Dynamic state estimation of a doubly fed induction generator based on a comprehensive nonlinear model

Shahriari, Sayyed Ali Akbar; Raoofat, Mahdi; Mohammadi, Mohammad; Dehghani, Maryam; Saad, Maarouf

doi:10.1016/j.simpat.2016.09.005

Cited by 11 publications

(7 citation statements)

References 27 publications

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“…v r and v t refer to the rotor and turbine speed in per unit, respectively. Similar to Shahriari et al (2016aShahriari et al ( , 2016b, T m is obtained by equation (30):…”

Section: Doubly Fed Induction Generator Drive Train Modelmentioning

confidence: 99%

“…Although accurate modelling and estimation of DFIGs improve monitoring and control of DFIG, it has not been studied sufficiently. Pérez et al (2012), Malakar et al (2014), Shahriari et al (2016b) and Shahriari et al (2018) are some of the studies on this subject. Pérez et al (2012) and Malakar et al (2014) consider a sixth-order model for DFIG and neglect most of the state variables, especially those related to the controllers and grid-side filter current.…”

Section: Introductionmentioning

confidence: 99%

“…This deficiency is shown by comparing the estimation results of our DFIG model with results of Malakar et al (2014). A 15th-order model is considered for DFIG in Shahriari et al (2016b), which does not consider the DC link voltage and some DFIG controllers as the state variables. So, it is not possible to estimate the DC link voltage by the model presented in Shahriari et al (2016b).…”

Section: Introductionmentioning

confidence: 99%

“…A 15th-order model is considered for DFIG in Shahriari et al (2016b), which does not consider the DC link voltage and some DFIG controllers as the state variables. So, it is not possible to estimate the DC link voltage by the model presented in Shahriari et al (2016b). Shahriari et al (2018) uses the dynamic estimation technique to enhance lowvoltage ride-through capability of DFIG.…”

Section: Introductionmentioning

confidence: 99%

“…However, Shahriari et al (2018) consider an 11th-order model for DFIG and cannot estimate the DC link voltage and the state variables related to the grid-side controllers. Furthermore, Shahriari et al (2016b) and Shahriari et al (2018) use SFO for voltage control scheme.…”

Section: Introductionmentioning

confidence: 99%

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

Shahriari

2020

COMPEL

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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.

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“…v r and v t refer to the rotor and turbine speed in per unit, respectively. Similar to Shahriari et al (2016aShahriari et al ( , 2016b, T m is obtained by equation (30):…”

Section: Doubly Fed Induction Generator Drive Train Modelmentioning

confidence: 99%