A Review of Floating Wind Turbine Controllers - Springer Link

A Review of Floating Wind Turbine Controllers H. Namik and K. Stol

Abstract A review of the most important contributions made towards designing and testing controllers for floating wind turbines is presented in this chapter. These controllers range in complexity, description detail, simulation results and testing styles, and floating platform structure. Furthermore, they have not yet been compared against each other quantitatively using the same simulation conditions. However, the attributes of each control approach documented in the literature are listed in this chapter. Several approaches that deal with the reduction of platform pitch damping are discussed such as the use of individual blade pitching and tuned mass dampers.

1 Introduction A recent trend in the wind turbine industry is to install the wind turbines offshore mainly due to better wind resource attributes when compared to onshore wind; these include [10, 31, 34]: stronger and steadier wind; less turbulence resulting in longer turbine life; less vertical shear (i.e. higher wind speeds at lower altitudes) and higher annual mean wind speed. On average offshore winds are 20 % faster. However, offshore winds also have greater extremes, interact with waves, and are harder to measure. In addition to having better offshore wind resource attributes, placing wind turbines offshore has several design and location advantages; these include [13, 34]:

H. Namik (&) K. Stol Department of Mechanical Engineering, The University of Auckland, Auckland, New Zealand e-mail: [email protected] K. Stol e-mail: [email protected]

P. M. Pardalos et al. (eds.), Handbook of Wind Power Systems, Energy Systems, DOI: 10.1007/978-3-642-41080-2_13, Springer-Verlag Berlin Heidelberg 2013

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H. Namik and K. Stol

• Turbines operate at maximum efficiency since no restrictions are imposed due to noise and other regulations. • Proximity to load centres (large cities) thus requiring less transmission distance and access less heavily used transmission lines. • Reduced visual impact. • Energy balance: An offshore turbine can recover the energy cost used during manufacture, transport, installation, operation, maintenance, and decommissioning in 3 months. Over its lifetime, the turbine can generate over 100 times that energy. • Offset emissions: As the contribution of wind energy increases the emissions for power generation are reduced. For example, if 10 % of the UK’s power comes from wind energy then UK’s annual emissions are reduced by 15 %. • Economics: Although going offshore has considerable increase in foundation cost, it was shown by the Danish wind industry that offshore wind turbines are feasible [34]. However, the foundation costs increase with increasing depth. Placing the turbines offshore has the following challenges [14]: increased capital cost due to foundations, integration with the electrical network, and installation, operation and maintenance being subject to weather conditions. The offshore wind energy potential is very large. For example, the UK offshore energy potential is estimated to be 986 TWh/year while demand is estimated to be 321 TWh/year (based on 2003 estimates); i.e. more than three times the demand [34]. The US energy potential for winds between 10 and 100 km offshore is estimated to be more than 900 GW which is more than the currently installed generation capacity; however, much of this potential is located in deep water that is deeper than 30 m [19]. Currently, the deepest installed fixed-bottom offshore wind turbine is the Beatrice wind farm off the coast of Scotland [1]; it is constructed in 44 m deep water. However, for water deeper than 60 m the most feasible option is to have floating wind turbines [24, 31]. A main challenge of floating wind turbines is that the motions induced by wind and wave conditions are almost impossible to eliminate. Therefore, the design of these turbines must take into account the added degrees of freedom due to platform motions. Hence, the control system can play an important role in reducing these motions. There are three main floating wind turbine concepts. Each concept uses a different principle to achieve static stability. The three floating concepts (shown in Fig. 1) are: a buoyancy stabilised barge platform, a mooring line stabilised Tension Leg Platform (TLP), and a ballast stabilised spar-buoy. Of course, each concept has its advantages and limitations. In June of 2009, the world’s first floating wind turbine, based on the spar-buoy concept with a 2.3 MW wind turbine, was installed off the coast of Norway in 220 m deep water [5]. Early comparisons of all three platforms used simple static or dynamic models that usually excluded the effect of the control system [11, 12, 23, 31, 39].

A Review of Floating Wind Turbine Controllers

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Fig. 1 The three main floating concepts

In this chapter, a brief review of the most important contributions made towards designing and testing controllers for floating wind turbines is presented. Section 2 describes some of the tools used to simulate and test the developed controllers on floating wind turbines. Properties of the main floating platforms and a nominal 5 MW wind turbines are also listed. Differences between collective and individual blade pitching mechanisms from a controls point of view are discussed in Sect. 3. Section 4 reviews several controllers implemented specifically on floating wind turbine platforms. Finally, main conclusions and an outlook into the future of floating controllers are presented in Sect. 5.

2 Simulation Tools and Models Several simulation and analysis codes have been developed or modified to handle the addition of the floating degrees of freedom (DOFs) and the interaction with the incident waves. In phase IV of the offshore code comparison collaboration (OC3) project1 [32] within the International Energy Agency (IEA) Wind Task 23 subtask 2, the major codes (listed in Table 1) are compared against each other for a floating 1

The OC3 project started in 2005 and the last phase, phase IV, was finished and results published in 2010.

GH

MSC ? NREL ? LUH Risø-DTU IFE-UMB MARINTEK DNV

Bladed

ADAMS HAWC2 3Dfloat Simo SESAM/DeepC

NREL ? LUH Risø-DTU IFE-UMB MARINTEK Acciona energia ? NTNU

GH

NREL ? POSTECH

OC3 participant

Airy+ airy wave theory, + with free surface corrections BEM blade-element/momentum DLL external dynamic link library DNV det norsk veritas DS dynamic stall DTU technical university of Denmark FEMP finite-element method P for mode processing only GDW generalised dynamic wake GH Garrad Hassan and partners limited IFE institute for energy technology LUH Leibniz university of Hannover MBS multibody-dynamics formulation ME Morison’s equation MSC MSC software corporation NREL National renewable energy laboratory NTNU the Norwegian university of science and technology PF linear potential flow with radiation and diffraction POSTECH Pohang university of science and technology QSCE quasi-static catenary equations SM interface to Simulink with MATLAB UD implementation through user-defined subroutines available UDFD implementation through user-defined force–displacement relationships UMB the Norwegian university of life sciences

NREL

FAST

Code developer

(BEM or GDW) ? DS (BEM or GDW) ? DS (BEM or GDW) BEM None

(BEM or GDW) ? DS

(BEM or GDW) ? DS

Aerodynamics

Airy ? ME, Airy ? PF ? ME Airy ? ME Airy ? ME Airy ? PF ? ME Airy+ ? ME, Airy ? PF ? ME

+

DLL, UD DLL, UD, SM UD DLL None

DLL

DLL, UD, SM

Airy+ ? ME, Airy ? PF+ (Airy ? or Stream) ? ME

Control system (servo)

Hydrodynamics

Table 1 Participating floating wind turbine simulation codes in the OC3 project (Adapted from [18])

