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Neural Predictive Control of a Doubly Fed Induction Generator

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Doubly-Fed Induction Generators (DFIGs) are widely used in large wind turbines. This is primarily due to the many advantages DFIGs offer over other types of generators in applications where the mechanical power provided by the prime mover varies greatly. Power control of the DFIG usually uses a Proportional Integral Decoupled control PI-D strategy due to its simplicity of implementation. However, the design of its fixed gains is very complex, since it depends on the exact mathematical model of the generator. To handle this problem, this paper investigates an efficient and robust control method for the DFIG in order to control the active and reactive power flows. The basic idea of an Artificial Neural Network Generalized Predictive Controller (ANNGPC) is to operate in two modes: prediction and control. It generates a sequence of future control signals within each sampling interval to optimize the control effort. The NNGPC performances are compared in terms of reference tracking, sensitivity to perturbations and robustness against parameters variations.
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Artificial Neural Network; DFIG; Generalized Predictive Controller; PI-D Controller

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