Modeling of doubly fed induction generator based wind energy conversion system and speed controller

Year 2021, Volume: 5 Issue: 1, 46 - 59, 31.03.2021

Endalew Haile Getachew Worku Asrat Mulatu Beyene , Milkias Tuka

https://doi.org/10.30521/jes.854669

Cited By: 6

Abstract

In this paper, modeling of doubly fed induction generator (DFIG) based wind energy conversion system (WECS) and generator speed controller are presented. The System Identification Toolbox of MatLab is used to develop the linear model of the WECS by considering the wind speed as input and speed of the generator as output. Two models, namely Auto Regressive with eXogenous Input (ARX) and Auto-Regressive Moving Average with eXogenous Input (ARMAX), are estimated. We used the ARX221 model structure with the best fit of 84.31%, Final Prediction Error (FPE) of 0.0433 and Mean Square Error (MSE) of 0.0432. The Ziegler-Nichols (Z-N) method and the fuzzy logic technique are employed in the proportional integral derivative (PID) controller design to control the speed of the generator. The classical Z-N PID used for the responses of the system is observed to be insufficient for both uniform and variable inputs, hence, a better response has been obtained by applying the fuzzy logic-based PID controller. The present study proves that the fuzzy logic based control enhances the speed regulation of generator in the WECS by overcoming the effect of varying wind speed.

Keywords

Fuzzy logic, PID, Speed control, WECS model, Ziegler-Nichols (Z-N) method

Supporting Institution

Addis Ababa Science and Technology University

Thanks

We acknowledge the professionals working at Adama wind farm owned by the Government of Ethiopia for providing the real-time wind speed data used in this research work.

