Adaptive PID Control of Wind Energy Conversion Systems Using RASP1 Mother Wavelet Basis Function Networks

In this paper a PID control strategy using neural network adaptive RASP1 wavelet for WECS-s control is proposed. It is based on single layer feedforward neural networks with hidden nodes of adaptive RASP1 wavelet functions controller and an infinite impulse response (IIR) recurrent structure. The IIR is combined by cascading to the network to provide double local structure resulting in improving speed of learning. This particular neuro PID controller assumes a certain model structure to approximately identify the system dynamics of the unknown plant (WECS-s) and generate the control signal. The results are applied to a typical turbine/generator pair, showing the feasibility of the proposed solution.

Authors:

• M. Sedighizadeh
• A. Rezazadeh

Keywords:

• Adaptive PID Control
• RASP1 Wavelets
• WindEnergy Conversion Systems.

References:

[1] P.Puleston, "Control strategies for wind energy conversion systems",
Ph.D. dissertation, Univ. La Plata, Argentina, 1997.
[2] P. Simoes, B. K. Bose, and R. J. Spiege, "Fuzzy logic-based intelligent
control of a variable speed cage machine wind generation system,",
IEEE Trans. Power Electron., Vol. 12, no. 1, Jan. 1997.
[3] F. D. Kanellos, N. D. Hatziargyriou, "A new control scheme for variable
speed wind turbine using neural networks", IEEE Power Engineering
Society Winter Meeting, 2002, Vol.1, 27-31Jan.2002.
[4] S. Li, D. C. Wunsch, E. A. O-Hair, "Using neural networks to estimate
wind turbine power generation",IEEE Transaction on energy conversion,
Vol. 16, No.3, Sept 2001.
[5] S. Haykin, Neural Networks, A Comprehensive Foundation. New York:
Macmillan, 1994.
[6] M. A. Mayosky, G. I. E. Cancelo, "Direct adaptive control of wind
energy conversion systems using gaussian networks", IEEE Transactions
on neural networks, Vol. 10, No. 4, july 1999.
[7] X. Ye, N. K. Loh,"Dynamic system identification using recurrent radial
basis function network," Proce. American control conf. , Vol. 3,June
1993.
[8] G. Lekutai, "Adaptive Self-Tuning Neuro Wavelet Network
Controllers"; PhD Thesis, Virginia Polytechnic Institute and State
Universitym, 1997.
[9] A.U. Levin and K.S. Narendra, "Control of nonlinear dynamical, PII:
Obserabiliy, Identification and control," IEEE Trans. On Neural
Network,1995
[10] "Data base of wind characteristics", downloaded from
www.windata.com