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Control algorithms for wind turbines are traditionally designed on the basis of (linearized) dynamic models. On the accuracy of such models depends hence the performance of the control, and validating the dynamic models is an essential requirement for achieving the optimum design. The aim of this work is to identify, at different wind speeds, the d...
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... identified ARX model is then parameterized by this optimal parameter matrix P . It can be theoretically shown that the identified model is unbiased under reasonable assumptions [5] . A schematic system identification setup is depicted in Figure 1 . Typical inputs are the (collective) blade pitch angle θ and generator setpoint T g , and outputs are generator speed Ω and tower fore-aft v nod and sidewards v nay velocities (or accelerations). The blocks K and K in the feedback loops represent the generator and the pitch controllers, respectively, which are not required in the Direct identification methods, presented in Section 2.1 . Time series of these typical inputs and outputs allow the identification of the transfer functions from θ to v nod , from T g to Ω , and from T g to v nay , from which the tower fore-aft, tower side-to-side and drive train dynamics can be analyzed. The frequency range where the models can be accurately identified depends on the bandwidth of the excitation signals: r θ (on the blade pitch) and/or r g (on the generator). When the frequency and damping of the first tower mode need to be identified, the bandwidth should at least include the expected first tower frequency. When the first drive-train mode is needed, the excitation bandwidth must at least include the first drive-train frequency. Hence, the proper choice of excitation signals is key-important for achieving informative experiment under reasonable amount of excitation. Two opposite objectives exist indeed, and a trade-off should be made. On the one hand, a good excitation for system identification can be achieved by choosing a high energy excitation signal with wide flat spectrum. On the other hand, the system limitations (such as hardware limits, loads, etc.) impose the use of low-energy, narrow bandwidth excitation. The design of excitation signals should therefore prescribe that ( a ) the signals remain within the hardware limits, ( b ) the additional loads are as small as possible, and ( c ) the dynamic models are still accurately identified. For the considered wind turbine specifically, the excitation signals r θ and r g have been designed in such a way, that no unacceptable loads are induced, the excited pitch demand has acceptable speed and acceleration, and the electric power remains within acceptable limits. To this ...
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Citations
... Dynamic modeling, control design [11][12][13] Subspace identification Dynamic modeling [14][15][16] Mechanism-oriented Modeling (white-box) ...
... RLS parameter identification Dynamic modeling [23] Optimization-based parameter identification Dynamic modeling [24,25] Data-driven modeling of input-output characteristics is a common way, including machinelearning [7][8][9][10], standard-model-set approximation [11][12][13] and subspace identification [14][15][16]. In [7][8][9][10], machine learning algorithms, such as artificial neural network (ANN), support vector machine (SVM), random forest and deep neural network were useful for black-box modeling. ...
... If optimization is used, computation burden is non-negligible. In [11][12][13], model structures such as ARX (auto-regressive), ARMAX (auto-regressive moving average), BJ (Box-Jenkins) and OE (Output-Error) were adopted for identification with LS (least square) or PEM (prediction-error method) criterion, where PRBS (pseudo-random binary excitation signal) was often used as input excitation. To get numerical solution, it usually has requirements about forms and amplitudes of excitation signals, open-loop or closed-loop structure and sampling period, etc. ...
With increasing size and flexibility of modern grid-connected wind turbines, advanced control algorithms are urgently needed, especially for multi-degree-of-freedom control of blade pitches and sizable rotor. However, complex dynamics of wind turbines are difficult to be modeled in a simplified state-space form for advanced control design considering stability. In this paper, grey-box parameter identification of critical mechanical models is systematically studied without excitation experiment, and applicabilities of different methods are compared from views of control design. Firstly, through mechanism analysis, the Hammerstein structure is adopted for mechanical-side modeling of wind turbines. Under closed-loop control across the whole wind speed range, structural identifiability of the drive-train model is analyzed in qualitation. Then, mutual information calculation among identified variables is used to quantitatively reveal the relationship between identification accuracy and variables’ relevance. Then, the methods such as subspace identification, recursive least square identification and optimal identification are compared for a two-mass model and tower model. At last, through the high-fidelity simulation demo of a 2 MW wind turbine in the GH Bladed software, multivariable datasets are produced for studying. The results show that the Hammerstein structure is effective for simplify the modeling process where closed-loop identification of a two-mass model without excitation experiment is feasible. Meanwhile, it is found that variables’ relevance has obvious influence on identification accuracy where mutual information is a good indicator. Higher mutual information often yields better accuracy. Additionally, three identification methods have diverse performance levels, showing their application potentials for different control design algorithms. In contrast, grey-box optimal parameter identification is the most promising for advanced control design considering stability, although its simplified representation of complex mechanical dynamics needs additional dynamic compensation which will be studied in future.
