Recurrent wavelet neural network control of a PMSG system based on a PMSM wind turbine emulator

A recurrent wavelet neural network (NN)-controlled 3-phase permanent magnet synchronous generator system (PMSG), which is direct-driven by a permanent magnet synchronous motor (PMSM) based on a wind turbine emulator, is proposed to control the output values of a rectifier (or AC to DC power converter) and inverter (or DC to AC power converter) in this study. First, a closed-loop PMSM drive control based on a wind turbine emulator is designed to generate the maximum power for the PM synchronous generator (PMSG) system according to different wind speeds. Next, the rotor speed of the PMSG, the DC bus voltage, and the current of the power converter are detected simultaneously to yield a better power output of the converter through DC bus power control. Because the PMSG system is a nonlinear and time-varying dynamic system, one online-trained recurrent wavelet NN controller is developed for the tracking controller of the DC bus power to improve the control performance in the output end of the rectifier. Additionally, another online-trained recurrent wavelet NN controller is also developed for tracking the controller of the AC power to improve the control performance in the output end of the inverter. Finally, some experimental results are verified to show the effectiveness of the proposed recurrent wavelet NN-controlled PMSG system direct-driven by a PMSM based on a wind turbine emulator.

Anahtar Kelimeler:

Permanent magnet synchronous motor, recurrent wavelet neural network, permanent magnet synchronous generator, rectifier, inverter

Recurrent wavelet neural network control of a PMSG system based on a PMSM wind turbine emulator

Keywords:

Permanent magnet synchronous motor, recurrent wavelet neural network, permanent magnet synchronous generator, rectifier, inverter,

