IEC TS 61400-9:2025

Wind energy generation systems - Part 9: Probabilistic design measures for wind turbines

IEC TS 61400-9:2025 sets out minimum requirements to the use of probabilistic design measures in order to ensure the structural and mechanical integrity of wind turbines. The document is based on the general approach in ISO 2394, which also forms the basis for IEC 61400-1. In 61400-1, the design verification approach is based on deterministic design using safety factors. However, edition 4 of IEC 61400-1:2019 opens for introduction of probabilistic design in an informative annex specifying requirements to the calibration of structural material safety factors and structural design assisted by testing. IEC 61400-1 is the governing standard. This document provides appropriate methodologies and requirements for full probabilistic design by taking into account specific uncertainties on not only material properties but also on environmental conditions, design models and the degree of validation. This document also provides provisions for semi-probabilistic design, including reliability-based calibration of partial safety factors and assessment of existing wind turbines. The probabilistic methods in this document are formulated generically and can be applied to structural and mechanical failure modes where a limit state equation can be formulated.

General Information

• Status: Published
• Publication Date: 29-Jul-2025
• ICS: 27.180 - Wind turbine energy systems
• Technical Committee: TC 88 - Wind energy generation systems
• Drafting Committee: PT 61400-9 - TC 88/PT 61400-9
• Current Stage: PPUB - Publication issued
• Start Date: 30-Jul-2025
• Completion Date: 11-Jul-2025

Overview

IEC TS 61400-9:2025 - "Wind energy generation systems ‑ Part 9: Probabilistic design measures for wind turbines" defines methodologies and minimum requirements for applying probabilistic design to wind turbines. Building on the general approach of ISO 2394 and the governing IEC 61400‑1, this Technical Specification formalizes full probabilistic and semi‑probabilistic methods to ensure structural and mechanical integrity by explicitly quantifying uncertainties in materials, environmental conditions, models and validation.

Key topics and requirements

• Probabilistic vs semi‑probabilistic approaches: Provides methods for full reliability‑based design and for semi‑probabilistic design (including reliability‑based calibration of partial safety factors).
• Uncertainty modelling: Guidance on representing and propagating uncertainties for wind conditions, load modelling (aeroelastic and extreme/fatigue loads), structural resistance, geometrical and material properties, and model error.
• Limit state formulation: Methods applicable where a limit state equation can be established for structural or mechanical failure modes.
• Performance and robustness: Requirements for assessing performance of primary structures, mechanical and electrical components, serviceability and ultimate limit states, and system robustness.
• Reliability assessment: Probabilistic measures and computation methods (including Bayesian updating, maximum likelihood, FORM/Inverse FORM and sampling‑based techniques referenced in informative annexes) plus accuracy, sensitivity analysis and test/inspection updating.
• Site suitability analysis: Reliability‑based procedures for assessing site‑specific loads, uncertainties and structural performance for project siting.
• Annexes and worked examples: Informative annexes cover uncertainty quantification (Bayesian, MLE), inverse FORM, example calculations for ultimate and fatigue limit states, event‑driven load cases, and updating based on evidence.

Applications and users

IEC TS 61400‑9:2025 is intended for engineers and organizations involved in wind turbine design, certification and asset management:

• Design engineers and structural/mechanical specialists applying reliability‑based methods for new turbine designs or component optimization.
• Certification bodies and test laboratories adopting probabilistic evidence to verify compliance and calibrate partial safety factors.
• Manufacturers and OEMs using probabilistic modelling to justify material choices, validate models, or support load cases.
• Consultants and site assessors performing site suitability analyses and uncertainty quantification for project development.
• Researchers developing improved uncertainty models, aeroelastic simulations and reliability tools.

Related standards

• IEC 61400‑1 (governing standard for design requirements) - deterministic baseline; edition 4 (2019) allows informative probabilistic approaches.
• ISO 2394 - general principles for reliability of structures referenced as a basis for probabilistic calibration.

Keywords: IEC TS 61400‑9:2025, probabilistic design, wind turbines, reliability-based design, uncertainty modelling, site suitability, partial safety factors, aeroelastic modelling.

