Multi-objective Control of Floating Offshore Wind Turbines
best dynamic performance results simply because its platform motions and rotations were small. This was due to the tensioned mooring lines that resulted in significantly lower loads on the turbine structure.
If the wind turbine was mounted on the barge platform, then tower fore–aft loads would increase at least six-fold (i.e. to seven times that of an onshore wind turbine). Power fluctuations and tower side–side loads would also increase, by approximately three-fold. This considerable increase was caused by the large induced motions of the platform due to the barge’s sensitivity to incident waves.
The spar-buoy platform was less sensitive to incident waves than the barge and hence experienced a smaller increase in the aforementioned loads than the barge. The baseline control system properties on the spar-buoy platform, however, were slightly changed to avoid destabilisation of platform pitch motion. For this reason, power regulation was the worst of all the systems analysed.
Multi-objective Control
Multi-objective controllers are those that can regulate more than one objective in a single controller implementation. This configuration eliminates the possibility of conflicting blade pitch commands issued by several independent controllers regulating the same number of objectives.
State space control is one of the many ways to achieve multi-objective control and is the method used in this research. State space control requires a linear model that describes the dynamic behaviour of the floating system. Floating wind turbines are inherently non-linear, however, so the linear models obtained for controller design are only valid approximations around the operating points from which they were linearised. Furthermore, as the number of control objectives and/or the model fidelity increase, more sensors may be needed to make the controller implementation realisable thus increasing computational power required.
Multi-objective controllers are also multi-input multi-output (MIMO) systems and hence enable more than one actuator to be used for regulation. This allows for state space control to utilise the generator torque with the blade pitch actuators in one central controller implementation. It also enables the pitch of each blade (operating individually) to be used to reduce the platform motions, increasing the number of actuators from one for SISO controllers to four.
IBP describes when each blade of the wind turbine is commanded to give a different pitch angle simultaneously by the controller. This allows the controller to induce asymmetric loads on the wind turbine that help regulate rolling, pitching and yawing, whereas collective blade pitching can only create symmetric loads on the rotar. Since with IBP each blade becomes an independent actuator, a MIMO controller is usually required to implement IBP. In related work by others7 been implemented with multiple SISO3
IBP has control loops, but this
requires the assumption of decoupled dynamics which is not always a valid assumption. The IBP state space controller developed for the floating wind turbines was designed to regulate platform roll, pitch and yaw, rotor speed and power, tower fore–aft and side-to-side bending.
MODERN ENERGY REVIEW – VOLUME 2 ISSUE 2
Figure 2: Averaged Normalised Performance of the Baseline Controller on the Three Main Floating Platforms versus an Onshore Wind Turbine
1 2 3 4 5
0
Power error Blade pitch usage
Power regulation and actuator usage
TLP = tension leg platform.
Figure 3: Averaged Normalised Performance of the State Space Controllers on the Barge and Tension Leg Platform Floating Platforms Relative to an Onshore Wind Turbine
0 1 2 3 4 5
Blade root flapwise bending
Barge TLP
Tower base fore–aft bending
Tower base side–side bending
Fatigue damage equivalent load Spar-bouy
Low-speed shaft torsion
Figure 1: The Three Main Floating Wind Turbine Platforms
Tension leg
Barge platform Spar-bouy
Power error Blade pitch usage
Power regulation and actuator usage
Blade root flapwise bending
Tower base fore–aft bending
Tower base side–side bending
Fatigue damage equivalent load Barge: baseline Barge: IBP SS TLP: baseline IBP = individual blade pitching; SS = state space; TLP = tension leg platform.
Individual Blade Pitching State Space Controller Simulation Results
The IBP state space controller was applied on the TLP and barge platforms; it has not yet been implemented on the spar-buoy platform. Simulation results obtained using the same set of conditions as for the baseline controller are shown in Figure 3. The performance of the baseline controller is also shown in the figure for easy comparison.
41 TLP: IBP SS
Low speed shaft torsion
Normalised performance index
4.38 1.72
4.63 0.72
2.77 8.80 1.11 5.02
1.87 1.93 1.03 0.99
7.72 5.12 1.48 1.31
4.16 1.83 0.97 0.71
1.54 1.55 1.02
1.04
Normalised performance index
4.38 7.97
1.15
2.77 1.11 0.56
1-87 1.03 1.06
7.72 1.48 3.14
4.16 0.97 1.17
1.54 1.02 1.04
Mooring lines not to scale
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