... In addition, a closed-loop method is employed to identify wind turbine's model utilizing predictor-based system identification [35]. Finally, [36] applies two different system identification methods on a wind turbine to extract modal information at different operational conditions through experimental modal analysis in which input/output signals were measured and through operational modal analysis in which only output signals were measured. In this paper, the nonlinear model of HWPTSs that operate on a wide range is linearized to construct the model bank. ...
Hydraulic wind power transfer systems exhibit a highly nonlinear dynamic influenced by system actuator hysteresis and disturbances from wind speed and load torque. This paper presents a system identification approach to approximate such a nonlinear dynamic. Piecewise affine (PWA) models are obtained utilizing the averaged nonlinear models of hysteresis in a confined space. State-space representation of PWA models is obtained over the allocated operating point clusters. The experimental results demonstrate a close agreement with that of the simulated. The experimental results and simulation show more than 91% match.
... Stability analysis methods use some appropriate linear model of the system, capable of representing with sufficient fidelity its response in the proximity of a given operating condition. If the model is assumed to be linear time invariant (LTI), then stability can be readily assessed by LTI stability theory, for which a variety of methods is readily available [1, 2, 3, 4, 5]. However, wind turbine models are more appropriately characterized by periodic rather than time invariant coefficients [6] . ...
In this work, a new method is proposed for the stability analysis of wind turbines. The method uses input–output time histories obtained by conducting virtual excitation experiments with a suitable wind turbine simulation model. Next, a single-input/single-output periodic reduced model is identified from the recorded response and used for a stability analysis conducted according to the Floquet theory. Since only input–output sequences are used, the approach is model independent in the sense that it is applicable to wind turbine simulation models of arbitrary complexity. The use of the Floquet theory reveals a much richer picture than the one obtained by widespread classical approaches based on the use of the multi-blade coordinate transformation of Coleman. In fact, it is shown here that, for each principal mode computed by the classical approach, there are in reality infinite super-harmonics of varying strength fanning out from the principal one at multiples of the rotor speed. The relative strength of each harmonic in a fan provides for a way of measuring how periodically one specific fan of modes behaves. The notion of super-harmonics allows one to justify the presence of peaks in the response spectra, peaks that cannot be explained by the classical time-invariant analysis. The Campbell diagram, i.e., the plot of system frequencies vs. rotor speed, is in this work enriched by the presence of the super-harmonics, revealing a much more complex pattern of possible resonant conditions with the per-rev excitations than normally assumed. Copyright © 2014 John Wiley & Sons, Ltd.
... • Controls: Use the OMA based system identification for better controller design by using results from system identification and extracting linear models from real experiments and compare them with linear models extracted from the simulations. This is important since control performance is dependent on accuracy of these linear models and hence it's important to validate these models for achieving optimal control [5]. ...
Previous works by the authors have shown that though Operational Modal Analysis (OMA) techniques are suitable for global dynamic
analysis of a wind turbine under parked conditions, there are several issues in their application to operational wind turbines.
These issues including time varying nature of the structure, presence of harmonic content in the loading (due to rotor rotation),
considerable aerodynamic damping etc. prevent straightforward application of OMA to operational wind turbines. The authors
have further proposed a strategy to combat these issues and modify OMA methodology to tune it for operational wind turbines.
A successful implementation of this strategy was employed and demonstrated to work satisfactorily on simulated vibration response
data for a 3MW wind turbine. Work presented in the current paper in an extension of the pervious work and describes the details
of the measurement campaign aimed at identifying modal parameters of ALSTOM’s ECO 100 wind turbine. Since measuring on an
operational wind turbine is a challenging job in itself, the paper also describes measurement planning and execution phases.
The paper illustrates various key aspects related to practical measurements on an actual wind turbine and underlines the importance
of proper planning and experiment design. The importance of a priori knowledge provided by finite element model based simulations
is also underlined.
This paper deals with the problem of wind turbine tower damping control design and implementation in situations where the support structure parameters vary from their nominal design values. Such situations can, in practice, occur for onshore and especially offshore wind turbines and are attributed to aging, turbine installation, scour or marine sand dunes phenomena and biofouling. Practical experience of wind turbine manufacturing industry has shown that such effects are most easily quantified in terms of the first natural frequency of the turbine support structure. The paper brings forward a study regarding the amount to which nominal tower damping controller performance is affected by changes in the turbine natural frequency. Subsequently, an adaptive tower damping control loop is designed using linear parameter-varying control synthesis; the proposed tower damping controller depends on this varying parameter which is assumed throughout the study to be readily available. An investigation of the fatigue load reduction performance in comparison with the original tower damping control approach is given for a generic three-bladed horizontal-axis wind turbine. Copyright
