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This paper aims to propose an 18th-order nonlinear model for doubly fed induction generator (DFIG) wind turbines. Based on the proposed model, which is more complete than the models previously developed, an extended Kalman filter (EKF) is used to estimate the DFIG state variables.

State estimation is a popular approach in power system control and monitoring because of minimizing measurement noise level and obtaining non-measured state variables. To estimate all state variables of DFIG wind turbine, it is necessary to develop a model that considers all state variables. So, an 18th-order nonlinear model is proposed for DFIG wind turbines. EKF is used to estimate the DFIG state variables based on the proposed model.

An 18th-order nonlinear model is proposed for DFIG wind turbines. Furthermore, based on the proposed model, its state variables are estimated. Simulation studies are done in four cases to verify the ability of the proposed model in the estimation of state variables under noisy, wind speed variation and fault condition. The results demonstrate priority of the proposed model in the estimation of DFIG state variables.

Evaluating DFIG model to estimate its state variables precisely.

The purpose of this study is to propose a control scheme based on state estimation algorithm to improve zero or low-voltage ride-through capability of permanent magnet synchronous generator (PMSG) wind turbine.

Based on the updated grid codes, during and after faults, it is necessary to ensure wind energy generation in the network. PMSG is a type of wind energy technology that is growing rapidly in the network. The control scheme based on extended Kalman filter (EKF) is proposed to improve the low voltage ride-through (LVRT) capability of the PMSG. In the control scheme, because the state estimation algorithm is applied, the requirement of DC link voltage measurement device and generator speed sensor is removed. Furthermore, by applying this technique, the extent of possible noise on measurement tools is reduced.

In the proposed control scheme, zero or low-voltage ride-through capability of PMSG is enhanced. Furthermore, the requirement of DC link voltage measurement device and generator speed sensor is removed and the amount of possible noise on the measurement tools is minimized. To evaluate the ability of the proposed method, four different cases, including short and long duration short circuit fault close to PMSG in the presence and absence of measurement noise are studied. The results confirm the superiority of the proposed method.

This study introduces EKF to enhance LVRT capability of a PMSG wind turbine.