Review and Improvement of Several Optimal Intelligent Pitch Controllers and Estimator of WECS via Artificial Intelligent Approaches

Wind turbines in megawatt classification ordinarily rotate at variable speed in wind farm. Therefore turbine operation must be managed in order to maximize the conversion efficiency below rated power and reduce loading on the drive-train. In addition, to control the energy captured throughout operation above and below rated wind speed, researchers particularly employ pitch control of the blades. Thus, we could manage the energy captured throughout operation above and below rated wind speed using pitch control of the blades. This chapter suggests six new plans to conquer wind fluctuation problems based on a new Nero Fuzzy and Nero Fuzzy Genetic Controller where the fuzzy knowledge based are tuned automatically by Genetic Algorithm (GA) as known Tuned Fuzzy Genetic System (TFGS). Additionally In this Chapter, a new hybrid control has been trained that Wind Energy Conversion System (WECS) has optimal performance. This method contains a Multi-Layer Perceptron (MLP) Neural Network (NN) (MLPNN) and a Fuzzy Rule extraction from a Trained Artificial Neural Network using Genetic Algorithm (FRENGA). Proposed Hybrid method recognizes disturbance wind with sensors and it generates desired pitch angle control by comparison between FRENGA and MLPNN results. One of them has better signal control is selected to send to pitch blade controller. Consequently Proposed strategies reject wind disturbance in Wind Energy Conversion Systems (WECSs) input with pitch angel control generation. Consequently, proposed approaches have regulated output aerodynamic power and torque in the nominal range. Results indicate that the new proposed Artificial Intelligent (AI) methods extraction system outperform the best and earliest methods in controlling the output during wind fluctuation.

[1]  Constantin V. Negoita,et al.  On Fuzzy Systems , 1978 .

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

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

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

[5]  Kazumi Saito,et al.  Extracting regression rules from neural networks , 2002, Neural Networks.

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

[7]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[8]  Daming Shi,et al.  Mining fuzzy association rules with weighted items , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[9]  Whei-Min Lin,et al.  Fuzzy neural network output maximization control for sensorless wind energy conversion system , 2010 .

[10]  Eduard Muljadi,et al.  The History and State of the Art of Variable-Speed Wind Turbine Technology , 2001 .

[11]  S. K. Salman,et al.  Windmill modelling consideration and factors influencing the stability of a grid-connected wind power based embedded generator , 2003, 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491).

[12]  Nikos D. Hatziargyriou,et al.  Dynamic analysis of a variable speed wind turbine equipped with a voltage source AC/DC/AC converter interface and a reactive current control loop , 2000, 2000 10th Mediterranean Electrotechnical Conference. Information Technology and Electrotechnology for the Mediterranean Countries. Proceedings. MeleCon 2000 (Cat. No.00CH37099).

[13]  Sushmita Mitra,et al.  Neuro-fuzzy rule generation: survey in soft computing framework , 2000, IEEE Trans. Neural Networks Learn. Syst..

[14]  Bart Baesens,et al.  Using Neural Network Rule Extraction and Decision Tables for Credit - Risk Evaluation , 2003, Manag. Sci..

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

[16]  Alex A. Freitas,et al.  Extracting comprehensible rules from neural networks via genetic algorithms , 2000, 2000 IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks. Proceedings of the First IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks (Cat. No.00.

[17]  S. K. Salman,et al.  Windmill modeling consideration and factors influencing the stability of a grid-connected wind power-based embedded generator , 2002 .

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

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

[20]  M. M. Hand.  Variable-Speed Wind Turbine Controller Systematic Design Methodology: A Comparison of Non-Linear and Linear Model-Based Designs , 1999 .

[21]  Jacek M. Zurada,et al.  Extraction of rules from artificial neural networks for nonlinear regression , 2002, IEEE Trans. Neural Networks.

[22]  Giacomo Mantriota,et al.  Dynamic behaviour of wind power systems equipped with automatically regulated continuously variable transmission , 1996 .

[23]  Stephen I. Gallant,et al.  Connectionist expert systems , 1988, CACM.

[24]  Whei-Min Lin,et al.  Intelligent approach to maximum power point tracking control strategy for variable-speed wind turbine generation system , 2010 .

[25]  M. J. Verdonschot,et al.  Modeling and Control of wind turbines using a Continuously Variable Transmission , 2009 .

[26]  Huei-Lin Chang,et al.  Hour-ahead wind power and speed forecasting using simultaneous perturbation stochastic approximation (SPSA) algorithm and neural network with fuzzy inputs , 2010 .

[27]  Xin Ma,et al.  Adaptive Extremum Control and Wind Turbine Control , 1997 .

[28]  C. F. Moyano,et al.  An optimization approach for wind turbine commitment and dispatch in a wind park , 2009 .

