Comparative Analysis of Pitch Angle Controller Strategies for PMSG Based Wind Energy Conversion System

Comparative Analysis of Pitch Angle Controller Strategies for PMSG Based Wind Energy Conversion System

Автор: Ramji Tiwari, Ramesh Babu. N

Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa

Статья в выпуске: 5 vol.9, 2017 года.

Бесплатный доступ

This paper proposes an advanced pitch angle control strategy based on neural network (NN) for variable speed wind turbine. The proposed methodology uses Radial Basis Function Network (RBFN) and Feed-forward based Back propagation network (BPN) algorithm to generate pitch angle. The performance of the proposed control technique is analyzed by comparing the results with Fuzzy Logic Control (FLC) and Proportional - Integral (PI) control techniques. The control techniques implemented is able to compensate the nonlinear characteristic of wind speed. The wind turbine is smoothly controlled to maintain the generator power and the mechanical torque to the rated value without any fluctuation during rapid variation in wind speed. The effectiveness of the proposed control strategy is verified using MATLAB/Simulink for 2-MW permanent magnet synchronous generator (PMSG) based wind energy conversion system.

Еще

Wind energy conversion system, Permanent magnet synchronous generator, Pitch angle, Fuzzy logic, Back propagation, neural network, Radial basis function network

Короткий адрес: https://sciup.org/15010933

IDR: 15010933

Список литературы Comparative Analysis of Pitch Angle Controller Strategies for PMSG Based Wind Energy Conversion System

