C. Guo, Z. Xing, Y. Li, S. Liu and  X. Wang, “Pitch regulated LQG controller design for variable speed wind turbine”, Mechatronics and Automation ICMA International Conference, pp.845–9, (2009).

[5]    X. Yao, L. Guan, Q. Guo and X. Ma, “RBF neural network based self-tuning PID pitch control strategy for wind power generation system, Computer,Mechatronics”, Control and Electronic Engineering, pp.482–5, (2010).

[6]  X. Yao, X. Su and L. Tian, “Pitch angle control of variable pitch wind turbines based on neural network PID”, Industrial Electronics and Applications, pp.3235–9, (2009).

[7]    X. Yao, S. liu, G. Shan, Z. Xing, C. Guo and C. Yi, “LQG controller for a variable speed pitch regulated wind turbine”, Intelligent Human–Machine Systems and Cybernetics, pp.210–3, (2009).

 

[8]    F. Gao, D. Xu and Y. Lv, “Pitch control for large scale wind turbines based on feed forward fuzzy-PI”, Intelligent Control and Automation, pp.2277–82, (2008).

[9]    M.Q. Duong, F. Grimaccia, S. Leva, M. Mussetta and E. Ogliari, “Pitch angle control using hybrid controller for all operating regions of SCIG wind turbine system”, Renewable Energy, Vol. 70, pp.197–203, (2014).

[10]   S.A. Taher, M. Farshadnia and M.R. Mozdianfard, “Optimal gain scheduling controller design of a pitch-controlled VS-WECS using DE optimization algorithm”, Applied Soft Computing,Vol.13, No. 5, pp. 2215–2223, (2013).

[11]  B. Chen, P.C. Matthews and P.J. Tavner, “Wind turbine pitch faults prognosis using a-priori knowledge-based ANFIS”, Expert Systems with Applications,Vol. 40, No. 17, pp. 6863–6876, (2013).

[12]  A. Musyafa, A. Harika, I.M.Y. Negara and  I. Robandi, “Pitch angle control of variable low rated speed wind turbine using fuzzy logic controller”, International Journal of Engineering &Technology IJET-IJENS, pp.4-21, (2010).

[13]   J. Jonkman, S. Butterfield, W. Musial and G. Scott, “ Definition of a 5-MW reference wind turbine for offshore system development. Technical report”, National Renewable Energy Laboratory (NREL), Golden, Colorado, USA, (2009).

[14]   F. Iof, A.D. Hansen, P. Sorensen and  F. Blaabjerg, “Wind turbine blockset in MATLAB/ simulink, General overview and description of the model”, Aalborg University, (2004).

[15] B. Boukhezzar, H. Siguerdidjane, “Nonlinear control of a variable wind turbine using a two mass model”, IEEE Transactions on Energy Conversion, Vol.26, No.1, pp.149-162, (2011).

[16] D.T. Pham and X. Liu, “Neural Networks for  Identification”, Prediction and Control, Springer Verlag, London, (1995).

[17] L.F.F. Miguel, R.H. Lopez and L.F.F. Miguel, “Multimodal size, shape, and topology optimisation of truss structures using the Firefly algorithm”, Advances in Engineering Software,Vol. 56, pp.23–37, (2013).

[18]    J.M. Jonkman and M.L. Buhl, “FAST user’s guide”, NREL/EL-500-29798, Golden, CO: National Renewable Energy Laboratory, (2005).

[19]    P. Sorensen, A.D. Hansen and P.A.C. Rosas, “Wind models for simulation of power fluctuations from wind farms”, Wind Engineering, pp.1381–402, (2002).

[20]    E. Mjabber, A. Hajjaji and A. khamlichi , “Analysis of a RBF neural network based controller for pitch angle of variable speed wind turbines”, procedia engineering, Vol.181, pp.552-559, 2017.

[21]   L. Zhongwei, C. Zhenyu, W. Qiuwei,  Y. Shuo and M. Hongmin, “Coordinated pitch & torque control of large-scale wind turbine based on Pareto efficiency analysis”,  Energy, DOI: 10.1016/j.energy.2018.01.055, (2018).

CAPTCHA Image