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Doubly fed induction generators are widely adopted for the wind turbine systems since it is cheap and reliable. Based on the traditional stator voltage oriented vector control method, the performance of the proposed vector control is largely influenced by the variations of the DFIG parameters. And the classical PID algorithm cannot achieve the maximum power point tracking (MPPT) in time (owing to the transient wind). Hence, in this paper, to eliminate parameters variations on the power output and capture the MPPT rapidly, we propose a stator voltage oriented vector control which is based on a Neural Network PID (NNPID) technology. The weights which are being similar to the PID coefficients are adapted by Hebb rule to decrease the power error online according to the error gradient descent method, while the classical PID coefficients will be a constant. The effectiveness of the proposed method is demonstrated by corresponding simulation results: even in the case of wind mutation change, the proposed NNPID can track the variation of the wind energy, and robust to the DFIG parameters variations.

NNPID-based Stator Voltage Oriented Vector Control for DFIG based Wind Turbine Systems