References

• [1] Solberg O. A New Wind Turbine Control Method to Smooth Power Generation - Modelling and Comparison to Wind Turbine Frequency Control (MSc). Chalmers University of Technology, Sweden, 2012.
• [2] Billings SA, Chen S, Korenberg MJ. Identification of MIMO non-linear systems using a forward-regression orthogonal estimator. International journal of control 1989; 49(6): 2157-2189.
• [3] Subudhi B, Ogeti PS. Non-linear autoregressive moving average with exogenous input model-based adaptive control of a wind energy conversion system. The Journal of Engineering 2016; 2016(7): 218-226.
• [4] Johansen TA, Foss B. Constructing NARMAX models using ARMAX models. International journal of control 1993; 58(5): 1125-1153.
• [5] Bekker JC. Efficient modelling of a wind turbine system for parameter estimation applications (PhD), Stellenbosch University, Stellenbosch, South Africa, 2012.
• [6] Cadenas E, Rivera W. Wind speed forecasting in three different regions of Mexico using a hybrid ARIMA–ANN model. Renewable Energy 2010; 35(12): 2732-2738.
• [7] Perez-Llera C, Fernandez-Baizan MC, Feito JL, González del Valle V. Local short-term prediction of wind speed: a neural network analysis. In: 1st International Congress on Environmental Modelling and Software; 24-27 June 2002, Switzerland: Brigham Young University Scholars Archives, pp.124-129.
• [8] Nur Dalila Binti Khirul Ashar, Zakiah Mohd Yusoff, Nurlaila Ismail, Muhammad Asraf Hairuddin. ARX Model Identification for the Real-Time Temperature Process with MATLAB-ARDUINO Implementation. ICIC Express Letter 2020; 14(2): 103-111.
• [9] MATLAB R2020b Documentation – System Identification Toolbox, Accessed from https://www.mathworks.com/help/ident/?s_tid=srchbrcm, Accessed on October 2020.
• [10] Muhammad Junaid Rabbani, Kashan Hussain, Asim-ur-Rehman khan, Abdullah Ali. Model Identification and Validation for a Heating System using MATLAB System Identification Toolbox, 2013 IOP Conf. Ser.: Mater. Sci. Eng.51 012022.
• [11] Vijayalaxmi1, Shanmuga Vadivoo. Identification of Doubly Fed Induction Generator based Wind Energy Conversion System Using Piecewise-Linear Hammerstein Wiener Model, Proceedings of 7th International Conference on Intelligent Systems and Control 2013: 37 – 43.
• [12] Lin WM, Hong CM, Cheng FS. On-line designed hybrid controller with adaptive observer for variable-speed wind generation system. Energy 2010, 35(7): 3022-3030.
• [13] Barambones O, de Durana JG, De la Sen M. Robust speed control for a variable speed wind turbine. International Journal of Innovative Computing, Information and Control 2012, 8(11):7627-7640.
• [14] Munteanu I, Bratcu AI, Cutululis NA, Ceanga E. Optimal control of wind energy systems: towards a global approach. London, England: Springer-Verlag London, 2008.
• [15] Aroussi HA, Ziani E, Bossoufi B. Speed control of the doubly fed induction generator applied to a wind system. Journal of Theoretical and Applied Information Technology 2016; 83(3): 426-433.
• [16] Osmic J, Kusljugic M, Becirovic E, Toal D. Analysis of active power control algorithms of variable speed wind generators for power system frequency stabilization. Turkish Journal of Electrical Engineering & Computer Sciences 2016; 24(1): 234-246.
• [17] Jalali M. DFIG based wind turbine contribution to system frequency control (MSc). University of Waterloo, Ontario, Canada, 2011.
• [18] Adel Ab-BelKhair, Javad Rahebi, Abdulbaset Abdulhamed Mohamed Nureddin. A Study of Deep Neural Network Controller-Based Power Quality Improvement of Hybrid PV/Wind Systems by Using Smart Inverter. International Journal of Photoenergy2020, 2020.
• [19] Abdulbaset Abdulhamed Mohamed Nureddin, Javad Rahebi, Adel Ab-BelKhair. Power Management Controller for Microgrid Integration of Hybrid PV/Fuel Cell System Based on Artificial Deep Neural Network. International Journal of Photoenergy 2020, 2020.
• [20] Yasmine Ihedrane, Chakib El Bekkali, Badre Bossoufi, Manale Bouderbala. Control of Power of a DFIG Generator with MPPT Technique for Wind Turbines Variable Speed. In: Modelling, Identification and Control Methods in Renewable Energy Systems, Green Energy and Technology 2019; Springer Nature Singapore Pte Ltd., N. Derbel, Q. Zhu (eds.), pp. 105-127.
• [21] Hassane Mahmoudi, Marouane El Azzaoui, Chafik Ed-Dahmani. Zed Board-FPGA Control of the DFIG Based Wind Power System. In: Modeling, Identification and Control Methods in Renewable Energy Systems, Green Energy and Technology 2019; Springer Nature Singapore Pte Ltd., N. Derbel, Q. Zhu (eds.), pp. 333 -355.
• [22] Abdelbaset A, Mohamed YS, El-Sayed AH, Ahmed AE. Wind Driven Doubly Fed Induction Generator: Grid Synchronization and Control. Geneva, Switzerland: Springer International Publishing, 2018.
• [23] Vestas. General Specification V90–1.8/2.0 MW 50 Hz VCS: Document no.: 0004-6207 V05. Copenhagen, Denmark: Vestas Wind Systems A/S, 2010.
• [24] Simões, M. Godoy, Farret, A. Felix. Renewable Energy Systems: Design and Analysis with Induction Generators. California, USA: CRC Press, 2004.
• [25] Atacak I, Küçük B. PSO-Based PID Controller Design for an Energy Conversion System Using Compressed Air. Tehnicki Vjesnik/Technical Gazette 2017, 24(3): 671-679.
• [26] Woo ZW, Chung HY, Lin JJ. A PID type fuzzy controller with self-tuning scaling factors. Fuzzy sets and systems 2000, 115(2): 321-326.
• [27] Güzelkaya M, Eksin I, Yeşil E. Self-tuning of PID-type fuzzy logic controller coefficients via relative rate observer. Engineering applications of artificial intelligence 2003, 16(3): 227-236.
• [28] Qiao WZ, Mizumoto M. PID type fuzzy controller and parameters adaptive method. Fuzzy sets and systems 1996, 78(1), 23-35.
• [29] Jantzen J. Tuning of fuzzy PID controllers. In Rames C. P., Introduction to PID Controllers - Theory, Tuning and Application to Frontier Areas, Technical University of Denmark, Denmark: IntechOpen, 1998, 171-190.
• [30] Ogata K. Modern Control Engineering: Mathematical Modelling of Control Systems. New Jersey, USA: Prentice Hall, 2010.
• [31] Nadhir A, Naba A, Hiyama T. FIS/ANFIS based optimal control for maximum power extraction in variable-speed wind energy conversion system. IEEJ Transactions on Power and Energy 2011; 131(8): 708-714. DOI: 10.1541/ieejpes.131.708
• [32] Aydin E, Polat A, Ergene LT. Vector control of DFIG in wind power applications. In: IEEE International Conference on Renewable Energy Research and Applications (ICRERA); Nov 20 2016: IEEE, pp. 478-483.