PDF

___

K. Tan, S. Islam, “Optimum control strategies in energy conversion of PMSG wind turbine system without
mechanical sensors”, IEEE Transactions on Energy Conversion, Vol. 19, pp. 392–400, 2004.
M. Kolhe, J.C. Joshi, D.P. Kothari, “Performance analysis of a directly coupled photovoltaic water-pumping
system”, IEEE Transactions on Energy Conversion, Vol. 19, pp. 613–618, 2004.
G.K. Andersen, C. Klumpner, S.B. Kjaer, F. Blaabjerg, “A new green power inverter for fuel cells”, Proceedings of
the IEEE 33rd Annual Power Electronics Specialists Conference, pp. 727–733, 2002.
Z. Lubosny, Wind Turbine Operation in Electric Power Systems, Berlin, Springer, 2003.
T. Ackermann, Wind Power in Power Systems, New York, Wiley, 2005.
M. Karrari, W. Rosehart, O.P. Malik, “Comprehensive control strategy for a variable speed cage machine wind
generation unit”, IEEE Transactions on Energy Conversion, Vol. 20, pp. 415–423, 2005.
I. Boldea, Synchronous Generators, Boca Raton, FL, USA, Taylor and Francis, 2006.
M. Chinchilla, S. Arnaltes, I.C. Burgos, “Control of permanent magnet generators applied to variable-speed wind
energy systems connected to the grid”, IEEE Transactions on Energy Conversion, Vol. 21, pp. 130–135, 2006.
S. Sajedi, F. Kahlifeh, T. Karimi, Z. Khalifeh, “Maximum power point tracking of variable speed wind energy
conversion system”, International Journal of Physical Sciences, Vol. 6, pp. 6843–6851, 2011.
F. Gharedaghi, H. Jamali, M. Deysi, A Khalili, “Maximum power point tracking of variable speed wind generation system connected to permanent magnet synchronous generator”, International Review of Electrical Engineering, Vol. 4, pp. 1044–1049, 2011.
B. Delyon, A. Juditsky, A. Benveniste, “Accuracy analysis for wavelet approximations”, IEEE Transactions on
Neural Networks, Vol. 6, pp. 332–348, 1995.
C.F. Chen, C.H. Hsiao, “Wavelet approach to optimizing dynamic systems”, IEE Proceedings – Control Theory
and Applications, Vol. 146, pp. 213–219, 1999.
Q. Zhang, A. Benveniste, “Wavelet networks”, IEEE Transactions on Neural Networks, Vol. 3, pp. 889–898, 1992.
J. Zhang, G.G. Walter, Y. Miao, W.N.W. Lee, “Wavelet neural networks for function learning”, IEEE Transactions
on Signal Processing, Vol. 43, pp. 1485–1496, 1995.
Z. Zhang, C. Zhao, “A fast learning algorithm for wavelet network and its application in control”, Proceedings of IEEE International Conference on Control Automation, pp. 1403–1407, 2007.
H.L. Wei, S.A. Billings, “A unified wavelet-based modelling framework for nonlinear system identification: the
WANARX model structure”, International Journal of Control, Vol. 77, pp. 351–366, 2004.
H.L. Wei, S.A. Billings, M.A. Balikhin, “Wavelet based nonparametric NARX models for nonlinear input-output
system identification”, International Journal of Systems Science, Vol. 37, pp. 1089–1096, 2006.
M. Ravan, R.K. Amineh, M. Karrari, W.B. Rosehart, O.P. Malik, “Synchronous machine model identification using continuous wavelet NARX network”, Proceedings of the Institution of Mechanical Engineers – Part I: Journal of Systems and Control Engineering, Vol. 223, pp. 467–477, 2009.
J. Xu, D.W.C. Ho, D. Zhou, “Adaptive wavelet networks for nonlinear system identification”, Proceedings of the American Control Conference, pp. 3472–3473, 1997.
N. Sureshbabu, J.A. Farrell, “Wavelet-based system identification for nonlinear control”, IEEE Transactions on
Automatic Control, Vol. 44, pp. 412–417, 1999.
S.A. Billings, H.L. Wei, “A new class of wavelet networks for nonlinear system identification”, IEEE Transactions
on Neural Networks, Vol. 16, pp. 862–874, 2005.
R.H. Abiyev, O.Kaynak, “Fuzzy wavelet neural networks for identification and control of dynamic plants—a novel
structure and a comparative study”, IEEE Transactions on Industrial Electronics, Vol. 55, pp. 3133–3140, 2008.
D. Giaouris, J.W. Finch, O.C. Ferreira, R.M. Kennel, G.M. El-Murr, “Wavelet denoising for electric drives”, IEEE
Transactions on Industrial Electronics, Vol. 55, pp. 543–550, 2008. D. Gonzalez, J.T. Bialasiewicz, J. Balcells, J. Gago, “Wavelet-based performance evaluation of power converters operating with modulated switching frequency”, IEEE Transactions on Industrial Electronics, Vol. 55, pp. 3167– 3176, 2008.
F.J. Lin, R.J. Wai, M.P. Chen, “Wavelet neural network control for linear ultrasonic motor drive via adaptive sliding-mode technique”, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, Vol. 50, pp. 686–697, 2003.
A.T. ¨Ozdemir, K. Danı¸sman, “Fully parallel ANN-based arrhythmia classifier on a single-chip FPGA: FPAAC”, Turkish Journal of Electrical Engineering & Computer Sciences, Vol. 19, pp. 667–687, 2011.
A.A. Kulaksız, R. Akkaya, “Training data optimization for ANNs using genetic algorithms to enhance MPPT
efficiency of a stand-alone PV system”, Turkish Journal of Electrical Engineering & Computer Sciences, Vol. 20, pp. 241–254, 2012.
M. Moazzami, R.A. Hooshmand, “Short-term nodal congestion price forecasting in a large-scale power market using
ANN with genetic optimization training”, Turkish Journal of Electrical Engineering & Computer Sciences, Vol. 20, pp. 751–768, 2012.
H. Bouzari, H. Moradi, E. Bouzari, “Adaptive neuro-wavelet system for the robust control of switching power supplies”, IEEE International Multitopic Conference, pp. 1–6, 2008.
K. Funahashi, Y. Nakamura, “Approximation of dynamical systems by continuous time recurrent neural network”,
Neural Networks, Vol. 6, pp. 801–806, 1993.
L. Jin, P.N. Nikiforuk, M. Gupta, “Approximation of discrete-time state-space trajectories using dynamic recurrent networks”, IEEE Transactions on Automatic Control, Vol. 6. pp. 1266–1270, 1995.
C.C. Ku, K.Y. Lee, “Diagonal recurrent neural networks for dynamical system control”, IEEE Transactions on Neural Networks, Vol. 6, pp. 144–156, 1995.
C.H. Lu, C.C. Tsai, “Adaptive predictive control with recurrent neural network for industrial processes: an
application to temperature control of a variable-frequency oil-cooling machine”, IEEE Transactions on Industrial Electronics, Vol. 55, pp. 1366–1375, 2008.
P. Brandstetter, M. Kuchar, I. Neborak, “Selected applications of artificial neural networks in the control of AC induction motor drives”, International Review of Electrical Engineering, Vol. 4, pp. 1084–1093, 2011.
M. Ghariani, I.B. Salah, M. Ayadi, R. Neji, “Neural induction machine observer for electric vehicle applications”, International Review of Electrical Engineering, Vol. 3, pp. 314–324, 2010.
S.J. Yoo, J.B. Park, Y.H. Choi, “Stable predictive control of chaotic systems using self-recurrent wavelet neural network”, International Journal of Automatic Control Systems, Vol. 3, pp. 43–55, 2005.
S.J. Yoo, Y.H. Choi, J.B. Park, “Generalized predictive control based on self-recurrent wavelet neural network for stable path tracking of mobile robots: adaptive learning rates approach”, IEEE Transactions on Circuits and Systems I, Vol. 53, pp. 1381–1394, 2006.
C.H. Lu, “Design and application of stable predictive controller using recurrent wavelet neural networks”, IEEE Transactions on Industrial Electronics, Vol. 56, pp. 3733–3742, 2009. C.H. Chen, C.F. Hsu, “Recurrent wavelet neural backstepping controller design with a smooth compensator”, Neural Computation and Applications, Vol. 19, pp. 1089–1100, 2010.
S.I. Han, J.M. Lee, “Adaptive dynamic surface control with sliding mode control and RWNN for robust positioning
of a linear motion stage”, Mechatronics, Vol. 22, pp. 222–238, 2012.
F.J. Lin , L.T. Teng, M.H. Yu, “Radial basis function network control with improved particle swarm optimization
for induction generator system”, IEEE Transactions on Power Electronics, Vol. 23, pp. 2157–2169, 2008.
Y. Oussar, G. Dreyfus, “Initialization by selection for wavelet network training”, Neurocomputing, Vol. 34, pp. 131–143, 2000.