Standards Content (Sample)

IEC TS 61400-9 ®
Edition 1.0 2025-07
TECHNICAL
SPECIFICATION
Wind energy generation systems -
Part 9: Probabilistic design measures for wind turbines
ICS 27.180 ISBN 978-2-8327-0494-3
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CONTENTS
FOREWORD . 5
1 Scope . 7
2 Normative references . 7
3 Terms, definitions, symbols and abbreviated terms . 7
3.1 Terms and definitions. 7
3.2 Symbols and abbreviated terms . 10
3.2.1 Symbols . 10
3.2.2 Abbreviated terms . 12
4 Principal elements . 13
4.1 General . 13
4.2 Minimum reliability level and component classes . 14
4.3 Limit states . 14
4.4 Data validity . 15
5 Uncertainty representation and modelling . 16
5.1 General . 16
5.1.1 Uncertainties . 16
5.1.2 Types of uncertainty . 16
5.1.3 Interpretation of probability and treatment of uncertainty . 16
5.1.4 Probabilistic model . 17
5.1.5 Uncertainties for wind turbines. 18
5.2 External condition uncertainty modelling . 19
5.2.1 General . 19
5.2.2 Wind conditions . 19
5.2.3 Normal wind conditions . 20
5.2.4 Other conditions . 21
5.2.5 Electrical network conditions . 22
5.3 Load uncertainty modelling . 23
5.3.1 General . 23
5.3.2 Aeroelastic model . 23
5.3.3 Extreme loads . 24
5.3.4 Fatigue loads . 25
5.4 Structural resistance uncertainty modelling . 25
5.4.1 General . 25
5.4.2 Geometrical properties . 25
5.4.3 Material properties . 25
5.4.4 Resistance models . 26
5.4.5 Fatigue strength and damage accumulation . 26
6 Performance modelling . 26
6.1 General . 26
6.2 Structural performance of primary structures. 26
6.2.1 General . 26
6.2.2 Load performance calibration for ultimate limit states . 27
6.2.3 Evaluation of serviceability limit states. 30
6.3 Performance of primary mechanical and electrical components . 30
6.3.1 General . 30
6.3.2 Requirements for mechanical components . 31
6.3.3 Serviceability limit states for mechanical components . 31
6.3.4 Requirements for electrical components and control and protection
systems . 31
6.4 Robustness . 32
7 Assessment of reliability . 32
7.1 Overview . 32
7.1.1 General . 32
7.1.2 Reliability measures . 32
7.1.4 Accuracy requirements . 34
7.1.5 Sensitivity analysis . 34
7.2 Reliability-based method . 35
7.2.1 General . 35
7.2.2 Probability of failure for extreme design situations . 35
7.2.3 Probability of failure for fatigue design situations . 35
7.2.4 Updating probability of failure using test or inspection data . 36
7.3 Semi-probabilistic method . 36
7.3.1 General . 36
7.3.2 Representative and characteristic values . 36
7.3.3 Partial factor method for extreme and fatigue design situations . 37
7.3.4 Reliability-based calibration of partial safety factors . 37
8 Site suitability analysis . 37
8.1 General approach and scope . 37
8.2 Reliability models for site suitability analysis . 38
8.2.1 General . 38
8.2.2 Load models for site suitability assessment . 39
8.2.3 Resistance model for site suitability assessment . 40
8.2.4 Structural performance on site specific conditions . 41
8.3 Site specific uncertainty modelling . 41
8.3.1 General . 41
8.3.2 Quantification of site-specific uncertainties . 45
8.4 Reliability assessment . 46
Annex A (informative) Uncertainty quantification . 47
A.1 General . 47
A.2 Bayesian methods . 47
A.2.1 General . 47
A.2.2 Closed form solutions for parameter estimation . 48
A.2.3 Exact inference for continuous parameters . 51
A.2.4 Sampling-based inference . 51
A.2.5 Exact inference for discretized parameters . 51
A.3 Maximum likelihood . 51
A.4 Model uncertainties . 52
A.4.1 General . 52
A.4.2 Example: Model uncertainty quantification . 54
Annex B (informative) Inverse FORM . 63
Annex C (informative) Example calculations for reliability assessment . 67
C.1 General . 67
C.2 Ultimate limit state . 67
C.2.1 Design equation. 67
C.2.2 Limit state equation . 67
C.2.3 Reliability assessment . 68
C.2.4 Direct reliability-based design . 69
C.2.5 Reliability-based calibration of partial safety factors . 69
C.2.6 Assess the accuracy of the computation and perform sensitivity studies . 70
C.3 Fatigue limit state . 71
C.3.1 General . 71
C.3.2 Limit state equation . 72
C.3.3 Reliability-based calibration of partial safety factors . 74
Annex D (informative) Formulation of event driven design load cases . 76
D.1 General . 76
D.2 Formulation of wind conditions with conditional events (Example DLC 2.3) . 76
D.3 Probability of failure for independent events (Example DLC 2.1) . 77
Annex E (informative) Updating of distributions based on evidence. 79
E.1 Updating of distributions for basic variables . 79
E.2 Event updating . 80
Annex F (informative) Example of the relative approach to site suitability assessment . 81
Annex G (informative) Uncertainty scenarios for site specific wind assessment . 83
Bibliography . 87

