Overcoming fundamental limitations of wind turbine individual blade pitch control with inflow sensors

Jones, Bernard J. T.; Lio, Wai Hou; Rossiter, J.A.

doi:10.1002/we.2205

Cited by 15 publications

(15 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Barlas et al (2012) also considered an extension of the MPC strategy that measured the local inflow of the blade section. Jones et al (2018) use a blade-mounted pitot tube in addition to the blade root strain gauges as an addition to an IPC strategy. The use of the inflow sensor in a cascaded configuration helps improve the performance of the controller by bypassing the limitations of the slow blade dynamics on the controller input.…”

Section: Sensors For Active Flap Controlmentioning

confidence: 99%

“…They find that the results of the blade optimization are comparable if the blade is optimized with a fatigue-focused flap controller or if the blade is optimized without flaps altogether. Chen et al (2017) use an optimization algorithm to optimize the blade of the NREL 5 MW Reference Wind Turbine (Jonkman et al, 2009) so that the levelized cost of energy is minimized. The algorithm also optimizes a model-based controller used for pitch and TE flap control (Chen et al, 2016).…”

Section: Active Flap Fatigue Control and Blade Design Driving Loadsmentioning

confidence: 99%

“…Because sensor and actuator are far apart, aerodynamic loads and deflections at the blade tip are sensed at the root with a considerable time lag. This time lag carries through to the actuation response of the controller, limiting its effectiveness (Andersen et al, 2010;Fisher and Madsen, 2016;Jones et al, 2018). Inflow sensors would be an attractive sensor choice were it not for the practical drawbacks of cost (e.g., lidar) or susceptibility to the elements (e.g., pitot tubes and pressure sensors) in their implementation (Cooperman and Martinez, 2015).…”

Section: Active Flap Control For Reducing Extreme Blade Loads and Deflectionsmentioning

confidence: 99%

“…The used structural model in FAST is ElastoDyn. It has a combined multi-body and modal dynamics representation that is able to model the wind turbine with flexible blades and tower (Jonkman, 2003).…”

Section: Fastmentioning

confidence: 99%

See 3 more Smart Citations

Active flap control with the trailing edge flap hinge moment as a sensor: using it to estimate local blade inflow conditions and to reduce extreme blade loads and deflections

2021

View full text Add to dashboard Cite

Abstract. Active trailing edge flaps are a promising technology that can potentially enable further increases in wind turbine sizes without the disproportionate increase in loads, thus reducing the cost of wind energy even further. Extreme loads and critical deflections of the blade are design-driving issues that can effectively be reduced by flaps. In this paper, we consider the flap hinge moment as a local input sensor for a simple flap controller that reduces extreme loads and critical deflections of the DTU 10 MW Reference Wind Turbine blade. We present a model to calculate the unsteady flap hinge moment that can be used in aeroelastic simulations in the time domain. This model is used to develop an observer that estimates the local angle of attack and relative wind velocity of a blade section based on local sensor information including the flap hinge moment of the blade section. For steady wind conditions that include yawed inflow and wind shear, the observer is able to estimate the local inflow conditions with errors in the mean angle of attack below 0.2∘ and mean relative wind speed errors below 0.4 %. For fully turbulent wind conditions, the observer is able to estimate the low-frequency content of the local angle of attack and relative velocity even when it is lacking information on the incoming turbulent wind. We include this observer as part of a simple flap controller to reduce extreme loads and critical deflections of the blade. The flap controller's performance is tested in load simulations of the reference turbine with active flaps according to the IEC 61400-1 power production with extreme turbulence group. We used the lifting line free vortex wake method to calculate the aerodynamic loads. Results show a reduction of the maximum out-of-plane and resulting blade root bending moments of 8 % and 7.6 %, respectively, when compared to a baseline case without flaps. The critical blade tip deflection is reduced by 7.1 %. Furthermore, a sector load analysis considering extreme loading in all load directions shows a reduction of the extreme resulting bending moment in an angular region covering 30∘ around the positive out-of-plane blade root bending moment. Further analysis reveals that a fast reaction time of the flap system proves to be critical for its performance. This is achieved with the use of local sensors as input for the flap controller. A larger reduction potential of the system is identified but not reached mainly because of a combination of challenging controller objectives and the simple controller architecture.