  • K.Y. Oh, J. Y. Park, J. S. Lee, and J. Lee, "Implementation of a torque and a collective pitch controller in a wind turbine simulator to characterize the dynamics at three control regions," Renewable Energy 79, 150-60 (2015).
  • R. Tiwari, and N. R. Babu, "Recent developments of control strategies for wind energy conversion system," Renewable Sustainable Energy Rev. 66, 268-85 (2016).
  • T. L. Van, T. H. Nguyen and D. C. Lee, "Advanced pitch angle control based on fuzzy logic for variable-speed wind turbine systems," IEEE Trans. Energy Convers. 30, 578-87 (2015).
  • 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 70, 197-203 (2014).
  • H. Moradi, and G. Vossoughi, "Robust control of the variable speed wind turbines in the presence of uncertainties: A comparison between H∞ and PID controllers," Energy 90, 1508-21 (2015).
  • R. Saravanakumar and D. Jena, "Validation of an integral sliding mode control for optimal control of a three blade variable speed variable pitch wind turbine," Int. J. Elect. Power Energy Syst. 69, 421-9 (2015).
  • X. Yao, S. Liu, G. Shan, Z. Xing, C. Guo and C. Yi, "LQG controller for a variable speed pitch regulated wind turbine," IEEE Int. Conf. Intelligent Human-Machine Systems Cybernetics (2009), pp. 210–213.
  • T. Senjyu, R. Sakamoto, N. Urasaki, T. Funabashi, H. Fujita and H. Sekine, "Output power leveling of wind turbine generator for all operating regions by pitch angle control," IEEE Trans Energy Convers. 21, 467-75 (2006).
  • R. Tiwari and N. R. Babu, "Fuzzy Logic Based MPPT for Permanent Magnet Synchronous Generator in wind Energy Conversion System," IFAC-PapersOnLine 49, 462-7 (2016).
  • M. A. Chowdhury, N. Hosseinzadeh and W. X. Shen, "Smoothing wind power fluctuations by fuzzy logic pitch angle controller," Renewable Energy 38, 224-33 (2012).
  • Y. Daili, J. P. Gaubert, L. Rahmani, "Implementation of a new maximum power point tracking control strategy for small wind energy conversion systems without mechanical sensors," Energy Convers. Manag. 97, 298-306 (2015).
  • E. G. Shehata, "A comparative study of current control schemes for a direct-driven PMSG wind energy generation system," Electr. Power Syst. Res. 143, 197-205 (2017).
  • Y. Zhang, Z. Chen, W. Hu and M. Cheng, "Flicker mitigation by individual pitch control of variable speed wind turbines with DFIG," IEEE Trans. Energy Convers. 29, 20-8 (2014).
  • X. X. Yin, Y. G. Lin, W. Li, Y. J. Gu, X. J. Wang and P. F. Lei, "Design, modeling and implementation of a novel pitch angle control system for wind turbine," Renewable Energy 81, 599-608 (2015).
  • V. Akhmatov, "Analysis of dynamic behaviour of electric power systems with large amount of wind power," Ph.D. dissertation (Denmark: Technical University, 2003).
  • Y. Zhang, Z. Chen and M. Cheng, "Proportional resonant individual pitch control for mitigation of wind turbines loads," IET Renewable Power Gener. 7, 191-200 (2013).
  • A. Beddar, H. Bouzekri, B. Babes and H. Afghoul, "Experimental enhancement of fuzzy fractional order PI+ I controller of grid connected variable speed wind energy conversion system," Energy Convers. Manag. 123, 569-80 (2016).
  • S. M. Muyeen, R. Takahashi, T. Murata and J. Tamura, "A variable speed wind turbine control strategy to meet wind farm grid code requirements," IEEE Trans. Power Syst. 25, 331-40 (2010).
  • Y. Ren, L. Li, J. Brindley and L. Jiang, "Nonlinear PI control for variable pitch wind turbine. Control Eng. Practice 50, 84-94 (2016).
  • R. M. Kamel, A. Chaouachi, K. Nagasaka, "Enhancement of micro-grid performance during islanding mode using storage batteries and new fuzzy logic pitch angle controller. Energy Convers. Manag. 52, 2204-16 (2011).
  • V. S. Raviraj and P. C. Sen, "Comparative study of proportional-integral, sliding mode, and fuzzy logic controllers for power converters," IEEE Trans. Ind. Appl. 33, 518-24 (1997).
  • A. S. Yilmaz and Z. Özer, "Pitch angle control in wind turbines above the rated wind speed by multi-layer perceptron and radial basis function neural networks,” Expert Syst. Appl. 36, 9767-75 (2009).
  • H. Sefidgar and S. A. Gholamian, “Fuzzy logic control of wind turbine system connection to PM synchronous generator for maximum power point tracking,” Int. J. Intel. Syst. Appl. 6, 29-35 (2014).
  • M. T. Makhloufi, M. S. Khireddine, Y. Abdessemed and A. Boutarfa, “Tracking Power Photovoltaic System using Artificial Neural Network Control Strategy,” Int. J. Intel. Syst. Appl. 6, 17-26, (2014).
  • C. H. Chen, C. M. Hong and T. C. Ou, "Hybrid fuzzy control of wind turbine generator by pitch control using RNN," Int. J. Ambient Energy 33, 56-64 (2012).
  • N. R. Babu and P. Arulmozhivarman, "Improving forecast accuracy of wind speed using wavelet transform and neural networks," J. Elect. Eng. Tech. 8, 559-64 (2013).
  • 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," Renewable Sustainable Energy Rev 51, 1023-37 (2015).
  • N. R. Babu and P. Arulmozhivarman, "Forecasting of wind speed using artificial neural networks," Int. Rev. Mod. Sim. 5, 2276-80 (2012).
  • S. Saravanan and N. R. Babu, "RBFN based MPPT algorithm for PV system with high step up converter," Energy Convers. Manag. 122, 239-51 (2016).
  • I. Poultangari, R. Shahnazi and M. Sheikhan, "RBF neural network based PI pitch controller for a class of 5-MW wind turbines using particle swarm optimization algorithm," ISA Trans. 51, 641-8 (2012).
  • W. M. Lin, C. M. Hong, T. C. Ou and T. M. Chiu, "Hybrid intelligent control of PMSG wind generation system using pitch angle control with RBFN," Energy Convers. Manag. 52, 1244-51 (2011).
Еще
Статья научная
QR-код для установки приложения SciUp Наведите камеру и скачайте бесплатное приложение SciUp