Model Predictive wind turbine control for load alleviation and power leveling in Extreme Operation conditions

Jassmann, Uwe; Zierath, János; Dickler, Sebastian; Abel, Dirk

doi:10.1109/cca.2016.7587997

Cited by 10 publications

(6 citation statements)

References 8 publications

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“…As soon the increase of rotor speed is stopped, the individual pitch control is slowly phased in again. The example demonstrates that the proposed MPC algorithm can satisfy the rate constraints (13) while also allowing the operation on the actuators' limits, that is, _ β i = M _ β in this example.…”

Section: Resultsmentioning

confidence: 86%

“…The examples so far demonstrated that the presented control algorithm can reduce 1P load component and adequately deal with the saturation constraints (14) when transitioning between two different operating regions by phasing out the individual pitch control when the wind speed becomes too low. The purpose of the following example is to illustrate how the proposed control algorithm deals with pitch rate constraints (13). To this end, the wind turbine is excited with an extreme operating gust according to the IEC 61400-1 standard, 35 as shown in Figure 8.…”

Section: Resultsmentioning

confidence: 99%

“…The use of the MPC to limit rotor speed, certain structural loads or fatigue has been investigated by Bottasso et al, 9 Schuurmans 10 and Petrović and Bottasso 11 . Benefits of using the MPC in extreme conditions have been demonstrated by Koerber 12 and Jassmann et al 13 Since the research in lidar technology suggested it may be possible to obtain future wind speed information, the benefits of including such information inside the MPC have been investigated by, for example, Spencer et al, 14 Laks et al 15 and Schlipf et al 16 The model predictive control for wind turbines that takes system uncertainties into account is described by Mirzaei et al, 17 and benefits of including wind turbine constraints directly in the control design are analysed by Henriksen et al 18 …”

Section: Introductionmentioning

confidence: 99%

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MPC framework for constrained wind turbine individual pitch control

Petrović

Jelavić²,

Baotić

2020

Wind Energy

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The reduction of structural loads is becoming an important objective for the wind turbine control system due to the ever-increasing specifications/demands on wind turbine rated power and related growth of turbine dimensions. Among various control algorithms that have been researched in recent years, the individual pitch control has demonstrated its effectiveness in wind turbine load reduction. Since the individual pitch control, like other load reduction algorithms, requires higher levels of actuator activity, one must take actuator constraints into account when designing the controller. This paper presents a method for the inclusion of such constraints into a predictive wind turbine controller. It is shown that the direct inclusion of constraints would result in a control problem that is nonconvex and difficult to solve. Therefore, a modification of the constraints is proposed that ensures the convexity of the control problem. Simulation results show that the developed predictive control algorithm achieves individual pitch control objectives while satisfying all imposed constraints.

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Section: Resultsmentioning

confidence: 86%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

MPC framework for constrained wind turbine individual pitch control

Petrović

Jelavić²,

Baotić

2020

Wind Energy

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“…The MPC system comprises an MPC algorithm and an extended Kalman filter (the state observer has already been described in detail in [13]). The MPC algorithm solves the online optimization problem Δu opt .…”

Section: Mpc For the 3 Mw Wind Turbinementioning

confidence: 99%

Full-scale field test of a model predictive control system for a 3 MW wind turbine

Dickler

Wintermeyer-Kallen

Zierath³

et al. 2021

Forsch Ingenieurwes

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Model predictive control (MPC) is a strong candidate for modern wind turbine control. While the design of model predictive wind turbine controllers in simulations has been extensively investigated in academic studies, the application of these controllers to real wind turbines reveals open research challenges. In this work, we focus on the validation of a linear time-variant MPC system for a 3 MW wind turbine in a full-scale field test. First, the study proves the MPC’s capability to control the real wind turbine in the partial load region. Compared to the turbine’s baseline PID controller, the MPC system offers similar results for the electrical power output and for the occurring mechanical loads. Second, the study validates a previously proposed, simulation-based rapid control prototyping process for a systematic MPC development. The systematic development process allows to completely design and parameterize the MPC system in a simulative environment independent of the real wind turbine. Through the rapid control prototyping process, the MPC commissioning in the wind turbine’s programmable logic controller can be realized within a few hours without any modifications required in the field. Thus, this study establishes the proof of concept for a linear time-variant MPC system for a 3 MW wind turbine in a full-scale field test and bridges the gap between the control design and field testing of MPC systems for wind turbines in the multi-megawatt range.

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“…However, to date, lidar systems can still account for a significant portion of the capital and operational expenditures of wind turbines (Canet et al, 2020). In order to avoid lidar-related costs or effort, 430 some studies are directed towards predictive control without explicit preview measurement (Evans et al, 2015;Jassmann et al, 2016). In this case, the wind prediction over the MPC horizon can for instance be generated via constant extrapolation of the instantaneous wind estimate at the rotor ("persistence").…”

Section: Benefit Of "Perfect Prediction" Vs "Perfect Persistence"mentioning

confidence: 99%

Lidar-assisted model predictive control of wind turbine fatigue via online rainﬂow-counting considering stress history

Loew

Bottasso

2021

Preprint

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Abstract. The formulation of Parametric Online Rainflow Counting implements the standard fatigue estimation process and a stress history in the cost function of a Model Predictive Controller. The formulation is tested in realistic simulation scenarios where the states are estimated by a Moving Horizon Estimator and the wind is predicted by a lidar simulator. The tuning procedure for the controller toolchain is carefully explained. In comparison to a conventional MPC in a turbulent wind setting, the novel formulation is especially superior with low lidar quality, benefits more from the availability of a wind prediction, and exhibits a more robust performance with shorter prediction horizons. A simulation excerpt with the novel formulation provides deeper insight into the update of the stress history and the fatigue cost parameters. Finally, in a deterministic gust setting, both the conventional and the novel MPC - despite their completely different fatigue cost - exhibit similar pitch behavior and tower oscillation.

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Model Predictive wind turbine control for load alleviation and power leveling in Extreme Operation conditions

Cited by 10 publications

References 8 publications

MPC framework for constrained wind turbine individual pitch control

MPC framework for constrained wind turbine individual pitch control

Full-scale field test of a model predictive control system for a 3 MW wind turbine

Lidar-assisted model predictive control of wind turbine fatigue via online rainﬂow-counting considering stress history

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Model Predictive wind turbine control for load alleviation and power leveling in Extreme Operation conditions

Cited by 10 publications

References 8 publications

MPC framework for constrained wind turbine individual pitch control

MPC framework for constrained wind turbine individual pitch control

Full-scale field test of a model predictive control system for a 3 MW wind turbine

Lidar-assisted model predictive control of wind turbine fatigue via online rainﬂow-counting considering stress history

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Product

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Full-scale field test of a model predictive control system for a 3 MW wind turbine