Optimal Intelligent Control for Wind Turbulence Rejection in WECS Using ANNs and Genetic Fuzzy Approach

One of the disadvantages in Connection of wind energy conversion systems (WECSs) to transmission networks is plentiful turbulence of wind speed. Therefore effects of this problem must be controlled. �owadays, pitch-controlled WECSs are increasingly used for variable speed and pitch wind turbines. Megawatt class wind turbines generally turn at variable speed in wind farm. Thus turbine operation must be controlled in order to maximize the conversion efficiency below rated power and reduce loading on the drive-train. Due to random and non-linear nature of the wind turbulence and the ability of Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) Artificialeuraletworks (A��s) in the modeling and control of this turbulence, in this study, widespread changes of wind have been perused using MLP and RBF artificial ��s. In addition in this study, a new genetic fuzzy system has been successfully applied to identify disturbance wind in turbine input. Thus output power has been regulated in optimal and nominal range by pitch angle regulation. Consequently, our proposed approaches have regulated output aerodynamic power and torque in the nominal range.

[1]  Yoshikazu Ikeda,et al.  Explanatory rule extraction based on the trained neural network and the genetic programming , 2006 .

[2]  Hisao Ishibuchi,et al.  Techniques and Applications of Genetic Algorithm-Based Methods for Designing Compact Fuzzy Classification Systems , 1999 .

[3]  Muhando Billy Endusa,et al.  Modeling-Based Design of Intelligent Control Paradigms for Modern Wind Generating Systems , 2008 .

[4]  S. G. Deshmukh,et al.  FMS scheduling with knowledge based genetic algorithm approach , 2011, Expert Syst. Appl..

[5]  Mitsuo Gen,et al.  Genetic algorithms and engineering design , 1997 .

[6]  T. Funabashi,et al.  Output power leveling of wind farm using pitch angle control with fuzzy neural network , 2006, 2006 IEEE Power Engineering Society General Meeting.

[7]  Qinwei Li,et al.  Application of BP Neural Network for Wind Turbines , 2009, 2009 Second International Conference on Intelligent Computation Technology and Automation.

[8]  Siegfried Heier,et al.  Grid Integration of Wind Energy Conversion Systems , 1998 .

[9]  Yoh-Han Pao,et al.  Adaptive pattern recognition and neural networks , 1989 .

[10]  C. P. Butterfield,et al.  Variable speed operation of generators with rotor-speed feedback in wind power applications , 1995 .

[11]  Nikola Kasabov,et al.  Foundations Of Neural Networks, Fuzzy Systems, And Knowledge Engineering [Books in Brief] , 1996, IEEE Transactions on Neural Networks.

[12]  R. J. Kuo.  Intelligent diagnosis for turbine blade faults using artificial neural networks and fuzzy logic , 1995 .

[13]  Stefan Wermter,et al.  A Novel Modular Neural Architecture for Rule-Based and Similarity-Based Reasoning , 1998, Hybrid Neural Systems.

[14]  Qi Chen,et al.  Artificial-neural-network-based fast valving control in a power-generation system , 1997 .

[15]  Henning Heider,et al.  A cascaded genetic algorithm for improving fuzzy-system design , 1997, Int. J. Approx. Reason..

[16]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[17]  Mohammad Saniee Abadeh,et al.  Fuzzy Rule Extraction from a trained artificial neural network using Genetic Algorithm for WECS control and parameter estimation , 2011, 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD).

[18]  W. E. Leithead,et al.  LQG control of a constant speed horizontal axis wind turbine , 1994, 1994 Proceedings of IEEE International Conference on Control and Applications.

[19]  Tao Zhang,et al.  Application of fuzzy adaptive back-propagation neural network in thermal conductivity gas analyzer , 2010, Neurocomputing.

[20]  R. W. De Doncker,et al.  Doubly fed induction generator systems for wind turbines , 2002 .

[21]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[22]  Sang-Hoon Oh,et al.  Error back-propagation algorithm for classification of imbalanced data , 2011, Neurocomputing.

[23]  Fa-Long Luo,et al.  Applied neural networks for signal processing , 1997 .

[24]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[25]  Ahmet Serdar Yilmaz,et al.  Pitch angle control in wind turbines above the rated wind speed by multi-layer perceptron and radial basis function neural networks , 2009, Expert Syst. Appl..

[26]  Pierre-Yves Glorennec.  Coordination between autonomous robots , 1997, Int. J. Approx. Reason..

[27]  L.Y. Pao,et al.  Control of variable-speed wind turbines: standard and adaptive techniques for maximizing energy capture , 2006, IEEE Control Systems.

[28]  Hamid Reza Momeni,et al.  Optimal estimation and control of WECS via a Genetic Neuro Fuzzy Approach , 2012 .

[29]  N. Horiuchi,et al.  Torque and power limitations of variable speed wind turbines using pitch control and generator power control , 2001, 2001 Power Engineering Society Summer Meeting. Conference Proceedings (Cat. No.01CH37262).

[30]  Chul-Hwan Kim,et al.  LQG Design for Megawatt-Class WECS With DFIG Based on Functional Models' Fidelity Prerequisites , 2009, IEEE Transactions on Energy Conversion.

[31]  Maureen Hand,et al.  Multivariable control strategy for variable speed, variable pitch wind turbines , 2007 .

[32]  William Leithead,et al.  Review of wind turbine control , 1990 .

[33]  Fernando D. Bianchi,et al.  Optimal gain-scheduled control of fixed-speed active stall wind turbines , 2008 .

[34]  M. Grimble.  Two and a half degrees of freedom LQG controller and application to wind turbines , 1994, IEEE Trans. Autom. Control..

[35]  Tomonobu Senjyu,et al.  Output power leveling of wind farm using pitch angle control with fuzzy neural network , 2006 .