Application of combined mathematical modeling/optimization methods coupled pitch controller in wind turbine using hybrid MLP neural network and firefly algorithm

Assareh, Ehsanolah; Poultangari, Iman; Ghanbarzadeh, Afshin

doi:10.22061/jcarme.2018.2482.1238

تعداد نشریات	11
تعداد شماره‌ها	227
تعداد مقالات	2,290
تعداد مشاهده مقاله	3,542,449
تعداد دریافت فایل اصل مقاله	2,592,146

	Application of combined mathematical modeling/optimization methods coupled pitch controller in wind turbine using hybrid MLP neural network and firefly algorithm
Journal of Computational & Applied Research in Mechanical Engineering (JCARME)
مقاله 10، دوره 13، شماره 2 - شماره پیاپی 26، فروردین 2024، صفحه 241-250 اصل مقاله (861.46 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22061/jcarme.2018.2482.1238
نویسندگان
Ehsanolah Assareh^* ¹؛ Iman Poultangari²؛ Afshin Ghanbarzadeh³
¹Department of Mechanical Engineering, Islamic Azad University Dezful Branch, Dezful, Khouzestan, Iran
²Department of Electrical Engineering, on, Islamic Azad University Dezful Branch, Dezful, Khouzestan, Iran
³Department of Mechanical Engineering, Shahid Chamran University, Ahvaz, Khouzestan, Iran
تاریخ دریافت: 30 فروردین 1396، تاریخ بازنگری: 15 شهریور 1397، تاریخ پذیرش: 21 شهریور 1397
چکیده
With pitch angle control, wind turbines can retain power generated at high speeds of wind and avoid severe mechanical stress. By varying the angle of the blades of the wind turbines, they can keep the power generated up near the maximum amount. A controller based on PI is suggested due to control angle of the pitch of the wind turbine blades in the present study. Therefore, PI controller gains are tuned via hybridization of firefly evolutionary algorithm and MLP artificial neural network so that the controller at its output sends a suitable control signal to the pitch actuator and thus varies the blades pitch angle appropriately to preserve power of the generator at a nominal amount even at high wind speeds. Simulating and analyzing the results was done by employing a five MW wind turbine made by National Renewable Energy Laboratory based on FAST software code. The simulation of the method showed that its performance is good.
کلیدواژه‌ها
Wind turbine؛ Pitch control؛ Firefly algorithm؛ MLP neural network

مراجع
[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).
آمار تعداد مشاهده مقاله: 1,137 تعداد دریافت فایل اصل مقاله: 1,000

سامانه مدیریت نشریات علمی. طراحی و پیاده سازی از سیناوب

پیوندهای مفید

آمار

Application of combined mathematical modeling/optimization methods coupled pitch controller in wind turbine using hybrid MLP neural network and firefly algorithm