Turbine: FEMP ? (Modal/MBS), moorings: QSCE Turbine: FEMP ? (modal/MBS), moorings: UDFD Turbine: MBS moorings: QSCE, UDFD Turbine: MBS/FEM, moorings: UDFD Turbine: FEM, moorings: FEM, UDFD Turbine: MBS, moorings: QSCE, MSB Turbine: MBS, moorings: QSCE, FEM

Structural dynamics (elastic)

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A Review of Floating Wind Turbine Controllers Table 2 NREL 5 MW turbine model details

419

Power rating

5 MW

Rotor orientation Control

Upwind Variable speed, variable pitch, active yaw 126, 3 m 90 m 12.1, 1173.7 rpm Pitch to feather 8 deg/s 43,093 Nm 47,402 Nm

Rotor, hub diameter Hub height Rated rotor, generator speed Blade operation Maximum blade pith rate Rated generator torque Maximum generator torque

configuration in a multinational team spanning multiple countries, universities, research institutes and industry. In phase IV of the OC3 project, the design codes are compared against each other under a specified set of load cases designed to test different model features of the floating system. Results show that most design codes agree across the simulation load cases. Some differences were identified which lead to ‘‘greater understanding of offshore floating wind turbine dynamics and modelling techniques, and better knowledge of the validity of various approximations’’ used by different design codes [18].

2.1 The 5 MW Wind Turbine For feasible offshore deployment of wind turbines, the minimum power rating of wind turbines is 5 MW [23]. Table 2 lists the properties of a generic 5 MW wind turbine created by Jonkman et al. [17] and used by most of the offshore wind turbine control community; the turbine is commonly known as the ‘‘NREL Offshore 5 MW Baseline Wind Turbine’’. The wind turbine is a fictitious 5 MW machine with its properties based on a collection of existing wind turbines of similar rating.

2.2 Floating Platform Models The main properties of the three floating platforms (shown in Fig. 1) are listed in Table 3. The ITI Energy barge is a rectangular platform designed to be cost effective and easy to install on site. However, the barge is very sensitive to incident waves since most of the structure is above the water; i.e. it rides the waves rather than pass through them. The result is large platform translations and rotations which directly affect the turbine tower loads and power fluctuations. Conversely,

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Table 3 Main properties of the floating platforms Barge platform Tension leg platform Width Length Height Draft Water depth Platform mass

40 m 40 m 10 m 4m 150 m 5.59106 kg

Diameter

Spar-buoy platform

18 m

Diameter above taper Diameter below taper Freeboard Draft 47.89 m Draft Water depth 200 m Water depth Platform mass 8.69106 kg Platform mass

6.5 m 9.4 m 10 m 120 m 320 m 7.59106 kg

the MIT/NREL TLP experiences very little motion compared to the barge platform but the cost of the anchors for the tensioned mooring lines cannot always be justified. Finally, the OC3-Hywind spar-buoy has a cylindrical deep drafted hull with catenary mooring lines. Like the TLP, most of the platform structure is underwater and therefore will not be as sensitive to the incident waves as the barge platform. Having catenary mooring lines means that the spar-buoy experiences a larger motion envelope than the TLP; however, the spar-buoy is generally more cost effective than the TLP due to lower anchoring system costs.

3 Collective and Individual Blade Pitching There are several actuators available for use by a control system on wind turbines in general. These include: blade pitch, generator torque, turbine yaw drive, passive devices such as tuned mass dampers. For floating systems, passive devices such as oscillating water column can also be used to absorb energy from the waves. For active control, the most commonly used actuators are the blade pitch angle (operated either collectively or individually) and the applied generator torque. In this section, the physical mechanism behind collective blade pitching (CBP) and individual blade pitching (IBP) are described. The objective is to illustrate the differences between the two pitching schemes in terms of implementation, effectiveness at regulating multiple control objectives, and limitations with an emphasis on their impact on floating wind turbines.

3.1 Collective Blade Pitching Collective blade pitch (CBP) control is widely used in wind turbine control simply because it provides the necessary actuation required for rotor speed control. It works by changing the symmetric thrust and torque loads on the rotor. It is also


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easy to implement since all three blades are commanded the same pitch angle and hence combining the actuation of the three blades into one single rotor actuator; useful for single-input single-output (SISO) control. However, when multiple objectives are to be regulated, collective blade pitching may not always provide the necessary actuation without sacrificing the regulation of other objectives or affecting other uncontrolled and/or un-modelled turbine DOFs. With regards to floating offshore wind turbines, a major limitation of CBP is conflicting blade pitch commands issued by the control system with multiple objectives [whether implemented as separate SISO loops or a single multi-input multi-output (MIMO) controller] [28]. The objectives that conflict with each other in terms of actuator demand are rotor speed regulation and platform pitch motion regulation. Below is a description of how these conflicting blade pitch commands are issued based on physical insight into the floating system. For simplicity, assume there are two separate control loops: rotor speed controller and platform pitch controller. For restoring platform pitch, the most useful force generated by the blades is rotor thrust. Since the blades cannot influence the platform pitch angle directly but only through the thrust force, we will consider the effects to a change in platform pitching velocity on the turbine in equilibrium. Consider a forward pitching velocity shown in Fig. 2a (negative pitch velocity according to the coordinate system). To keep the turbine in its equilibrium position (not necessarily upright due to the effect of constant wind), the platform pitch controller must generate a positive restoring pitch moment; this can be achieved by increasing the aerodynamic thrust of each blade collectively where it will create a positive pitching moment about the pitch axis as shown in Fig. 2a. Therefore, the blade pitch angles will have to be decreased. Now, consider the effect of the same pitching velocity on the rotor speed. As the platform pitch controller decreases the blade pitch to restore platform pitch, it is also accelerating the rotor by generating additional aerodynamic torque. This is coupled by the fact that as the turbine pitches forward, the wind speed relative to the blade increases thus further accelerating the rotor. Observing an increase in rotor speed, the rotor speed controller commands the blades to reduce the generated torque by increasing the collective blade pitch angle to reduce aerodynamic efficiency of the blades. Therefore, the two objectives of speed and platform pitch regulation are competing for commanded blade pitch.

3.2 Individual Blade Pitching Individual blade pitch (IBP) control where each blade is commanded independently uses a different mechanism from CBP control. It creates asymmetric aerodynamic loads in addition to the symmetric loads created by collective pitching thus enhancing the platform pitch restoring moment [28]. Due to the periodic nature of wind turbines, implementing IBP control results in time variant gains that vary periodically with the rotor azimuth. That is, the controller ‘‘knows’’

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(a)


(b)

Fig. 2 Platform pitch restoring forces with different blade pitch operation: a collective, b individual

how the effectiveness of the blade changes as it rotates and acts accordingly to create the necessary restoring forces/moments to achieve its control objectives. There are many ways to achieve individual blade pitch control depending on the control objectives. Multi-blade coordinate (MBC) transformation (also known as Coleman transformation) captures periodic properties of a system in a linear timeinvariant (LTI) model—useful for control design—by transforming the rotating DOFs into a non-rotating frame of Ref. [6]. Bossanyi [8] implemented IBP control using PI controllers to mitigate blade loads. He used a direct and quadrature (d–q) axis representation (a form of MBC) to be able to allow for MIMO control using PI controllers. Direct periodic control allows the controller gains to change depending on the rotor azimuth position and control objectives based on a periodic state space model [36, 38]. Periodic control captures all the periodicity of the system. Although MBC transformation does not capture all the periodic effects, it has been shown that the residual periodic effects are negligible for analysis and control for three bladed wind turbines [37]. Wright [40] achieved IBP control using a disturbance accommodating controller. He used an internal model for the wind disturbance and the effect of wind shear to drive the individual blade pitching.

