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PD IEC/TS 61400-9:2025 Wind energy generation systems - Probabilistic design measures for wind turbines, 2025
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- CONTENTS
- FOREWORD
- 1 Scope
- 2 Normative references
- 3 Terms, definitions, symbols and abbreviated terms [Go to Page]
- 3.1 Terms and definitions
- 3.2 Symbols and abbreviated terms [Go to Page]
- 3.2.1 Symbols
- 3.2.2 Abbreviated terms
- 4 Principal elements [Go to Page]
- 4.1 General
- 4.2 Minimum reliability level and component classes
- 4.3 Limit states
- Tables [Go to Page]
- Table 1 – Minimum reliability requirements
- 4.4 Data validity
- 5 Uncertainty representation and modelling [Go to Page]
- 5.1 General [Go to Page]
- 5.1.1 Uncertainties
- 5.1.2 Types of uncertainty
- 5.1.3 Interpretation of probability and treatment of uncertainty
- Figures [Go to Page]
- Figure 1 – Application of a mathematical model to estimate an output based on a given input [Go to Page]
- 5.1.4 Probabilistic model
- 5.1.5 Uncertainties for wind turbines
- Figure 2 – Typical flow chart and uncertainties to be considered inprobabilistic design of wind turbine components
- 5.2 External condition uncertainty modelling [Go to Page]
- 5.2.1 General
- 5.2.2 Wind conditions
- 5.2.3 Normal wind conditions
- 5.2.4 Other conditions
- 5.2.5 Electrical network conditions
- 5.3 Load uncertainty modelling [Go to Page]
- 5.3.1 General
- 5.3.2 Aeroelastic model
- 5.3.3 Extreme loads
- Figure 3 – Typical split between uncertainties which should be propagated through the aeroelastic model (shaded) and those which should be represented by model uncertainty [Go to Page]
- 5.3.4 Fatigue loads
- 5.4 Structural resistance uncertainty modelling [Go to Page]
- 5.4.1 General
- 5.4.2 Geometrical properties
- 5.4.3 Material properties
- 5.4.4 Resistance models
- 5.4.5 Fatigue strength and damage accumulation
- 6 Performance modelling [Go to Page]
- 6.1 General
- 6.2 Structural performance of primary structures [Go to Page]
- 6.2.1 General
- 6.2.2 Load performance calibration for ultimate limit states
- 6.2.3 Evaluation of serviceability limit states
- 6.3 Performance of primary mechanical and electrical components [Go to Page]
- 6.3.1 General
- 6.3.2 Requirements for mechanical components
- 6.3.3 Serviceability limit states for mechanical components
- 6.3.4 Requirements for electrical components and control and protection systems
- 6.4 Robustness
- 7 Assessment of reliability [Go to Page]
- 7.1 Overview [Go to Page]
- 7.1.1 General
- 7.1.2 Reliability measures
- Figure 4 – Measures of reliability computation of failure probability [Go to Page]
- 7.1.4 Accuracy requirements
- 7.1.5 Sensitivity analysis
- 7.2 Reliability-based method [Go to Page]
- 7.2.1 General
- 7.2.2 Probability of failure for extreme design situations
- 7.2.3 Probability of failure for fatigue design situations
- 7.2.4 Updating probability of failure using test or inspection data
- 7.3 Semi-probabilistic method [Go to Page]
- 7.3.1 General
- 7.3.2 Representative and characteristic values
- 7.3.3 Partial factor method for extreme and fatigue design situations
- 7.3.4 Reliability-based calibration of partial safety factors
- 8 Site suitability analysis [Go to Page]
- 8.1 General approach and scope
- 8.2 Reliability models for site suitability analysis [Go to Page]
- 8.2.1 General
- 8.2.2 Load models for site suitability assessment
- 8.2.3 Resistance model for site suitability assessment
- 8.2.4 Structural performance on site specific conditions
- 8.3 Site specific uncertainty modelling [Go to Page]
- 8.3.1 General
- Figure 5 – Highlight of relevant uncertainties related to site suitability assessment [Go to Page]
- 8.3.2 Quantification of site-specific uncertainties
- 8.4 Reliability assessment
- Annexes [Go to Page]
- Annex A (informative) Uncertainty quantification [Go to Page]
- A.1 General
- A.2 Bayesian methods [Go to Page]
- A.2.1 General
- A.2.2 Closed form solutions for parameter estimation
- Figure A.1 – Graphical representation of the structure for estimation of a) the mean value of X when the population variance is known and b) the mean value and the variance of X [Go to Page]
- [Go to Page]
- A.2.3 Exact inference for continuous parameters
- A.2.4 Sampling-based inference
- A.2.5 Exact inference for discretized parameters
- A.3 Maximum likelihood
- A.4 Model uncertainties [Go to Page]
- A.4.1 General
- Figure A.2 – Plot for estimation of model uncertainty [Go to Page]
