Adaptive controller for PMSG wind turbine systems with back-to-back PWM converters

This paper presents an adaptive control strategy for wind energy conversion systems. The control scheme uses a B-spline artificial neural network for tuning controllers when the system is subjected to disturbances. Voltage-source converter is controlled in a synchronous orthogonal d-q frame by an adaptive PI controller. The B-spline neural network must be able to enhance the system performance through online updating parameters. Thus, the paper proposes the use of adaptive PI controllers to regulate the current, frequency, and DC link voltage. MatLab is employed for simulation studies to verify the performance of the proposed strategy.

[1]  W. Marsden.  I and J , 2012 .

[2]  Jian Chen,et al.  Decoupling current control for voltage source converter in synchronous roating frame , 2001, 4th IEEE International Conference on Power Electronics and Drive Systems. IEEE PEDS 2001 - Indonesia. Proceedings (Cat. No.01TH8594).

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

[4]  Reza Iravani,et al.  Voltage-Sourced Converters in Power Systems: Modeling, Control, and Applications , 2010 .

[5]  Ervin Bossanyi,et al.  Wind Energy Handbook , 2001 .

[6]  Jesus Lopez.  Doubly Fed Induction Machine , 2011 .

[7]  Luis Marroyo,et al.  Introduction to A Wind Energy Generation System , 2011 .

[8]  Neville R. Watson,et al.  Self-Commutating Converters for High Power Applications , 2009 .

[9]  M. Sanada,et al.  Sensorless output maximization control for variable-speed wind generation system using IPMSG , 2003, IEEE Transactions on Industry Applications.

[10]  Lei Tian,et al.  Wind Turbine Control Strategy at Lower Wind Velocity Based on Neural Network PID Control , 2009, 2009 International Workshop on Intelligent Systems and Applications.

[11]  J.M. Ramirez,et al.  Details on the implementation of a conventional StatCom’s control , 2008, 2008 IEEE/PES Transmission and Distribution Conference and Exposition: Latin America.

[12]  Laszlo Gyugyi,et al.  Understanding FACTS: Concepts and Technology of Flexible AC Transmission Systems , 1999 .

[13]  O.P. Malik,et al.  Transmission line distance relaying using on-line trained neural networks , 2005, IEEE Transactions on Power Delivery.

[14]  Lei Tian,et al.  Pitch angle control of variable pitch wind turbines based on neural network PID , 2009, 2009 4th IEEE Conference on Industrial Electronics and Applications.

[15]  Olimpo Anaya-Lara,et al.  Wind Energy Generation: Modelling and Control , 2009 .

[16]  Zhe Chen,et al.  A Review of the State of the Art of Power Electronics for Wind Turbines , 2009, IEEE Transactions on Power Electronics.

[17]  J. Rocabert,et al.  Grid synchronization for advanced power processing and FACTS in wind power systems , 2010, 2010 IEEE International Symposium on Industrial Electronics.

[18]  M.R. Iravani,et al.  A Control Strategy for a Distributed Generation Unit in Grid-Connected and Autonomous Modes of Operation , 2008, IEEE Transactions on Power Delivery.

[19]  Reza Iravani,et al.  Voltage-Sourced Converters in Power Systems: Modeling, Control, and Applications , 2010 .

[20]  D. Popovic,et al.  Stability of a MV distribution network with electronically interfaced distributed generation , 2004 .