A Review of Floating Wind Turbine Controllers 2 1.5 1

0.5

Gains

Fig. 3 Blade 1 pitch angle controller gains for the platform pitch velocity for collective and individual blade pitch controllers. Individual blade pitching results in a gain that is periodic with the rotor azimuth

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0 −0.5 −1 Constant Periodic

−1.5 −2

0

60

120

180

240

300

360

Azimuth, deg

As an example to illustrate the difference between the CBP and IBP controllers, blade 1 control gain for the IBP controller corresponding to platform pitch velocity is shown in Fig. 3 along with the equivalent constant CBP controller gain; both were calculated using the same weighting for the control objectives [28]. The zero azimuth position is at the 12 o’clock position on the rotor with angle increasing clockwise when looking downwind; initially blade 1 is at the 0 azimuth position. The periodic gain changes sign twice as the blade goes through a full rotor revolution. The figure does not show the gains for the other two blades as the gains are the same but shifted 120 and 240 out of phase for blades 2 and 3 respectively. The significance of the sign change will be explained next. The mechanism can be explained by looking at the periodic gain matrix (Fig. 3). The gain for blade 1 is negative for azimuth angles approximately between 90 and 270; these angles correspond to the blade lying in the lower half section of the rotor. Therefore, given a negative platform pitch velocity, shown in Fig. 2b, blades at the top with a positive controller gain will be commanded to reduce blade pitch thus increasing thrust. Blades at the bottom with a negative controller gain will be commanded to increase blade pitch and hence reducing thrust. This asymmetric aerodynamic loading will generate a positive restoring pitching moment in addition to the restoring moment generated by the mean thrust load (as with collective pitch) as illustrated in Fig. 2b. Looking further at the periodic gain shown in Fig. 3, it can be seen that the maximum positive peak is when the blade is at the top (12 o’clock) position. A more positive gain value means increased blade pitching, which indicates that the controller is making use of the combined effect of increased moment arm and wind shear. The use of IBP increases the number of available actuators to an under-actuated system where the number of degrees of freedom is more than the number of actuators. Furthermore, having the wind turbine mounted on a floating platform makes it even more difficult to regulate the control objectives than turbines with fixed foundations by introducing 6 additional DOFs (surge, sway, heave, roll,

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Fig. 4 Basic wind turbine control block diagram

pitch, and yaw). However, there are some issues that arise when implementing IBP that one must be aware of; these include: • Increased blade pitch actuation which may result in blade pitch saturation and/or increased blade loads (depending on the control objectives). • Increased computational requirements by the control system. • The possibility of exciting or destabilising other turbine modes due to coupling with un-modelled and/or unregulated DOFs.

4 Floating Wind Turbine Controllers Wind turbine control objectives are region dependent. In below rated wind speed region (region 2), the main objective is to maximise power capture while in above rated wind speed region (region 3) the objective is to limit power capture to the rated power of the generator. In addition to these basic control objectives, additional control objectives for wind turbine mainly focus on load reduction for the blades, tower, and drive-train. Having a floating wind turbine, allows the introduction of additional control objectives that focus on mainly reducing the platform rotational motions (roll, pitch, and yaw angles and velocities). Regulating the platform’s linear displacements and velocities (surge, sway, and heave) is possible but may require more actuators than just using the blades and generator torque. In its simplest form, a wind turbine’s control system can be represented by the block diagram shown in Fig. 4 where the controller commands the actuators (h and TGen) based on some measurements y to achieve their control objectives in the presence of disturbances ud such as wind and waves. The implemented controller can be a single centralised MIMO controller or consisting of several SISO control loops. Furthermore, inside each controller block there exist region transition logic and a control scheme for each operating region.


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In this section, a summary of the most important controllers specifically designed for floating wind turbines found in the literature is given. These controllers range in complexity, description detail, simulation results and testing styles, and floating platform structure. Furthermore, they have not yet been compared against each other quantitatively using the same simulation conditions. However, the attributes of each control approach are documented in the literature and are listed in this section. The main attributes of these controllers are given in Table 4. In the table, the platform types barge, TLP, and spar-buoy refer to the ITI Energy barge, MIT/NREL TLP, and OC3-Hywind spar-buoy respectively whose properties are listed in Table 3; The Hywind spar-buoy is a slightly different model than the OC3 spar-buoy where it has a different wind turbine mounted from the NREL 5 MW baseline wind turbine. Descriptions of each controller follows.

4.1 Gain Scheduled PI Control A collective blade pitch gain scheduled proportional-integral (GSPI) controller was implemented on a floating wind turbine by Jonkman to assess the dynamic performance of the barge floating platform [16]. The GSPI control loop only operates in the above rated wind speed region (region 3) to regulate the rotor speed to the rated speed by pitching the blades thus reducing their aerodynamic efficiency. The GSPI control law is given by Eq. 1 where h(t) is the commanded collective blade pitch angle, KP(h) and KI(h) are the scheduled proportional and integral gains respectively. The error signal is denoted e(t) with xRated and xGen as the rated and actual generator speed respectively. The GSPI controller is a SISO controller with a single objective of rotor speed regulation. The gains are scheduled as a function of blade pitch angle h such that the controlled rotor DOF has the same prescribed natural frequency and damping ratio for all above rated wind speeds. Gain scheduling is a simple form of nonlinear control. hðtÞ ¼ KP ðhÞeðtÞ þ KI ðhÞ

Zt

eðsÞds

where

eðtÞ ¼ xGen xRated

ð1Þ

0

In addition to the blade pitch control loop, a separate generator torque control loop was used to maximise power capture in below rated wind speeds and regulate power in above rated wind speeds. The torque controller varies the applied generator torque as a function of filtered generator speed. In below rated wind speed (region 2), the relationship that maximises power capture is given by Eq. 2 where q, RRotor, N, xGen, CP,max, and ko are air density, rotor radius, gearbox ratio, generator rotational speed, maximum power coefficient and tip speed ratio that yields CP,max respectively [7]. In above rated wind speed region where the objective is to regulate power to the rated, the relationship is given by Eq. 3 where PRated is the rated generator power and gGen is the generator efficiency.