Figure 1 – Application of a mathematical model to estimate an output based on a
given input . 16
Figure 2 – Typical flow chart and uncertainties to be considered in probabilistic design
of wind turbine components . 18
Figure 3 – Typical split between uncertainties which should be propagated through the
aeroelastic model (shaded) and those which should be represented by model
uncertainty . 24
Figure 4 – Measures of reliability computation of failure probability . 33
Figure 5 – Highlight of relevant uncertainties related to site suitability assessment . 42
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 . 48
Figure A.2 – Plot for estimation of model uncertainty . 53
Figure A.3 – Experiment value Y as function of theoretical value h(x) . 55
Figure A.4 – Cumulative distribution and probability density functions for the predictive
distribution, the estimated lognormal distribution and the predictive distribution
for n = 5 . 57
Figure A.5 – Various approaches for MCMC models for model uncertainty
quantification . 58
Figure A.6 – Probability density function for each method . 61
Figure A.7 – Cumulative distribution function for each method . 61
Figure A.8 – Lower tail of the cumulative distribution function for each method . 62
Figure A.9 – Lower tail of the cumulative distribution function for each method for a
sample size n = 50 . 62
Figure B.1 – Contour line for ETM in the u-space and the linear approximation used in

IFORM . 65
Figure B.2 – Contour lines for ETM, linear approximation (in the u-space), and the
characteristic values defined in IEC 61400-1 . 66
Figure C.1 – Yearly (conditional) probability of failure for the calibration example with
COV = 20,0 % and γ = 1,35 . 75
gen
R
Figure C.2 – Yearly (conditional) reliability index for the calibration example with
COV = 20,0 % and γ = 1,35 . 75
gen
R
Table 1 – Minimum reliability requirements . 14
Table A.1 – Theoretical and experimental values . 54
Table A.2 – Stochastic model. 58
Table A.3 – Parameters and moments estimated using each method . 60
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 . 60
Table C.1 – Baseline stochastic variables for load and resistance model . 68
Table C.2 – Annual reliability index for different main wind turbine components (tower)
and design situations (DLC 1.3 and 6.1) . 69
Table C.3 – Resulting reliability index for different values of design parameter for
simplified example . 69
Table C.4 – Resulting reliability for different partial safety factor for loads (given
= 1,2) . 70
γ
M
Table C.5 – Assessment of reliability index convergence with number of draws in
Monte Carlo simulations . 70
Table C.6 – Sensitivities of the reliability index with respect to each random variable in
the limit state equation . 71
Table C.7 – Baseline stochastic model for the fatigue limit state analysis . 73
Table C.8 – Results for safety factor calibration exercise under different assumptions
of COV . 74
gen
Table F.1 – Stochastic model . 81
Table G.1 – Uncertainty scenarios for anemometer calibration . 83
Table G.2 – Uncertainty scenarios for mounting effects of anemometers . 83
Table G.3 – Uncertainty scenarios for measurement heights . 83
Table G.4 – Uncertainty scenarios for amount of available data . 84
Table G.5 – Uncertainty scenarios for long-term wind speed distribution . 84
Table G.6 – Uncertainty scenarios for extreme wind speed events . 84
Table G.7 – Uncertainty scenarios for terrain map quality . 84
Table G.8 – Uncertainty scenarios for wind shear modelling . 85
Table G.9 – Uncertainty scenarios for terrain complexity. 85
Table G.10 – Uncertainty scenarios for distance from measurement point to position of
interest . 85
Table G.11 – Uncertainty scenarios for wake models . 85
Table G.12 – Representative values of COV of wind parameters for normal scenario . 86