show abstract

Section: Sensors For Active Flap Controlmentioning

confidence: 99%

Section: Active Flap Fatigue Control and Blade Design Driving Loadsmentioning

confidence: 99%

Section: Active Flap Control For Reducing Extreme Blade Loads and Deflectionsmentioning

confidence: 99%

Section: Fastmentioning

confidence: 99%

See 2 more Smart Citations

Active flap control with the trailing edge flap hinge moment as a sensor: using it to estimate local blade inflow conditions and to reduce extreme blade loads and deflections

2021

View full text Add to dashboard Cite

show abstract

“…More research is desperately needed into alternative bioresins and thermoplastics (Murray et al, 2017) which can be reused, along with natural reinforcements, such as bamboo and sisal. Other innovations include segmented blades to reduce costs and difficulties in transport (Peeters et al, 2017), and individual control of blade pitch (Jones et al, 2018).…”

mentioning

confidence: 99%

Grand Challenges in Wind Energy Research

Wood

2020

Front. Energy Res.

View full text Add to dashboard Cite

This is a brief summary of the author's views on the important future directions of wind energy research. It is impossible for a single researcher to provide a comprehensive review of a multidimensional subject but the recent publication of excellent and extensive multi-authored reviews, in 2019 (Veers et al., 2019) and 2016 (Van Kuik et al., 2016), is taken as justification for a more personal account. The author, moreover, is an engineer and will not cover issues associated with the societal context of wind energy. These range from the many aspects of noise which will be briefly mentioned here, to the intrusion of large wind turbines on isolated rural life (Aeon). One of the most important conferences in wind energy is the "Torque" series. Torque 2020 was to be held at the Technical University of Delft at the end of May but was eventually run electronically in October. It had seven themes which are listed below in decreasing order of the number of abstracts accepted for oral or poster presentation:

show abstract

Repetitive model predictive control for load alleviation on a research wind turbine using trailing edge flaps

et al. 2022

View full text Add to dashboard Cite

This paper presents the results of an advanced control strategy that employs active trailing edge flaps to reduce the fatigue loads of an experimental wind turbine. The strategy, called repetitive model predictive control, is a multiple-input multipleoutput controller that aims at the alleviation of out-of-plane blade root bending moments. The strategy incorporates the control commands, output errors, and state deviation from the previous rotation. This way, the time lag in the strain sensor input due to the blade inertia is compensated. Additionally, a strategy to limit the computational costs is presented. The load alleviation performance is evaluated at different yaw cases and compared with different individual flap control strategies. The repetitive model predictive control is able to reduce the fatigue loads by up to 23% compared with the better performing individual flap control strategy. This improvement in load reduction is accompanied by an increase in flap travel of up to 7% compared with the individual flap control strategies. K E Y W O R D S fatigue load control, individual flap control, repetitive control, trailing edge flaps | INTRODUCTIONWind energy has become one of the most important sources of renewable energy. In order for this trend to continue, the cost of energy of this technology needs to be as low as possible. A crucial factor for the cost of energy is the employed material for building a turbine. Lowering material input can be achieved by reducing the loads acting on the turbine components. These in turn can be decreased by a load control strategy specifically designed for this purpose. As an example, cyclic and individual pitch control has been studied by various institutes. [1][2][3][4] Substantial research was also carried out into smart rotor control which could supplement pitching of the rotor blades. In particular, employing locally distributed active flow control devices on the blades has drawn a lot of attention in the community. Due to their high bandwidth and control authority, trailing edge flaps are one of the most promising active flow control options. 5 In particular, it was shown that trailing edge flaps achieve the same load reduction for fatigue loads as individual pitch control. 3 A critical aspect of active load control is the choice of sensors for the control strategy. 6 Mostly, active flow control aims at the alleviation of the out-of-plane turbine loads. Therefore, employing a feedback control on the out-of-plane blade root bending moment is a common choice.

show abstract

Overcoming fundamental limitations of wind turbine individual blade pitch control with inflow sensors

Cited by 15 publications

References 43 publications

Active flap control with the trailing edge flap hinge moment as a sensor: using it to estimate local blade inflow conditions and to reduce extreme blade loads and deflections

Active flap control with the trailing edge flap hinge moment as a sensor: using it to estimate local blade inflow conditions and to reduce extreme blade loads and deflections

Grand Challenges in Wind Energy Research

Repetitive model predictive control for load alleviation on a research wind turbine using trailing edge flaps

Contact Info

Product

Resources

About