Improving efficiency of two-type maximum power point tracking methods of tip-speed ratio and optimum torque in wind turbine system using a quantum neural network

In this paper, a quantum neural network (QNN) is used as controller in the adaptive control structures to improve efficiency of the maximum power point tracking (MPPT) methods in the wind turbine system. For this purpose, direct and indirect adaptive control structures equipped with QNN are used in tip-speed ratio (TSR) and optimum torque (OT) MPPT methods. The proposed control schemes are evaluated through a battery-charging windmill system equipped with PMSG (permanent magnet synchronous generator) at a random wind speed to demonstrate transcendence of their effectiveness as compared to PID controller and conventional neural network controller (CNNC).

[1]  A. Piccolo,et al.  Designing an Adaptive Fuzzy Controller for Maximum Wind Energy Extraction , 2008, IEEE Transactions on Energy Conversion.

[2]  A. Sideris,et al.  A multilayered neural network controller , 1988, IEEE Control Systems Magazine.

[3]  Dinesh Kumar Jain,et al.  Isolated Operation of Variable Speed Driven PMSG for Wind Energy Conversion System , 2009 .

[4]  Chee Wei Tan,et al.  A review of maximum power point tracking algorithms for wind energy systems , 2012 .

[5]  N. Matsui,et al.  A network model based on qubitlike neuron corresponding to quantum circuit , 2000 .

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

[7]  Nobuyuki Matsui,et al.  Qubit neural network and its learning efficiency , 2005, Neural Computing & Applications.

[8]  Mona N. Eskander,et al.  Fuzzy logic control based maximum power tracking of a wind energy system , 2001 .

[9]  Mohamed Abid,et al.  A Fuzzy-PI control to extract an optimal power from wind turbine , 2013 .

[10]  A. Boudghene Stambouli,et al.  A variable speed wind generator maximum power tracking based on adaptative neuro-fuzzy inference system , 2011, Expert Syst. Appl..

[11]  Mariusz Malinowski,et al.  Comparison of maximum peak power tracking algorithms for a small wind turbine , 2013, Math. Comput. Simul..

[12]  Hui Li,et al.  Neural-network-based sensorless maximum wind energy capture with compensated power coefficient , 2004, IEEE Transactions on Industry Applications.

[13]  Romeo Ortega,et al.  Adaptive passivity-based control for maximum power extraction of stand-alone windmill systems , 2012 .

[14]  Nobuyuki Matsui,et al.  A Multi-Layerd Feed-Forward Network Based on Qubit Neuron Model , 2002 .

[15]  Nobuyuki Matsui,et al.  An Examination of Qubit Neural Network in Controlling an Inverted Pendulum , 2005, Neural Processing Letters.

[16]  F. Valenciaga,et al.  High-Order Sliding Control for a Wind Energy Conversion System Based on a Permanent Magnet Synchronous Generator , 2008, IEEE Transactions on Energy Conversion.

[17]  Tian Lei,et al.  A Gaussian RBF Network Based Wind Speed Estimation Algorithm for Maximum Power Point Tracking , 2011 .

[18]  Pierluigi Siano,et al.  Exploiting maximum energy from variable speed wind power generation systems by using an adaptive Takagi-Sugeno-Kang fuzzy model , 2009 .

[19]  Nobuyuki Matsui,et al.  Learning performance of neuron model based on quantum superposition , 2000, Proceedings 9th IEEE International Workshop on Robot and Human Interactive Communication. IEEE RO-MAN 2000 (Cat. No.00TH8499).

[20]  N. Matsui,et al.  Control for swing-up of an inverted pendulum using qubit neural network , 2002, Proceedings of the 41st SICE Annual Conference. SICE 2002..

[21]  Chee Wei Tan,et al.  A study of maximum power point tracking algorithms for wind energy system , 2011, 2011 IEEE Conference on Clean Energy and Technology (CET).

[22]  Thomas Ackermann,et al.  Wind Power in Power Systems , 2005 .

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

[24]  Abbas Hooshmand Viki,et al.  A comparative study of maximum power extraction strategies in PMSG wind turbine system , 2009, 2009 IEEE Electrical Power & Energy Conference (EPEC).

[25]  Chih-Ming Hong,et al.  Development of intelligent MPPT (maximum power point tracking) control for a grid-connected hybrid power generation system , 2013 .

[26]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[27]  Nobuyuki Matsui,et al.  Image Compression by Layered Quantum Neural Networks , 2002, Neural Processing Letters.

[28]  K. Rajambal,et al.  Modeling and performance analysis of a small scale direct driven PMSG based wind energy conversion systems , 2011 .