- [Go to Page]
- A.4.2 Example: Model uncertainty quantification
- Table A.1 – Theoretical and experimental values
- Figure A.3 – Experiment value Y as function of theoretical value h(x)
- Figure A.4 – Cumulative distribution and probability density functions for the predictive distribution, the estimated log normal distribution and the predictivedistribution for n = 5
- Figure A.5 – Various approaches for MCMC models for model uncertainty quantification
- Table A.2 – Stochastic model
- Table A.3 – Parameters and moments estimated using each method
- Table A.4 – Selected quantiles for the predictive distribution (including statistical/parameter uncertainty) and the fitted lognormal distribution (not including statistical/parameter uncertainty) using each method
- Figure A.7 – Cumulative distribution function for each method
- Figure A.6 – Probability density function for each method
- Figure A.8 – Lower tail of the cumulative distribution function for each method
- Figure A.9 – Lower tail of the cumulative distribution function for each method for a sample size n = 50
- Annex B (informative) Inverse FORM
- Figure B.1 – Contour line for ETM in the u-space and the linear approximation used in IFORM
- Figure B.2 – Contour lines for ETM, linear approximation (in the u-space), and the characteristic values defined in IEC 61400-1
- Annex C (informative) Example calculations for reliability assessment [Go to Page]
- C.1 General
- C.2 Ultimate limit state [Go to Page]
- C.2.1 Design equation
- C.2.2 Limit state equation
- C.2.3 Reliability assessment
- Table C.1 – Baseline stochastic variables for load and resistance model [Go to Page]
- [Go to Page]
- C.2.4 Direct reliability-based design
- C.2.5 Reliability-based calibration of partial safety factors
- Table C.2 – Annual reliability index for different main wind turbine components (tower) and design situations (DLC 1.3 and 6.1)
- Table C.3 – Resulting reliability index for different values of design parameter for simplified example [Go to Page]
- [Go to Page]
- C.2.6 Assess the accuracy of the computation and perform sensitivity studies
- Table C.4 – Resulting reliability for different partial safety factor for loads (given = 1,2)
- Table C.5 – Assessment of reliability index convergence with number of draws in Monte Carlo simulations [Go to Page]
- C.3 Fatigue limit state [Go to Page]
- C.3.1 General
- Table C.6 – Sensitivities of the reliability index with respect to each random variable in the limit state equation [Go to Page]
- [Go to Page]
- C.3.2 Limit state equation
- Table C.7 – Baseline stochastic model for the fatigue limit state analysis [Go to Page]
- [Go to Page]
- C.3.3 Reliability-based calibration of partial safety factors
- Table C.8 – Results for safety factor calibration exercise under different assumptions of COVgen
- Figure C.1 – Yearly (conditional) probability of failure for the calibration example with COVgen = 20,0 % and = 1,35
- Figure C.2 – Yearly (conditional) reliability index for the calibration example with COVgen = 20,0 % and = 1,35
- Annex D (informative) Formulation of event driven design load cases [Go to Page]
- D.1 General
- D.2 Formulation of wind conditions with conditional events (Example DLC 2.3)
- D.3 Probability of failure for independent events (Example DLC 2.1)
- Annex E (informative) Updating of distributions based on evidence [Go to Page]
- E.1 Updating of distributions for basic variables
- E.2 Event updating
- Annex F (informative) Example of the relative approach to site suitability assessment
- Table F.1 – Stochastic model
- Annex G (informative) Uncertainty scenarios for site specific wind assessment
- Table G.1 – Uncertainty scenarios for anemometer calibration
- Table G.2 – Uncertainty scenarios for mounting effects of anemometers
- Table G.3 – Uncertainty scenarios for measurement heights
- Table G.4 – Uncertainty scenarios for amount of available data
- Table G.5 – Uncertainty scenarios for long-term wind speed distribution
- Table G.6 – Uncertainty scenarios for extreme wind speed events
- Table G.7 – Uncertainty scenarios for terrain map quality
- Table G.8 – Uncertainty scenarios for wind shear modelling
- Table G.9 – Uncertainty scenarios for terrain complexity
- Table G.10 – Uncertainty scenarios for distance from measurement point to position of interest
- Table G.11 – Uncertainty scenarios for wake models
- Table G.12 – Representative values of COV of wind parameters for normal scenario
- Bibliography [Go to Page]
