Hierarchal Control System for a Variable Speed Cage Machine Wind Generation Unit Using Neural Networks

Abstract

A hierarchal control strategy, that addresses three control objectives for a wind generation system, is proposed in this paper. It controls the local bus voltage (to avoid voltage rise), captures the maximum power in the wind and also minimizes the power loss in the induction generator. In the first level, given the instantaneous wind speed, electrical torque and output power, the designed neural networks calculate the desired rotor speed, air-gap flux and the grid side reactive power. In the second level, the desired current wave shapes (instantaneous three-phase currents) of the rectifier and the inverter in a double-sided PWM converter system are calculated. In the third level, the PWM controller guides the system towards the optimum operating conditions. Simulation results show that even as the wind speed changes randomly, the proposed control strategy leads the system to the optimum operating conditions.

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