Modelling and dynamic state estimation of a doubly fed induction generator wind turbine

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

Evaluating DFIG model to estimate its state variables precisely.

Citation

Shahriari, S.A.A. (2020), "Modelling and dynamic state estimation of a doubly fed induction generator wind turbine", COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, Vol. 39 No. 6, pp. 1393-1409. https://doi.org/10.1108/COMPEL-07-2019-0277