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

[30]  Z. Litipu,et al.  Improve the reliability and environment of power system based on optimal allocation of WPG , 2004, IEEE PES Power Systems Conference and Exposition, 2004..

[31]  Haritza Camblong.  Minimisation de l'impact des perturbations d'origine éolienne dans la génération d'électricité par des aérogénérateurs à vitesse variable , 2003 .

[32]  Lucy Y. Pao,et al.  Control of wind turbines: Past, present, and future , 2009, 2009 American Control Conference.

[33]  Ahmad Taher Azar,et al.  Overview of Type-2 Fuzzy Logic Systems , 2012, Int. J. Fuzzy Syst. Appl..

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

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

[36]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

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

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

[39]  Sankar K. Pal,et al.  Data mining in soft computing framework: a survey , 2002, IEEE Trans. Neural Networks.

[40]  Lotfi Krichen,et al.  Electric power generation based on variable speed wind turbine under load disturbance , 2011 .

[41]  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).

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

[43]  Tomonobu Senjyu,et al.  An integrated control method for a wind farm to reduce frequency deviations in a small power system , 2011 .

[44]  Tzung-Pei Hong,et al.  Trade-off Between Computation Time and Number of Rules for Fuzzy Mining from Quantitative Data , 2001, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

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

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

[47]  Ahmad Taher,et al.  Adaptive Neuro-Fuzzy Systems , 2010 .

[48]  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).

[49]  Jude W. Shavlik,et al.  in Advances in Neural Information Processing , 1996 .

[50]  Asim Roy,et al.  On connectionism, rule extraction, and brain-like learning , 2000, IEEE Trans. Fuzzy Syst..

[51]  C. Lee Giles,et al.  Learning and Extracting Finite State Automata with Second-Order Recurrent Neural Networks , 1992, Neural Computation.

[52]  C. L. Giles,et al.  Heuristics for the extraction of rules from discrete-time recurrent neural networks , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[53]  Francisco Herrera,et al.  Genetic Fuzzy Systems - Evolutionary Tuning and Learning of Fuzzy Knowledge Bases , 2002, Advances in Fuzzy Systems - Applications and Theory.

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

[55]  Hamid Reza Momeni,et al.  A new hybrid optimal control for WECS using MLP Neural Network and Genetic neuro Fuzzy , 2011 .

[56]  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.

[57]  Whei-Min Lin,et al.  On-line designed hybrid controller with adaptive observer for variable-speed wind generation system , 2010 .

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

[59]  Terje Gjengedal,et al.  Large-scale Wind Power Farms as Power Plants , 2005 .

[60]  C. Lee Giles,et al.  Extraction, Insertion and Refinement of Symbolic Rules in Dynamically Driven Recurrent Neural Networks , 1993 .

[61]  E. L. van der Hooft,et al.  ESTIMATED WIND SPEED FEED FORWARD CONTROL FOR WIND TURBINE OPERATION OPTIMISATION , 2004 .

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

[63]  Ervin Bossanyi,et al.  The Design of closed loop controllers for wind turbines , 2000 .

[64]  Andrew Kusiak,et al.  Optimization of wind turbine energy and power factor with an evolutionary computation algorithm , 2010 .

[65]  J. Cotrell,et al.  Motion Technologies CRADA CRD-03-130: Assessing the Potential of a Mechanical Continuously Variable Transmission , 2004 .

[66]  Mohamed El Hachemi Benbouzid,et al.  High-Order Sliding-Mode Control of Variable-Speed Wind Turbines , 2009, IEEE Transactions on Industrial Electronics.

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

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

[69]  Zhi-Hua Zhou,et al.  A statistics based approach for extracting priority rules from trained neural networks , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[70]  Ian Witten,et al.  Data Mining , 2000 .

[71]  Hamid Reza Momeni,et al.  Optimal Intelligent Control for Wind Turbulence Rejection in WECS Using ANNs and Genetic Fuzzy Approach , 2012, SOCO 2012.

[72]  Reda Alhajj,et al.  A clustering algorithm with genetically optimized membership functions for fuzzy association rules mining , 2003, The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03..

[73]  Samir Kouro,et al.  Unidimensional Modulation Technique for Cascaded Multilevel Converters , 2009, IEEE Transactions on Industrial Electronics.

[74]  Sushmita Mitra,et al.  Fuzzy MLP based expert system for medical diagnosis , 1994, CVPR 1994.

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

[76]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

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

[78]  Shyi-Ming Chen,et al.  Generating Fuzzy Rules from Relational Database Systems for Estimating Null Values , 1997, Cybern. Syst..

[79]  Ashish Darbari,et al.  Rule Extraction from Trained ANN: A Survey , 2000 .