Unknown CBP SS IBP SS

IBP SS

ASC CBP SS IBP SS

DAC

ASC active structural control CBP collective blade pitch DAC disturbance accommodating control EBC estimator based control GSPI gain scheduled proportional-integral control IBP individual blade pitch SS state-space TTF tower-top feedback VPPC variable power pitch control Number of simulations guide: Limited \10 Moderate \100 Extensive [100

Unknown Tuned mass damper with active control Constant torque Constant power ? SS Constant Wind speed disturbance rejection only power ? SS

Unknown

EBC

FAST

HywindSim/ SIMORIFLEX FAST-SC FAST FAST Extensive

Moderate Limited Extensive

Limited

HAWC2/SIMO- Limited RIFLEX FASF054 Limited

Extensive

Simulation code Number of simulations

Constant power TTF loop, pitch to stall, or detuned gains FAST

Constant torque Constant speed region just below rated with PI torque control loop Constant torque Variable rotor speed setpoint. IBP for blade load reduction Unknown Wind turbine estimator to hide tower dynamics

CBP GSPI

GSPI CBP GSPI Hywind VPPC CBP GSPI

GSPI

Table 4 Overview of most important controller applied on floating wind turbines Blade pitch Torque control Additional control features control

High

High High High

3

3 3 3

3

3

High Low

2, 3

2, 3

Barge, TLP

Barge Barge Barge, TLP

Hywind sparbouy

Barge, TLP, Spar-buoy Hywind sparbouy Barge

Sim. Platform regions

High

High

Model fidelity

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TGen ¼

427

pqR5Rotor CP;max 2 xGen ¼ Kx2Gen 2k2o N 3

ð2Þ

PRated gGen xGen

ð3Þ

TGen ¼

The generator speed, used by both CBP and generator torque controllers, was filtered using a low pass filter with a cut-off frequency of 0.25 Hz. This frequency was chosen to be a quarter of the blades edgewise natural frequency to prevent the controller from exciting these modes [16]; this behaviour was also observed by Wright [40] where he found that a PI controller with a fast actuator can destabilise certain modes due to the actuation frequency. Since the GSPI controller was one of the first well documented controllers implemented on floating wind turbines, it became the reference or baseline controller for other researchers to compare more advanced control strategies. In this chapter, this controller with detuned gains (see next section) will be referred to as the Baseline controller.

4.1.1 Barge Platform Jonkman, using his newly developed FAST simulator with the HydroDyn module [16], conducted an extensive set of simulations on the ITI Energy barge floating platform in accordance with the IEC-61400-3 standard for ultimate loads [2]. The IEC-61400-3 is a standard for offshore wind turbines with fixed foundations. To date, no standards for floating wind turbines have been released. The barge platform exhibited large platform oscillations especially in the pitch direction (fore-aft rocking motion) resulting in large tower loads and power fluctuations. To mitigate these effect, Jonkman implemented three modifications to the controller; these were:

Tower-Top Feedback Loop Additional proportional control loop that aims to reduce tower fore-aft motion based on the measurement of the tower top acceleration was implemented. However, the addition of a tower top feedback control loop did not improve platform damping due to conflicting blade pitch commands issued by the separate control loops as discussed in Sect. 3.1.

Active Pitch to Stall Control The objective of using pitch to stall was to get extra restoring thrust force once the blades are stalled when the turbine pitches forward. The active pitch to stall

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controller had excellent power regulation, however, platform motions were not reduced but were increased instead. This contradictory result can be explained by looking at the open and ideal closed loop damping ratios (not shown here). Jonkman concluded that the pitch to stall damping was actually positive. And, since the pitch to feather controller had better damping, it was concluded that the actual closed loop damping of the pitch to feather controller was greater than that of the pitch to stall; i.e. the system has positive damping.

Detuned Controller Gains Detuning the controller gains reduced the use of the blades and possibly reduced the negative damping effect. The detuned gains produced the best results out of the four controllers. Reducing the controller gains made the response of the system closer to that of the open loop response hence increased damping. The reduction in controller gains produced reasonable power regulation and slightly reduced the platform oscillations.

Conclusions The increase in the main turbine loads is staggering. For example, the tower base loads are 6 times that of an onshore wind turbine. Jonkman concludes in his PhD dissertation that further reductions in platform motions is necessary and suggests many possibilities to improve platform pitch damping such as the use of constant torque algorithm, use of MIMO state space controllers to avoid conflicting blade pitch commands, and implementing individual blade pitching control strategies.

4.1.2 TLP and Spar Buoy Larsen and Hanson [21], working on the Hywind spar-bouy (slightly different from the OC3-Hywind model with a different mounted wind turbine), also implemented a GSPI pitch control strategy in above rated wind speed region. However, to avoid the platform pitch damping issue, they used a constant torque algorithm in the above rate wind speed region and introduced a constant speed region just below rated wind speed. A variable speed below rated region also exists to maximise power capture. In the constant speed region, a PI torque control was used to regulate the rotor speed. Switching between regions was handled by a set of variable min/max operations in addition to low pass filters to ensure smooth transition. Simulation results using HAWC2 coupled with SIMO-RIFLEX from MARINTEK showed a 30 % increase in rotor speed and power fluctuations. The motion response of the floating system was acceptable and tower loads were ‘‘reasonable’’ but were not compared to an onshore system. Larsen and Hanson accidentally simulated the floating system with active pitch to stall controller and


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achieved surprisingly good results and recommended further investigations. However, this contradicts with what Jonkman [16] found when he investigated pitch to stall on the ITI Energy barge. The difference in behaviour is most likely due to different platform dynamics and/or model complexity used. Matha [22], continuing on Jonkman’s work, extended the simulations on the Barge platform to include fatigue loads analysis. He also carried out a series of extensive simulations in accordance with the IEC-61400-3 standard on the TLP and Spar-Buoy platforms. Comparisons were made relative to an onshore wind turbine with the same controller. The Baseline controller was used on all three platforms, however, some modifications were made to the controller on the SparBuoy platform. A constant torque instead of constant power algorithm was used in the above rated wind speed region to improve platform pitch damping. Furthermore, the controller bandwidth was limited to avoid resonance issues due to lower natural frequencies of the system than the other platforms. Below is a brief summary of results obtained by Matha [22] on each platform.

ITI Energy Barge The barge concept has some advantages such as cost effective construction, ability to be assembled in any port due to its shallow draft, and relatively inexpensive slack anchoring system. However, fatigue loads analysis results are no different to the ultimate results found by Jonkman [16] in terms of their relative increase to an onshore system; e.g. tower fatigue loads are 6 times that of an onshore wind turbine. Matha concludes that the barge may not be suitable for sites with ‘‘severe sea states’’ but could provide a cost effective solution to more sheltered sites such as the Great Lakes in the USA.

OC3-Hywind Spar-Buoy Results from the extensive simulations show that the spar-buoy experiences significantly less loading on the turbine structure when compared to the barge. However, fatigue loads relative to an onshore wind turbine are still approximately 1.5–2.5 times more than an onshore system. This requires some degree of strengthening the turbine tower and blades to withstand these loads. Furthermore, the deep draft of the spar-buoy limits the number of ports where the hull can be fabricated and assembled. However, the first floating wind turbine prototype—the Hywind project—is a spar-buoy floating system. To date, the outcome of this project, which the offshore wind turbine community is very interested in, are yet to be analysed and published.

