Control-oriented modelling of wind turbines using a Takagi-Sugeno model structure

Georg, Sören; Schulte, Horst; Aschemann, Harald

doi:10.1109/fuzz-ieee.2012.6251302

Cited by 50 publications

(29 citation statements)

References 14 publications

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“…With reference to Figure 6, the force F T generates the tower displacement y T , which is modelled by the mechanical model of mass m T , with spring and damper parameters k T and d T , respectively. For the tower, the equivalent translational stiffness parameter is derived by means of a direct stiffness method common in structural mechanics calculations [18,19]. In the same way, the blade displacement y B generated by the force F T is described again as a mass m B with a spring and damper model, whose parameters are k B and d B , respectively.…”

Section: Wind Turbine Tower and Blade Modelsmentioning

confidence: 99%

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Overview of Modelling and Advanced Control Strategies for Wind Turbine Systems

Simani

2015

Energies

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Abstract:The motivation for this paper comes from a real need to have an overview of the challenges of modelling and control for very demanding systems, such as wind turbine systems, which require reliability, availability, maintainability, and safety over power conversion efficiency. These issues have begun to stimulate research and development in the wide control community particularly for these installations that need a high degree of "sustainability". Note that this represents a key point for offshore wind turbines, since they are characterised by expensive and/or safety critical maintenance work. In this case, a clear conflict exists between ensuring a high degree of availability and reducing maintenance times, which affect the final energy cost. On the other hand, wind turbines have highly nonlinear dynamics, with a stochastic and uncontrollable driving force as input in the form of wind speed, thus representing an interesting challenge also from the modelling point of view. Suitable control methods can provide a sustainable optimisation of the energy conversion efficiency over wider than normally expected working conditions. Moreover, a proper mathematical description of the wind turbine system should be able to capture the complete behaviour of the process under monitoring, thus providing an important impact on the control design itself. In this way, the control scheme could guarantee prescribed performance, whilst also giving a degree of "tolerance" to possible deviation of characteristic properties or system parameters from standard conditions, if properly included in the wind turbine model itself. The most important developments in advanced controllers for wind turbines are also briefly referenced, and open problems in the areas of modelling of wind turbines are finally outlined.

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Section: Wind Turbine Tower and Blade Modelsmentioning

confidence: 99%

“…On the other hand, the drivetrain consisting of rotor, shaft and generator is modelled as a two-mass inertia system, including shaft torsion, where the two inertias are connected with a torsional spring with spring constant k S and a torsional damper with damping constant d S , as illustrated in Figure 7 [19].…”

Section: Wind Turbine Tower and Blade Modelsmentioning

confidence: 99%

Overview of Modelling and Advanced Control Strategies for Wind Turbine Systems

Simani

2015

Energies

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“…This form is well suited for a transformation into a Takagi-Sugeno (TS) form [15] proposed in [17]. Here, the nonlinear term g g g(x x x, v) is first written as a product of a matrix and the state vector x x x and then reformulated into a TS structure using the sector nonlinearity approach [18].…”

Section: Drive Train Concept and Reduced-order Modelmentioning

confidence: 99%

“…Therefore, the DOFs of the model are the collective horizontal tip displacements of the rotor blade tips in a flap-wise direction y B , the displacement of tower top in the wind direction denoted by y T and the rotor and generator rotational angle θ r and θ g . The drive train is modeled by two rigid bodies joined with a torsionally elastic coupling, as described in [17]. The equations of motion of the four DOF yields four coupled differential equations.…”

Section: Drive Train Concept and Reduced-order Modelmentioning

confidence: 99%

Fault Tolerant and Optimal Control of Wind Turbines with Distributed High-Speed Generators

Giger¹,

Kühne

Schulte

2017

Energies

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Abstract:In this paper, the control scheme of a distributed high-speed generator system with a total amount of 12 generators and nominal generator speed of 7000 min −1 is studied. Specifically, a fault tolerant control (FTC) scheme is proposed to keep the turbine in operation in the presence of up to four simultaneous generator faults. The proposed controller structure consists of two layers: The upper layer is the baseline controller, which is separated into a partial load region with the generator torque as an actuating signal and the full-load operation region with the collective pitch angle as the other actuating signal. In addition, the lower layer is responsible for the fault diagnosis and FTC characteristics of the distributed generator drive train. The fault reconstruction and fault tolerant control strategy are tested in simulations with several actuator faults of different types.

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“…Moreover, the nonlinear functions involved in the modeling of the vehicle suspension studied here are rather given in terms of identified nonlinear force characteristics than by using analytic relationships, cf. Georg et al (2012). We organized the paper as follows: Section 2 gives a general introduction to TS modeling and observer design.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear State Estimation in Suspension Control Based on Takagi-Sugeno Model

Pletschen

Badur

2014

IFAC Proceedings Volumes

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We present a new nonlinear state estimation approach based on Kalman filter theory and Takagi-Sugeno (TS) modeling for an active vehicle suspension application in this paper. The nonlinear state equations of a so-called hybrid suspension configuration, which result from nonlinear spring and damping characteristics, are exactly represented by means of a continuoustime TS system, i. e. a convex combination of local linear state space models. We derive observer gain matrices for each linear subsystem on the basis of standard Kalman filter theory, before we construct the global observer for the overall nonlinear system. Convergence of the global observer is ensured in terms of linear matrix inequality conditions. We then study the estimation performance of the TS Kalman filter in simulations and experiments on a hybrid quarter-car test rig using a measured road profile as disturbance input. The approach achieves a high estimation accuracy of well above 90 % in the simulation and 70 − 90 % in the experiments.

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Control-oriented modelling of wind turbines using a Takagi-Sugeno model structure

Cited by 50 publications

References 14 publications

Overview of Modelling and Advanced Control Strategies for Wind Turbine Systems

Overview of Modelling and Advanced Control Strategies for Wind Turbine Systems

Fault Tolerant and Optimal Control of Wind Turbines with Distributed High-Speed Generators

Nonlinear State Estimation in Suspension Control Based on Takagi-Sugeno Model

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