Kassem, A. (2012). FUNCTIONAL PREDICTIVE CONTROL DESIGN FOR A STAND ALONE WIND ENERGY CONVERSION SYSTEM. JES. Journal of Engineering Sciences, 40(No 2), 473-490. doi: 10.21608/jesaun.2012.113125
Ahmed M. Kassem. "FUNCTIONAL PREDICTIVE CONTROL DESIGN FOR A STAND ALONE WIND ENERGY CONVERSION SYSTEM". JES. Journal of Engineering Sciences, 40, No 2, 2012, 473-490. doi: 10.21608/jesaun.2012.113125
Kassem, A. (2012). 'FUNCTIONAL PREDICTIVE CONTROL DESIGN FOR A STAND ALONE WIND ENERGY CONVERSION SYSTEM', JES. Journal of Engineering Sciences, 40(No 2), pp. 473-490. doi: 10.21608/jesaun.2012.113125
Kassem, A. FUNCTIONAL PREDICTIVE CONTROL DESIGN FOR A STAND ALONE WIND ENERGY CONVERSION SYSTEM. JES. Journal of Engineering Sciences, 2012; 40(No 2): 473-490. doi: 10.21608/jesaun.2012.113125
FUNCTIONAL PREDICTIVE CONTROL DESIGN FOR A STAND ALONE WIND ENERGY CONVERSION SYSTEM
This paper investigates the application of the model predictive control (MPC) approach to control the voltage and frequency of a stand alone wind generation system. This scheme consists of a wind turbine which drives an induction generator feeding an isolated load. A static reactive power compensator (SVAR) is connected at the induction generator terminals to regulate the load voltage. The rotor speed, and thereby the load frequency are controlled via adjusting the mechanical power input using the blade pitch-angle control. The MPC is used to calculate the optimal control actions including system constraints. To alleviate computational effort and to reduce numerical problems, particularly in large prediction horizon, an exponentially weighted functional model predictive control (FMPC) is employed. Digital simulations have been carried out in order to validate the effectiveness of the proposed scheme. The proposed controller has been tested through step changes in the wind speed and the load impedance. Simulation results show that adequate performance of the proposed wind energy scheme has been achieved. Moreover, this scheme is robust against the parameters variation and eliminates the influence of modeling and measurement errors.
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