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MIT/NREL TLP The performance of the TLP relative to the onshore wind turbine is excellent. Almost all of the turbine loads are close to unity with the exception of tower foreaft (FA) loads which are on average 1.5 times that of an onshore system. Therefore, the TLP probably has the best potential, with more advanced controllers, to achieve similar loading to that of an onshore wind turbine. However, the TLP may not be the most economical choice due to the relatively high cost of the taut anchoring system.

Platform Instabilities Jonkman [16] and Matha [22] have identified certain instabilities for each platform. These arise from a unique design load case (DLC) specified by the IEC61400-3 standard such as operating with a faulty blade or in extreme conditions. For example, all three platforms experience yaw instability in a certain DLC where the turbine is idling with two of its blades fully feathered and one stuck at the maximum lift position (blade pitch angle of 0). Most of these instabilities are due to the design of the system rather than being caused by the controller.

4.2 Variable Power Pitch Control A simple empirical yet effective solution to avoid the reduced or negative damping in the pitch motion was developed by Lackner [20]. As discussed in Sect. 3.1, a standard speed controller exacerbates platform pitching motion as it attempts to regulate rotor speed to the rated speed in above rated wind speed region. Lackner’s solution was to change the speed set-point for the collective blade pitch GSPI controller from a constant to a linear function of platform pitching velocity and implement a constant torque algorithm in above rated wind speed region. The idea is to increase the rotor speed as the turbine is pitching forward (negative pitching velocity according to the coordinate system) forcing the rotor to increase torque to accelerate the rotor and hence create a pitch restoring moment due to increased rotor thrust force. Also, when the wind turbine is pitching backwards, the desired rotor speed is less than the nominal rated speed forcing the speed controller to feather the blades to reduce the rotor speed and thereby reducing the rotor thrust allowing the turbine to pitch forward. By holding the generator torque constant, turbine power becomes a linear function of platform pitch velocity. Furthermore, rotor speed fluctuations are reduced resulting in reduced blade pitching which limit the changes in rotor thrust that causes the reduction in platform pitch damping. This simple control strategy essentially trades power fluctuations for reduced pitching motion. The rated rotor speed is given by Eq. 4 where xR and xO are the rotor speed reference/set-point and the nominal rated rotor speed respectively, u_ is the


431

platform pitch velocity and k is the gradient of the linear relationship—a design parameter. It can be seen from Eq. 4 that if the platform pitch velocity is zero, the desired rotor speed is the nominal rated speed. When the turbine is pitching _ the desired rotor speed is increased and vice versa. forward (negative u), xR ¼ xo ð1 ku_ Þ

ð4Þ

Lackner applied this control strategy on the ITI Energy barge using FAST. Results were obtained after running two 600 s simulations with turbulent wind and irregular waves for three different slopes (k) of Eq. 4. Results show that platform pitch and pitch rates were, on average, reduced by 8–20 % under different k values but power and rotor speed fluctuations, on average, only increased by 3–11 and 1.5–5 % respectively. Of course, the higher the slope, the bigger the reductions in platform motions and increased power fluctuations. In addition to the variable power pitch control method, Lackner also implemented individual blade pitch control to reduce blade loads in a similar way to Bossanyi’s method [9] of using MBC (or Coleman) transformation with PID control for the cosine-cyclic and sine-cyclic components. However, Lackner found that the individual blade pitch controller was not as effective at reducing blade loads when compared to its onshore counterpart. Blade load reductions were small (0.6–1.6 % reduction) but blade pitch usage had increased significantly. Furthermore, the IBP controller increased platform rolling and hence tower side–side fatigue loads. This effect was also observed by Namik et al. [30] and later resolved by Namik and Stol [28] by including the platform roll and first tower side–side bending mode DOFs into the state space controller design as explicit objectives (see Sect. 4.5.2).

4.3 Estimator Based Control Because the Hywind concept was designed with much lower tower resonance frequencies to avoid the wave energy spectrum [21, 35], Skaare et al. [35] implemented an estimator based control strategy in the above rated wind speed region to ‘‘hide the tower motions for the wind turbine control system and thereby avoid the negative damping effect of the tower motion’’. The wind turbine estimator contains a simplified SISO model of the wind turbine with the wind speed as an input and the rotor to the rotor speed as an output. The wind speed is assumed to be either directly measured or estimated via a wind speed estimator. In below rated wind speed the control system takes the measured rotor speed and commands the generator torque to maximise power capture. In above rated wind speed region, the controller commands the blade pitch based on the estimated rotor speed from the wind turbine estimator whose output is based on the estimated wind speed. Seven simulations were carried out using HAWC2 coupled with SIMO-RIFLEX using the same wind and wave conditions but different turbulence

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intensity. Results show noticeable reductions in nacelle motions and hence large reductions in tower and rotor loads were observed along with increased rotor speed fluctuations and a maximum of 3.81 % reduction in the average power output. Under these limited cases, the controller seems to be regulating the floating turbine well with promising results. However, since the controller in above rated wind speed region depends on the estimated rotor speed and not the actual which is calculated/estimated based on wind speed estimates, the robustness of such controller will heavily depend on the quality of estimates.

4.4 Active Structural Control An active structural control of floating wind turbines using a tuned mass damper (TMD) was developed by Rotea et al. [33]. The idea is to add a tuned mass damper with an active component in the nacelle to influence the platform pitching motion of the floating wind turbine. Rotea et al. derived the general equations of motion of the newly added TMDs (one along each of the pitching and rolling directions) and incorporated these changes into FAST resulting in a special version called FASTSC (for structural control). In their work in [33], only the TMD along the platform pitching direction is activated and analysed. The TMD parameters (mass, spring stiffness, and damping) were chosen such that they reduce the platform pitching motion on the ITI Energy barge passively (i.e. without a controller input). This is followed by designing a family of H? controllers to actively introduce additional damping by driving the TMD. Simulation results show that the passive TMD system achieves 10 % reduction in the tower fore-aft (FA) fatigue damage equivalent load (DEL)2 relative to a ‘‘baseline system’’ without structural control. However, no details were provided about the baseline system/controller. With active structural control, the tower loads are reduced by an average of 15–20 % relative to the passive TMD system at the cost of 3–4 % energy consumption of the rated power of the 5 MW wind turbine. However, the stroke of the TMD (passive or active) is large when the dimensions of the nacelle are considered. Rotea et al. recommend the use to nonlinear control to implement active TMD with stroke stop limits. A major benefit from using a TMD (active or passive) is achieved when the turbine is operating in extreme conditions. In these conditions the blade pitch controller is normally switched off and the blades are feathered. Having a passive TMD will help improve the pitching (and rolling when implemented) motion(s) in these situations. The performance in these conditions are yet to be evaluated.

