Predictive Power Control for a Linearized Doubly Fed Induction Generator Model

The paper is concerned with developing a predictive power control (PPC) approach for a linearized doubly fed induction generator (DFIG). The main contribution of the proposed control procedure is illustrated via reducing the inherent nonlinearity of the DFIG model through introducing new state variables (only four) and through adopting the hysteresis predictive control (HPC) instead of using the proportional integrators (PI) which are usually exist in vector control procedures. The simplicity of the proposed PPC is enhanced through eliminating the pulse width modulation via utilizing the finite control set (FCS) as a voltage vector selection procedure. In order to increase the efficiency of the generation unit (DFIG), a loss minimization methodology (LMM) is proposed based on deriving the optimal reactive power reference. For validating the feasibility of the proposed PPC and the LMM, the DFIG dynamic performance under the proposed PPC is compared with that obtained under applying the classic predictive power control. The obtained results confirm the validity of the proposed PPC in achieving full decoupling between the active and reactive power components at steady-state and transient operation as well. Moreover, the LMM confirms its effectiveness in minimizing the DFIG losses (copper and iron) which results in improving the drive efficiency.