Kalman filter-based wind speed estimation for wind turbine control

Single ended primary inductance converter (SEPIC) is a type of DC-DC converter whose industrial applications include maximum power point tracking and Active power factor correction. Owing to present nonlinear circuit components, SEPIC has discontinuous nonlinearity and for the mathematical model determination, state space averaging or more advanced modeling techniques are required. Once the approximated nonlinear model is obtained, the states of the plant can be estimated using a nonlinear state estimator. Nonlinear estimators are preferred mostly when the real trajectory of the states cannot be estimated accurately by a linear state estimator and the deviation from the linear behavior and nonlinear one is above what is tolerable for the applications. Extended Kalman filter (EKF), being such a state estimator is widely employed in control and fault monitoring applications where a complete or an approximated nonlinear model of the plant is available. EKF especially operates well for the plants where the nonlinearity does not cause the assumptions behind EKF to be violated. In this study, first an approximated nonlinear model for SEPIC is determined. Using the model, EKF is designed to estimate the states of the plant. The efficacy of the designed EKF is demonstrated in a series of numerical MATLAB based simulations where the both open loop and closed loop performances are analyzed, and the results are detailed in the paper.

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“…29). Finally, state estimate using the Kalman filter gain weighted innovation value and state error covariance matrix is updated [22].…”

Section: Ekf Designmentioning

confidence: 99%

Design of extended Kalman filter for SEPIC converter and comparison to Kalman filter

Sel

Kasnakoğlu

2020

SN Appl. Sci.

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“…To design an EFK-based estimator, the concerned system has to be modeled in the following nonlinear form [33,34]:…”

Section: Non-standard Ekf-based Wind Speed Estimatormentioning

confidence: 99%

“…The standard EKF algorithm for a continuous-discrete nonlinear system is implemented in two steps [33,34]:…”

Section: Non-standard Ekf-based Wind Speed Estimatormentioning

confidence: 99%

A Comparison Study between Two MPPT Control Methods for a Large Variable-Speed Wind Turbine under Different Wind Speed Characteristics

Song

Yang

et al. 2017

Energies

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Abstract:Variable speed wind turbines (VSWTs) usually adopt a maximum power point tracking (MPPT) method to optimize energy capture performance. Nevertheless, obtained performance offered by different MPPT methods may be affected by the impact of wind turbine (WT)'s inertia and wind speed characteristics and it needs to be clarified. In this paper, the tip speed ratio (TSR) and optimal torque (OT) methods are investigated in terms of their performance under different wind speed characteristics on a 1.5 MW wind turbine model. To this end, the TSR control method based on an effective wind speed estimator and the OT control method are firstly presented. Then, their performance is investigated and compared through simulation test results under different wind speeds using Bladed software. Comparison results show that the TSR control method can capture slightly more wind energy at the cost of high component loads than the other one under all wind conditions. Furthermore, it is found that both control methods present similar trends of power reduction that is relevant to mean wind speed and turbulence intensity. From the obtained results, we demonstrate that, to further improve MPPT capability of large VSWTs, other advanced control methods using wind speed prediction information need to be addressed.

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“…To solve this problem of wind speed measurement, some researchers have worked on the estimation of wind speed using different techniques. Song et al in [2] proposed and compared two methods of wind speed estimation using different versions of the Kalman filter. The inconvenience of this methods resides in the fact that Kalman filters are complex and require high number of tuning parameters.…”

Section: Introductionmentioning

confidence: 99%

Doubly Fed Induction Generator Wind Turbine Control Based on a Novel MRAS Observer

Adekanle

Guisser²,

Abdelmounim³

et al. 2019

Proceedings of the Third International Conference on Computing and Wireless Communication Systems, ICCWCS 2019, April 24-25, 20

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This paper proposes a novel Doubly Fed Induction Generator (DFIG) control approach using an optimized set of observers. Firstly, an Adaptive Disturbance Rejection Scheme (ADRS) Model Reference Adaptive System (MRAS) observer is applied to estimate generator speed. Secondly, a Fuzzy Logic MPPT controller is designed to optimized power conversion. Finally to achieve the overall sensor-less control, speed, current and DC-voltage control are achieved using a novel version of perturbation observer while taking into account the power loss in the system. The technique is evaluated for a 1.5MW DFIG under variable wind speed, measurement noise, grid voltage dip and parameter variation and compared with the conventional PI-controller.

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Kalman filter-based wind speed estimation for wind turbine control

Cited by 51 publications

References 15 publications

Design of extended Kalman filter for SEPIC converter and comparison to Kalman filter

Design of extended Kalman filter for SEPIC converter and comparison to Kalman filter

A Comparison Study between Two MPPT Control Methods for a Large Variable-Speed Wind Turbine under Different Wind Speed Characteristics

Doubly Fed Induction Generator Wind Turbine Control Based on a Novel MRAS Observer

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