2

Fatigue DELs are used as a metric to replace the stochastic loads on a component by a periodic load with a calculated magnitude at a known frequency.


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4.5 Linear State Space Control Linear state space (SS) control is one of the preferred types of controllers when dealing with multi-input multi-output (MIMO) systems with multiple objectives. This approach requires a linearised state space model of the nonlinear floating system. As a result, the system states x become perturbations, Dx, about an operating point xop such that x = Dx ? xop. This also applies for the measurements vector y, actuators vector u and disturbance inputs vector ud. A generic linearised state space model is given by Eq. 5 where A is the state matrix, B is the actuator gain matrix, Bd is the disturbance gain matrix, C relates the measurements to the states, D relates the measurements to the control inputs, and Dd relates the measurements to the disturbance inputs. D_x ¼ ADx þ BDu þ Bd Dud Dy ¼ CDx þ DDu þ Dd Dud

ð5Þ

Due to the periodic nature of the wind turbine, the linearised state space model is periodic. That is, the system matrices are periodic function of the rotor azimuth angle. There are three ways to address the periodicity of the system: 1. Averaging the periodicity of each matrix across the azimuth angle results in a time invariant SS model which allows linear time-invariant (LTI) control techniques to be applied. However, some information is lost by averaging and individual blade pitching cannot be used for most control objectives. 2. MBC transformation of the periodic SS model will result in a weekly periodic system. But it was shown that little or no information is lost if it was followed by averaging the weakly periodic matrices [37]. 3. Direct periodic control techniques capture all the periodicity of the wind turbine. However, it is more complicated and computationally more demanding than the previous two methods. The latter two methods facilitate the use of IBP. However, averaging after MBC transformation method allows for the utilisation of IBP control via LTI control design without the computational complexity of direct periodic control method. The state space control law is given by Eq. 6 where K is the controller gain matrix. In all the SS controllers described next, full state feedback (FSFB) is used to implement the control law. FSFB is where all the states are directly measured and fed to the controller. In cases where direct measurement of some of the states is not possible or desirable, a state estimator is required to estimate the missing states from the available measurements based on a linear state space model of the system. Du ¼ KDx

ð6Þ

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4.5.1 Collective Blade Pitch State Space Control A multi-objective collective blade pitch SS controller to regulate rotor speed and platform pitch (angle and velocity) was implemented by Namik et al. [30] on the ITI Energy barge. The torque controller in the above rated region was set to constant torque to improve platform pitch damping. Limited simulation results, using FAST, show that the SS controller was able to reduce rotor speed error, platform pitching and pitching velocity by an average of 19.5, 14.5, and 17 % respectively relative to the detuned GSPI developed by Jonkman (see Sect. 4.1). The improvement in performance due to the additional control objective was obvious, however, the magnitude of the improvement was not expected since the CBP SS controller has the same objectives as Jonkman’s controller with tower-top feedback loop. The difference was attributed to better controller tuning due to a large emphasis on regulating the platform pitch DOF since both controllers (CBP SS and GSPI with tower-top feedback loop) use collective blade pitching to regulate their objectives. Interestingly, tower side–side loads were increased by an average of 18 %. The increased side–side tower loading is due to the controller trying to regulate the platform pitch through increased collective blade pitching; as the turbine pitches forward, the controller commands the blades to increase aerodynamic thrust and consequently the aerodynamic torque. For a three bladed wind turbine, the generated aerodynamic torque is balanced given a uniform incident wind; but, due to wind shear combined with the effects of shaft tilt and rotor pre-cone, a blade at the top generates more torque and thus creates a net sideways force resulting in tower side–side excitation. This issue was resolved by using individual blade pitching.

4.5.2 Individual Blade Pitch State Space Control Individual blade pitching was implemented on the ITI Energy barge and the MIT/ NREL TLP by Namik and Stol [25–29]. IBP was initially implemented using direct periodic techniques but then was changed to using MBC transformation for simplicity; no impact on performance was identified. What follows is a summary of all the work done by Namik and Stol regarding IBP on floating platforms. The minimum order of the SS model for the controller design was established to be 6 DOFs to ensure stability and limit coupling between controller and uncontrolled turbine DOFs. These DOFs (also serve as control objectives) include platform roll, pitch, and yaw, first tower side–side bending mode, generator DOF, and drive-train twist DOF. The reason for these instabilities is simply due to the coupling between using the asymmetric loads created by the IBP controller and the uncontrolled DOFs. For example, when Namik et al. [30] first implemented IBP, a large increase in tower side–side fatigue load was noticed. By looking at the interaction between the blades, rolling and pitching DOFs, it was found that the turbine blades induce rolling and pitching moments on the floating system that are almost in phase. This means when the IBP controller commands the blades to


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create a restoring pitching moment the blades are also inducing a rolling moment. By including the roll DOF into the controller design model the issue was resolved. The generator torque was included in the SS control design to influence rotor speed regulation, tower side–side loading, and platform roll. Since the SS controller commands torque perturbations about an operating point, the generator torque operating point was made to change as a function of rotor speed such that constant power algorithm is implemented (Eq. 3). This configuration allows for explicit power regulation as power is not a design state/DOF in the IBP SS controller model. An extensive number of simulations (presented in [29]) using FAST were carried out in accordance with the IEC-61400-3 standard on the barge platform and TLP for design load case (DLC) 1.2; normal operation for fatigue load testing. Overall averaged results relative to an onshore wind turbine with a Baseline controller (GSPI CBP controller with constant power algorithm for torque control) for the barge and TLP are shown in Fig. 5 where RMS and LSS stand for root mean square and low speed shaft respectively. The figure also shows the performance results for the disturbance accommodating controller (DAC) which is discussed in the next section. The performance of the IBP SS controller on each platform is discussed next. For these results, the controllers are designed based on a 7 DOFs SS model; the additional DOF, first tower fore-aft bending mode, was included to further reduce tower FA fatigue DEL. The increase in blade pitch usage is definitely noticeable, but this increase does not result in prolonged blade pitch saturation.

ITI Energy Barge Figure 5 clearly shows that IBP makes a big impact at reducing tower loads relative to the Baseline controllers. However, tower loads among others remain too high relative to an onshore wind turbine (e.g. tower FA fatigue DEL after reduction is still more than 5 times that of an onshore wind turbine). These large loads are due to the barge ‘‘riding’’ the waves resulting in large platform motions and hence increased tower and blade loads. Namik and Stol also conclude that the barge, in its current design, is not suitable for open sea deployment.

