Independent blade pitch controller design for a three-bladed turbine using disturbance accommodating control

Wang, Na; Wright, Alan; Johnson, Kathryn

doi:10.1109/acc.2016.7525261

Cited by 17 publications

(11 citation statements)

References 22 publications

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“…As wind turbines increase in size, the loads on the different components of the turbine increase, which can reduce turbine lifetime [17,28]. This is, in part, due to the unbalanced and changing loads on the wind turbine rotor caused by wind shear, gravitational force, and atmospheric turbulence [17].…”

Section: Motivationmentioning

confidence: 99%

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Wind Turbine Collective and Individual Pitch Control Using Quantitative Feedback Theory

Wheeler

García-Sanz

2017

Volume 1: Aerospace Applications; Advances in Control Design Methods; Bio Engineering Applications; Advances in Non-Linear Cont

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Individual pitch control is an innovative technique in wind turbine control. It has the potential of reducing the asymmetric mechanical loads on the blades in large multi-megawatt turbines. As the mechanical fatigue is reduced, the lifetime of the turbine can be significantly extended. This work develops an individual pitch control for the National Renewable Energy Laboratory’s (NREL) 5 MW reference wind turbine. The individual pitch controller works along with a collective pitch controller, designed using Quantitative Feedback Theory (QFT) robust control. Simulations of the complete individual and collective pitch control system are conducted with the NREL’s computer-aided engineering tool for horizontal axis wind turbines (FAST). They show that the addition of the individual pitch controller significantly reduces the loads on the tilt and yaw directions in the nacelle and tower of the turbine at 1P and 3P frequencies, and on the blades at the 2P harmonic frequency.

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Section: Motivationmentioning

confidence: 99%

“…The turbine then requires less maintenance, has a longer lifetime, and is more reliable [27,28]. accommodating control [20,21,23,24,28]. However, the goal of individual pitch control is to reduce loads at specific frequencies, typically the 1p and 3p frequencies.…”

Section: Motivationmentioning

confidence: 99%

Wind Turbine Collective and Individual Pitch Control Using Quantitative Feedback Theory

Wheeler

García-Sanz

2017

Volume 1: Aerospace Applications; Advances in Control Design Methods; Bio Engineering Applications; Advances in Non-Linear Cont

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“…Statistical models use explanatory variables and historical/online information (measurements, log-data, etc. ), generally implementing recursive techniques, such as recursive least-squares or artificial neural networks (or deep learning) (Wang et al, 2016). In fact, forecasting is the field with the most deep-learning (and broadly artificial intelligence) applications in wind energy, e.g.…”

Section: Introductionmentioning

confidence: 99%

Model-free estimation of available power using deep learning

et al. 2021

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Abstract. In order to assess the level of power reserves during down-regulation, the available power of a wind turbine needs to be estimated. The current practice in available power estimation is heavily dependent on the pre-defined performance parameters of the turbine and the curtailment strategy followed. This paper proposes a single-input model-free approach dynamic estimation of the available power using recurrent neural networks. Accordingly, it combines wind turbine control considerations and modern forecasting methodologies for a model-free, single-input estimation of available power. It enables a robust real-time implementation of dynamic delta control, as well as higher-accuracy provision of the reserves to the system operators. The model-free approach requires only 1 Hz wind speed measurements as input and estimates 1 Hz available power as output. The neural network is trained, tested and validated using the DTU 10 MW reference wind turbine HAWC2 model under realistic atmospheric conditions. The unsteady patterns in the turbulent flow are represented via long short-term memory (LSTM) neurons which are trained during a period of normal operation. The adaptability of the network to changing inflow conditions is ensured via transfer learning, where the last LSTM layer is updated using new measurements. It is seen that the sensitivity of the networks to changing wind speed is much higher than that of turbulence, and the updates are to be implemented solely based on the altering inflow velocity. The validation of the trained LSTM networks on time series with 7, 9 and 11 m s−1 mean wind speeds demonstrates high accuracy (less than 1 % bias) and capability of transfer-learning online. Including highly turbulent inflow cases, the networks have shown to comply with the most recent grid codes, which require the quality of the available power estimations to be evaluated with high accuracy (less than 3.3 % standard deviation of the error around zero bias) at 1 min intervals.

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“…by using additional flow measurement sensors. The disturbance accommodation techniques that are presented in Wang et al . also achieve a small amount of load reduction for higher frequency loads.…”

Section: Introductionmentioning

confidence: 99%

Load reduction on a clipper liberty wind turbine with linear parameter‐varying individual blade pitch control

2017

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The increasing size of modern wind turbines also increases the structural loads caused by effects such as turbulence or asymmetries in the inflowing wind field. Consequently, the use of advanced control algorithms for active load reduction has become a relevant part of current wind turbine control systems. In this paper, an individual blade pitch control law is designed using multivariable linear parameter-varying control techniques. It reduces the structural loads both on the rotating and non-rotating parts of the turbine. Classical individual blade pitch control strategies rely on single-control loops with low bandwidth. The proposed approach makes it possible to use a higher bandwidth since it accounts for coupling at higher frequencies. A controller is designed for the utility-scale 2.5 MW Liberty research turbine operated by the University of Minnesota. Stability and performance are verified using the high-fidelity nonlinear simulation and baseline controllers that were directly obtained from the manufacturer. pitch control algorithm should not change the thrust and the torque of the turbine in order to maintain the power output at its desired level.Sophisticated, model-based state space control approaches also became possible by transforming the whole periodic system into the non-rotating frame. 7 Multivariable control designs were investigated in Bossanyi 2 on the basis of a linear quadratic regulator and in Geyler and Caselitz 8 and Vali et al. 9 on the basis of H 1 -norm optimal control. Adaptive control techniques for individual blade pitch control are presented in Magar et al. 10 Periodic control for load reduction using individual blade-pitch control was developed and tested in Stol et al. 11 These controllers aimed at the once-per-revolution (1P) blade loads and were shown to yield similar results as the classical two-SISO-loops control strategy. A direct extension of this control strategy to target loads at higher frequencies is addressed in van Engelen 12 and Petrović et al. 13 additionally using complex, higher-order transformations of the sensor data. Successful field test results of such controllers for two-bladed and three-bladed turbines is presented in Bossanyi et al. 14 However, in this case, a linear analysis of the closed-loop model to verify robustness and performance is not straightforward anymore. Further, it also requires several design steps instead of a single one. This is also true for the feedforward filters added in each of the two SISO loops to cope with the 3P loads on the nacelle proposed in Bossanyi. 15 The feedforward filters are designed using a constrained numerical optimizations technique. Because of these reasons, the classical two-SISO-loops control strategy serves for comparison in this paper. The reduction of higher frequency loads was also achieved in Barlas et al. 16 and Unguran and Kühn 17 by employing additional trailing edge flaps and in Larsen et al. 18 by using additional flow measurement sensors. The disturbance accommodation techniques that are presented in W...

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Independent blade pitch controller design for a three-bladed turbine using disturbance accommodating control

Abstract: NREL prints on paper that contains recycled content.

Cited by 17 publications

References 22 publications

Wind Turbine Collective and Individual Pitch Control Using Quantitative Feedback Theory

Wind Turbine Collective and Individual Pitch Control Using Quantitative Feedback Theory

Model-free estimation of available power using deep learning

Load reduction on a clipper liberty wind turbine with linear parameter‐varying individual blade pitch control

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