Assessment of Wind Turbine Aero-Hydro-Servo-Elastic Modelling on the Effects of Mooring Line Tension via Deep Learning
Abstract
:1. Introduction
- (1)
- Advanced sensing and condition monitoring (CM) provide an effective way of collecting data and detecting failures, but it is unable to explain the inherent driven force on these failures. To date, it is not clear what factors affect the FOWT mooring line tension. To tackle this problem, this paper studies the driven force of mooring line tension based on an advanced AHSE model and deep learning, taking into account different environmental conditions.
- (2)
- The global performance of wind turbine dynamics necessitates a coupled analysis of hydrodynamics, aerodynamics, structural dynamics, controls and so forth, which are highly dependent on environmental conditions. Consequently, traditional parametric studies have difficulty in analysing the influence on the mooring line tension under various conditions. Simply changing environmental parameters and operating conditions will result in an excessive number of case studies in the time domain, prominently increasing computation costs and sometimes are impractical to be realised. To this point, this paper applied deep learning to build a model for investigating the mechanism of mooring line tension. Therefore, the influence of various environmental parameters can be accounted for automatically.
- (3)
- Learned from offshore platforms, it is well known that mooring line tension is driven by the 6 Degree of Freedom (DOF) dynamic responses of the support structure. However, due to the large height-to-width ratio, the contribution from the upper structure, such as the tower and blades of the FOWT on the mechanism of mooring line tension, is not clear [18]. To solve this problem, the inputs of the deep learning model includes the 6-DOF motion responses of the platform and upper structure deflections. These influences are almost unable to be analysed through conventional parametric studies. In this paper, both linear relationships (correlation analysis) and nonlinear relationships (deep learning model) are considered and discussed.
- (4)
- We have focused on direct drive train wind turbines, which are believed to be more popular and suitable for floating wind turbines due to their larger loading capacities. The pitch control for the direct drive train for floating wind turbines has been redeveloped, and its accuracy has been validated against a gearbox wind turbine.
2. Methodology
2.1. AHSE Modelling
2.1.1. Aerodynamics
2.1.2. Hydrodynamics
2.1.3. Structural Dynamics
2.2. Correlation Analysis
2.3. Deep Learning Modelling
- ▪
- ▪
- In the deep learning configuration, each neuron was also determined by a bias (), for which the defaults initial bias was set as zero;
- ▪
- The outputs of the layer were controlled by an activation function (), using the non-linear activation functions of Rectified Linear Unit (ReLU) (see Figure 4B).
3. Wind Turbine Properties
3.1. Structure Specifications
3.2. Direct Drive Wind Turbine
3.2.1. Control methodology
3.2.2. Validation of Control Method
4. Case Studies and Discussion
4.1. Load Cases and Inputs for the Deep Learning Model
4.2. Load Cases and Inputs for the Deep Learning Model
4.3. Level of Significance
4.4. Discussions
4.4.1. Effects of Tower and Blade Flexibility—Slack Mooring Line
4.4.2. Effects of Mooring Configuration—Taut Mooring Line
5. Conclusions
- A deep learning model has been successfully built to rank the level of contributions to predicting the most loaded mooring line tension. Its accuracy has been validated against the nonlinear time-domain method.
- A numerical model has been developed on blade pitch control for a direct drive train configuration with FOWT, while its accuracy has been validated against gearbox wind turbine. Good agreement has been achieved in terms of blade pitch angles for above-rated wind conditions because of the perfect match of shaft speed.
- For the slack mooring configuration, the most loaded mooring line tension is mainly dominated by the platform surge motion, while the pitch and the heave have almost equal contributions to the tension, but not as important as surge. For taut mooring lines, the most loaded line tension is purely determined by surge, while other parameters are less significant.
- Compared with surge motion, blade and tower elasticities are insignificant for predicting FOWT most loaded mooring line tension, regardless of the mooring system types (slack or taut).