MIT/NREL TLP Although Fig. 5 does not show the results for the IBP SS controller on the TLP, the performance of the IBP SS controller is very similar to the DAC; the DAC has better power and speed regulation. It is worthy to note that the performance of the TLP Baseline controller relative to the onshore system is almost unity across most metrics with the exception of RMS power and speed errors, RMS blade pitch rate, and tower FA fatigue DEL. The almost unity performance of the TLP controllers demonstrate the effectiveness of the TLP design. That is, because platform motions

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Fig. 5 Overall averaged and normalised DLC results for controllers on the offshore platforms relative to the Baseline controller on an onshore system

and rotations are kept small due to the mooring lines, turbine fatigue loads were of a comparable level to an onshore wind turbine with the same controller. Since the IBP SS controller performance is very similar to that of the DAC (reasons will be given in the next section), the DAC performance will be described since it uses IBP and to avoid repetition. The DAC manages to have better power and speed regulation and reduce tower side–side fatigue DEL relative to the onshore system. Tower side–side fatigue DEL are reduced by 24 % relative to the onshore system. Tower fore-aft fatigue DEL are reduced from an increase of 48 % by the GSPI controller to an increase of 24 % relative to an onshore wind turbine. Namik and Stol conclude that with minor strengthening of the tower, the TLP is a good candidate for open sea deployment subject to commercial feasibility.

4.5.3 Disturbance Accommodating Control Disturbance accommodating control is used to minimise or cancel the effects of persistent disturbances ud that affect a dynamic system given by Eq. 5. The DAC is simply the IBP SS controller discussed in the previous section but with an additional module to reject disturbances. A realisable DAC, where direct measurement of the disturbance is not possible, requires a disturbance estimator to estimate the applied disturbance to the system and correct for that [3, 40]. The disturbance estimator requires a disturbance waveform model given by Eq. 7 where z is the disturbance states vector. The choice of matrices F, H; and initial conditions z(0) determines the nature of the assumed waveform (step, ramp, periodic, etc.). z_ ¼ Fz ud ¼ Hz

ð7Þ


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The realisable disturbance rejection control law is given by Eq. 8 where K is the state regulation gain matrix and Gd is the disturbance minimisation gain matrix. The ^ symbol indicates an estimate. Gd is calculated by evaluating Eq. 9 where the resulting gain will minimise or completely cancel the persistent disturbance. The + symbol indicates the application of the Moore–Penrose pseudo inverse. u ¼ K^x þ Gd^z

ð8Þ

Gd ¼ Bþ Bd H

ð9Þ

Namik and Stol implemented DAC on the ITI Energy barge platform and MIT/ NREL TLP to reject uniform wind speed perturbations [25–27, 29]. The DAC is designed to improve rotor speed regulation since it is minimising the effect of wind speed perturbations about an operating point. Simulation results using the same conditions for their IBP SS controller show that the DAC on the barge platform had limited impact. The improvement in rotor speed regulation and hence power regulation was minimal. The reason for such minimal improvement was determined to be due to the barge’s motion being dominated by incident waves rather than wind. Hence, reducing the effects of wind speed perturbations will have little or no impact on performance. Since the TLP is not as dominated by incident waves as the barge, the DAC has a noticeable impact on rotor speed regulation and hence power regulation. The results are shown in Fig. 5 and discussed in the previous section.

4.6 Other Controllers In this section, the remaining works to the authors’ best knowledge that have been carried out on control of floating wind turbines are briefly discussed. These works either have limited information available and/or limited conclusions can be drawn. In 2006, Nielsen et al. [31] implemented an additional blade pitch control algorithm for active damping of the platform pitching motion in above rated wind speed region on the Hywind floating concept. Simulation results using HywindSim (a tool to model a simple floating wind turbine) coupled with SIMO-RIFLEX in addition to scale model testing agree to some degree. The additional pitch control algorithm seems to improve the platform pitching response. Scale model testing was carried out at the Ocean Basin Laboratory at Marintek with a scale of 1:47 [4]. Henriksen [15] implemented a model predictive controller primarily on an onshore wind turbine but a simple model for a floating wind turbine was also used. However, little insight into the performance of the floating system can be offered due to limited floating model fidelity.

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5 Conclusions Offshore wind energy is part of the solution to meet the ever increasing demand for energy. To fully utilise the offshore potential, deployment of wind farms further offshore is necessary. Floating wind turbines offer a feasible solution for water deeper than 60 m. There are three main floating platform concepts; these include a buoyancy stabilised barge platform, a mooring line stabilised tension leg platform, and a ballast stabilised spar-buoy platform. A recurring issue with floating wind turbines in terms of control is reduced or negative platform pitch damping in above rated wind speed region (region 3). Several ways of dealing with this issue were proposed and below is a summary of the most important findings (in no particular order): • Using constant torque instead of constant power control in above rated wind speed region helps to improve the platform pitching response by reducing the use of the blade pitch actuator responsible for the reduction in damping. This is not a complete solution but it can help if used other controllers. • The use of individual blade pitching can greatly improve power and speed regulation, reduce platform motions, and reduce tower bending loads. This comes at the cost of increased blade pitch actuation which may lead to blade actuator saturation and possibly destabilising the system. • The addition of tuned mass dampers (TMDs) can noticeably improve the pitching motion on certain floating platform designs. Active TMDs further improve performance, however, based on current studies to date, the stroke length required to achieve these reductions cannot be used inside the nacelle of the wind turbine. A major benefit of using TMDs (active or passive) is that it has the potential to reduce pitching motion in extreme conditions where the blade pitch controller is not active. Below is a list of topics that are yet to be explored that have the potential to improve the response of floating systems when coupled with advancements in other wind turbine fields (such as blade design and materials). These topics are listed below in no particular order of importance. • Verification of simulation and controller responses against full scale prototypes. This is a critical issue. The verification process may take some time, but when successful verification of simulation tools is achieved, it will increase the confidence in the predicted controllers’ performance. • A detailed look at region transition to and from above and below rated for floating wind turbines is yet to be investigated. There is a potential for limit cycle oscillations (continuously switching between regions regardless of wind conditions) due to the lack of rigid foundations. • The effectiveness of TMDs in extreme conditions. It is expected that TMDs will reduce platform motions in extreme conditions, however, the extent of this reduction needs to be established.


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• Wave disturbance rejection, if possible, may significantly improve the response of floating platforms. • Including additional actuators on the platform may help improve the response but may require too much power. • Applying more advanced controllers such as model predictive control and nonlinear control on such systems. The benefits of implementing such complex controllers is yet to be evaluated on floating wind turbines.