INTERNATIONAL ELECTROTECHNICAL COMMISSION
____________
Wind energy generation systems –
Part 9: probabilistic design measures for wind turbines

FOREWORD
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shall not be held responsible for identifying any or all such patent rights.
IEC TS 61400-9 has been prepared by IEC technical committee 88: Wind energy generation
systems. It is a Technical Specification.
The text of this Technical Specification is based on the following documents:
Draft Report on voting
88/1063/DTS 88/1102/RVDTS
Full information on the voting for its approval can be found in the report on voting indicated in
the above table.
The language used for the development of this Technical Specification is English.
This document was drafted in accordance with ISO/IEC Directives, Part 2, and developed in
accordance with ISO/IEC Directives, Part 1 and ISO/IEC Directives, IEC Supplement, available
at www.iec.ch/members_experts/refdocs. The main document types developed by IEC are
described in greater detail at www.iec.ch/publications.
A list of all parts of the IEC 61400 series, under the general title: Wind energy generation
systems, can be found on the IEC website.
The committee has decided that the contents of this document will remain unchanged until the
stability date indicated on the IEC website under webstore.iec.ch in the data related to the
specific document. At this date, the document will be
• reconfirmed,
• withdrawn, or
• revised.
1 Scope
The part of IEC 61400, which is a Technical Specification, sets out minimum requirements to
the use of probabilistic design measures in order to ensure the structural and mechanical
integrity of wind turbines. The document is based on the general approach in ISO 2394, which
also forms the basis for IEC 61400-1. In 61400-1, the design verification approach is based on
deterministic design using safety factors. However, edition 4 of IEC 61400-1:2019 opens for
introduction of probabilistic design in an informative annex specifying requirements to the
calibration of structural material safety factors and structural design assisted by testing.
IEC 61400-1 is the governing standard. This document provides appropriate methodologies and
requirements for full probabilistic design by taking into account specific uncertainties on not
only material properties but also on environmental conditions, design models and the degree
of validation. This document also provides provisions for semi-probabilistic design, including
reliability-based calibration of partial safety factors and assessment of existing wind turbines.
The probabilistic methods in this document are formulated generically and can be applied to
structural and mechanical failure modes where a limit state equation can be formulated.
2 Normative references
The following documents are referred to in the text in such a way that some or all of their content
constitutes requirements of this document. For dated references, only the edition cited applies.
For undated references, the latest edition of the referenced document (including any
amendments) applies.
IEC 61400-1:2019, Wind energy generation systems – Part 1: Design requirements
IEC 61400-3-1, Wind energy generation systems – Part 3-1: Design requirements for fixed
offshore wind turbines
IEC TS 61400-3-2, Wind energy generation systems – Part 3-2: Design requirements for
floating offshore wind turbines
IEC 61400-6, Wind energy generation systems – Part 6: Tower and foundation design
requirements
IEC 61400-13, Wind turbines – Part 13: Measurement of mechanical loads
IEC TS 61400-31, Wind energy generation systems – Part 31: Siting risk assessment
ISO 2394:2015, General principles on reliability for structures
EN 1990, Eurocode – Basis of structural design
3 Terms, definitions, symbols and abbreviated terms
3.1 Terms and definitions
For the purposes of this document, the terms and definitions given IEC 61400-1:2019 and the
following apply.
ISO and IEC maintain terminological databases for use in standardization at the following
addresses:
• IEC Electropedia: available at http://www.electropedia.org/
• ISO Online browsing platform: available at http://www.iso.org/obp
3.1.1
limit state
state of a structure beyond which the structure no longer satisfies the design criteria
3.1.2
design situations