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Nomenclature
Latin symbols | |
A | Wave amplitude |
bj | Bias associated with neuron j |
C | Damping matrix |
Cd | Drag coefficients |
Cm | Mass coefficients |
D | Element diameter |
fi | Original value/Input of neuron j |
F | Force |
F1 | 1st-order wave forces |
F2 | 2nd-order wave forces |
Fw | Wind induced forces |
Fext | Wave exciting force |
Fscaled | Normalized value |
g | Gravitational acceleration |
h | Output of neuron j |
H | Water depth |
HNi | Net input of neuron j in the output or deeper hidden layer |
i, j | Member index |
k | Wave number |
K | Hydrostatic stiffness matrix |
M | Mass matrix |
Mprediction | Predicted value from the deep learning model |
Msimulation | Recorded value in simulations |
max(f) | Maximum value in the span |
min(f) | Minimum value in the span |
n | Unit vector |
p | Number of tests |
N | Number of individuals |
P | Feature 1 |
Q | Feature 2 |
R | Correlation coefficients |
Re | the real part |
s | Integration variable |
Sb | Body surface |
Sξ(ω) | Wave spectral density |
t | Time |
U | Velocity |
vij | Weights that linked neuron i and j |
w(ω) | Fourier transform |
Wi | Net buoyancy of each segment |
x, y & z | Space coordinates |
Greek symbols | |
ξ | Wave elevation time history |
φ | Velocity potential |
φD | Diffracted potential |
r | Space vector |
β | Incident wave direction |
ρ | Water density |
ω | Circular frequency |
Abbreviations
AHSE | Aero-hydro-servo-elastic |
BEM | Blade Element Momentum |
CFD | Computational fluid dynamics |
CM | Condition monitoring |
DOF | Degree of freedom |
FEA | Finite element analysis |
FEM | Finite element method |
FOWT | Floating offshore wind turbine |
LCs | Load cases |
MSE | Mean square error |
NREL | National Renewable Energy Laboratory |
RAO | Response amplitude operator |
ReLU | Rectified linear unit |
SPH | Smoothed particle hydrodynamics |
SWL | Still-water level |
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Parameter | Gearbox Wind Turbine | Direct Drive Wind Turbine | Scaling Factor |
---|---|---|---|
Proportional Gain | 0.006275604 | 0.608733588 | Gearbox Ratio |
Integral Gain | 0.0008965149 | 0.0869619453 | Gearbox Ratio |
Generator speed at the high-speed shaft end (Rad/s) | 122.9096 | 1.2671 | Gearbox Ratio |
Load Case Number | Wind Speed (m/s) | HS (m) | TP (s) |
---|---|---|---|
1 | 4 | 1.7 | 11.6 |
2 | 6 | 1.9 | 11.3 |
3 | 8 | 2.1 | 11.0 |
4 | 10 | 2.4 | 10.8 |
5 | 12 | 2.8 | 10.7 |
6 | 14 | 3.2 | 10.7 |
7 | 16 | 3.7 | 10.8 |
8 | 18 | 4.2 | 10.8 |
9 | 20 | 4.7 | 11.0 |
10 | 22 | 5.4 | 11.1 |
11 | 24 | 6.0 | 11.3 |
Parameter | Description | |
---|---|---|
Blade | TipDxb1 | Blade 1 flapwise tip deflection |
TipDyb1 | Blade 1 edgewise tip deflection | |
Tower | TTDspFA | Tower-top fore-aft deflection |
TTDspSS | Tower-top side-to-side deflection | |
Platform | PtfmSurge | Platform Surge Motion |
PtfmSway | Platform Sway Motion | |
PtfmHeave | Platform Heave Motion | |
Ptfmroll | Platform Roll Motion | |
PtfmPitch | Platform Pitch Motion | |
PtfmYaw | Platform Yaw Motion |
Model | Mean (kN) | Min (kN) | Max (kN) |
---|---|---|---|
Flexible | 1498.02 | 1140.00 | 2097.00 |
Rigid | 1496.93 | 1140.00 | 2092.00 |
Difference (%) | 0.72 | 0.00 | 2.38 |
Parameter | Value |
---|---|
Rope/Wire Properties nominal diam | 0.2 m |
Weight in air | 0.313 kN/m (0.032 te/m) |
Displacement | 0.234 kN/m (0.024 te/m) |
Weight in water | 0.079 kN/m (0.0081 te/m) |
Diam/Wt ratio | 2.166 m/(kN/m) (21.245 m/(te/m)) |
EA | 43.6E3 kN |
Added mass | 1.0 |
Line length | 245 m |
Line No | Position | |||
---|---|---|---|---|
X (m) | Y (m) | Z (m) | ||
Line1 | Fairlead | −40.868 | 0 | −14 |
Anchor | −200 | 0 | 0 | |
Line2 | Fairlead | 20.434 | 35.3917 | −14 |
Anchor | 100 | 173.2 | 0 | |
Line3 | Fairlead | 20.434 | −35.3917 | −14 |
Anchor | 100 | −173.2 | 0 |
Model | Mean (N) | Minimum (N) | Maximum (N) |
---|---|---|---|
flexible | 860,955.51173 | 313,500 | 1,313,000 |
rigid | 863,705.90408 | 328,400 | 1,305,000 |
Difference (%) | 0.3 | 4.8 | 0.6 |
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Share and Cite
Lin, Z.; Liu, X. Assessment of Wind Turbine Aero-Hydro-Servo-Elastic Modelling on the Effects of Mooring Line Tension via Deep Learning. Energies 2020, 13, 2264. https://doi.org/10.3390/en13092264
Lin Z, Liu X. Assessment of Wind Turbine Aero-Hydro-Servo-Elastic Modelling on the Effects of Mooring Line Tension via Deep Learning. Energies. 2020; 13(9):2264. https://doi.org/10.3390/en13092264
Chicago/Turabian StyleLin, Zi, and Xiaolei Liu. 2020. "Assessment of Wind Turbine Aero-Hydro-Servo-Elastic Modelling on the Effects of Mooring Line Tension via Deep Learning" Energies 13, no. 9: 2264. https://doi.org/10.3390/en13092264