References 1. (2006) Largest and deepest offshore wind turbine installed. Refocus 7(5):14. http:// www.sciencedirect.com/science/article/pii/S1471084606706783 2. (2009) Wind turbines—part 3: design requirements for offshore wind turbines. http:// webstore.iec.ch/webstore/webstore.nsf/mysearchajax?Openform\&key=61400-3\&sorting= \&start=1\&onglet=1 3. Balas MJ, Lee YJ, Kendall L (1998) Disturbance tracking control theory with application to horizontal axis wind turbines. In: Proceedings of the 1998 ASME wind energy symposium, pp 95–99. Reno, Nevada 4. Berthelsen AP (2006) Model testing of floating wind turbine facility. Review 1(1):6 5. Biester D (2009) Hywind: Siemens and StatoilHydro install first floating wind turbine. Retrieved 3 July 2009 from: http://www.siemens.com/press/pool/de/pressemitteilungen/ 2009/renewable_energy/ERE200906064e.pdf 6. Bir G (2008) Multi-blade coordinate transformation and its application to wind turbine analysis. In: 46th AIAA aerospace sciences meeting and exhibit. Reno, NV, pp CD–ROM 7. Bossanyi EA (2000) The design of closed loop controllers for wind turbines. Wind Energy 3(3):149–163. doi:10.1002/we.34 8. Bossanyi EA (2003) Individual blade pitch control for load reduction. Wind Energy 6(2):119–128. doi:10.1002/we.76 9. Bossanyi EA (2003) Wind turbine control for load reduction. Wind Energy 6(3):229–244. doi:10.1002/we.95 10. Brennan S (2008) Offshore wind energy resources. Workshop on deep water offshore wind energy systems. http://www.nrel.gov/wind_meetings/offshore_wind/presentations.html 11. Bulder B, Peeringa J, Pierik J, Henderson AR, Huijsmans R, Snijders E, Hees Mv, Wijnants G, Wolf M (2003) Floating offshore wind turbines for shallow waters. In: European wind energy conference. ECN Wind Energy, Madrid, Spain 12. Butterfield S, Musial W, Jonkman J, Sclavounos P, Wayman L (2005) Engineering challenges for floating offshore wind turbines. In: Copenhagen offshore wind 2005 conference and expedition proceedings. Danish Wind Energy Association, Copenhagen, Denmark 13. Goldman P (2003) Offshore wind energy. Workshop on deep water offshore wind energy systems 14. Henderson AR, Morgan C, Smith B, Sørensen HC, Barthelmie RJ, Boesmans B (2003) Offshore wind energy in Europe: a review of the state-of-the-art. Wind Energy 6(1):35–52 15. Henriksen LC (2007) Model predictive control of a wind turbine. Master’s thesis, Technical University of Denmark 16. Jonkman JM (2007) Dynamics modeling and loads analysis of an offshore floating wind turbine. Ph.D. thesis, University of Colorado

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17. Jonkman JM, Butterfield S, Musial W, Scott G (2007) Definition of a 5-MW reference wind turbine for offshore system development. Technical Report TP-500-38060, National Renewable Energy Laboratory 18. Jonkman JM, Lasren T, Hansen A, Nygaard T, Maus K, Karimirad M, Gao Z, Moan T, Fylling I, Nichols J, Kohlmeier M, Pascual Vergara J, Merino D, Shi W, Park H (2010) Offshore code comparison collaboration within IEA wind task 23: phase IV results regarding floating wind turbine modeling. In: European wind energy conference 2010. Warsaw 19. Jonkman JM, Sclavounos PD (2006) Development of fully coupled aeroelastic and hydrodynamic models for offshore wind turbines. In: Proceedings of the 44th AIAA aerospace sciences meeting and exhibit. Reno, NV, pp CD–ROM 20. Lackner MA (2009) Controlling platform motions and reducing blade loads for floating wind turbines. Wind Energy 33(6):541–553 21. Larsen TJ, Hanson TD (2007) A method to avoid negative damped low frequency tower vibrations for a floating pitch controlled wind turbine. In: Conference on Journal of Physics, series 75. doi:10.1088/1742-6596/75/1/012073 22. Matha D (2009) Modelling and loads and stability analysis of a floating offshore tension leg platform wind turbine. Master’s thesis, National Renewable Energy Lab’s National Wind Turbine Center and University of Stuttgart 23. Musial W, Butterfield S, Boone A (2004) Feasibility of floating platform systems for wind turbines. In: 23rd ASME wind energy symposium. NREL, Reno, NV 24. Musial W, Butterfield S, Ram B (2006) Energy from offshore wind. In: Offshore technology conference. Houston, Texas, USA, pp 1888–1898. doi:10.4043/1855-MS 25. Namik H, Stol K (2009) Control methods for reducing platform pitching motion of floating wind turbines. In: European offshore wind 2009. Stockholm, pp CD–ROM 26. Namik H, Stol K (2009) Disturbance accommodating control of floating offshore wind turbines. In: 47th AIAA aerospace sciences meeting and exhibit. Orlando, FL, pp CD–ROM 27. Namik H, Stol K (2010) Individual blade pitch control of a floating offshore wind turbine on a tension leg platform. In: 48th AIAA aerospace sciences meeting and exhibit. Orlando, FL, pp CD–ROM 28. Namik H, Stol K (2010) Individual blade pitch control of floating offshore wind turbines. Wind Energy 13(1):74–85. doi:10.1002/we.332 29. Namik H, Stol K (2011) Performance analysis of individual blade pitch control of offshore wind turbines on two floating platforms. Mechatronics 21(4):691–703, Elsevier 30. Namik H, Stol K, Jonkman J (2008) State-space control of tower motion for deepwater floating offshore wind turbines. In: 46th AIAA aerospace sciences meeting and exhibit. Reno, NV, pp CD–ROM 31. Nielsen FG, Hanson TD, Skaare B (2006) Integrated dynamic analysis of floating offshore wind turbines. In: Proceedings of the 25th international conference on offshore mechanics and arctic engineering. Hamburg, pp 671–679 32. Passon P, Kühn M, Butterfield S, Jonkman JM, Camp T, Larsen TJ (2007) OC3—benchmark exercise of aero-elastic offshore wind turbine codes. In: Conference on Journal of Physics, series 75(1):012,071. doi:10.1088/1742-6596/75/1/012071. url:http://stacks.iop.org/17426596/75/i=1/a=012071 33. Rotea M, Lackner MA, Saheba R (2010) Active structural control of offshore wind turbines. In: 48th AIAA aerospace sciences meeting and exhibit. Orlando, FL, pp CD–ROM 34. Shikha Bhatti TS, Kothari DP (2003) Aspects of technological development of wind turbines. J Energy Eng 129(3):81–95 35. Skaare B, Hanson TD, Nielsen FG (2007) Importance of control strategies on fatigue life of floating wind turbines. In: Proceedings of the 26th international conference on offshore mechanics and arctic engineering. San Diego, CA, pp 493–500 36. Stol K (2001) Dynamics modeling and periodic control of horizontal-axis wind turbines. Ph.D. thesis, University of Colorado


441

37. Stol K, Moll HG, Bir G, Namik H (2009) A comparison of multi-blade coordinate transformation and direct periodic techniques for wind turbine control design. In: 47th AIAA aerospace sciences meeting and exhibit. Orlando, FL, pp CD–ROM 38. Stol K, Zhao W, Wright AD (2006) Individual blade pitch control for the controls advanced research turbine (CART). J Sol Energy Eng, Trans ASME 22:16,478–16,488 39. Ushiyama I, Seki K, Miura H (2004) A feasibility study for floating offshore windfarms in Japanese waters. Wind Energy 28(4):383–397 40. Wright AD (2004) Modern control design for flexible wind turbines. Technical Report NREL/TP-500-35816, National Renewable Energy Lab