sets of physical conditions covering the conditions the structure will likely experience during a
certain time interval for which the design will demonstrate that relevant limit states are not
exceeded
Note 1 to entry: Internal force, moment, stress and strain are examples of action effect on structural members.
Deflection and rotation are examples of action effect on the whole structure.
3.1.3
fatigue limit state
structural failure due to damage accumulation under effects of repeated loading
3.1.4
ultimate limit states
limit states which generally correspond to the maximum load bearing capacity
Note 1 to entry: This generally corresponds to the maximum load-carrying resistance of a structure or structural
element but in some cases to a strain or deformation limit.
3.1.5
reliability
ability of a structure or structural member to fulfil the specified requirements during the working
life for which it has been designed
Note 1 to entry: Reliability is often expressed in terms of probability.
Note 2 to entry: Reliability covers safety, serviceability, and durability of a structure.
3.1.6
structural safety
ability (of a structure or structural member) to avoid exceedance of ultimate limit states,
including the effects of specified accidental phenomena, with a specified level of reliability,
during a specified period of time
3.1.7
system reliability
reliability of a system of more than one relevant structural member or a structural member which
has more than one relevant failure mode
3.1.8
resistance
ability of a structure (or a part of it) to withstand actions without failure
3.1.9
target reliability
specified average acceptable failure probability that is to be reached as close as possible
Note 1 to entry: Reliability targets are generally model dependent and shall be set for each case considered based
on the models used.
3.1.10
reliability-based design
design procedure that is subjected to prescribed reliability level of the structure
3.1.11
robustness
ability of a structure to withstand adverse and unforeseen events (like fire, explosion, impact)
or consequences of human errors without being damaged to an extent disproportionate to the
original cause
3.1.12
hazard
unusual and severe threat, e.g. a possible abnormal action or environmental influence,
insufficient strength or stiffness, or excessive detrimental deviation from intended dimensions
3.1.13
serviceability limit state
limit state concerning the criteria governing the functionalities related to normal use
3.1.14
limit state function
function gX( , X , …, X ) of the basic variables, which characterizes a limit state when
12 n
gX( , X , …, X )= 0
1 2 n
3.1.15
basic variables
variables representing physical quantities which characterize actions and environmental
influences, material and soil properties, and geometrical quantities
3.1.16
model uncertainty
basic variable related to the accuracy of physical or statistical models
3.1.17
probabilistic methods
verification methods in which the relevant basic variables are treated as random variables,
random processes, and random fields, discrete or continuous
3.1.18
reliability index
β
real number quantifying the reliability of a structure
-1 -1
Note 1 to entry: β=−ФP where Ф is the standard normal distribution and P is the annual probability of
( )
f f
failure of the structure.
3.1.19
structural model
idealization of the structure, physical, mathematical, or numerical, used for the purposes of
analysis, design, and verification
3.1.20
first and second order reliability methods
FORM/SORM
numerical methods used for determination of the reliability index β
3.1.21
reference period
period of time used as a basis for assessing the design value of variable and/or accidental
actions
3.1.22
load effect
result of actions on a structural member (e.g. internal force, moment, stress, strain) or on the
whole structure (e.g. deflection, rotation)
3.2 Symbols and abbreviated terms
3.2.1 Symbols
A parameter -
B parameter -
c environmental conditions -
C covariance matrix -
d distance between wind turbines -
D(t) accumulated damage at time t -
D
admissible fatigue damage -
adm
E event -
E[ ] expected value -
F (x) probability distribution function for a stochastic variable X -
X
f (x) probability density function for a stochastic variable X -
X
f() function -
g( . ) limit state equation -
G design equation -
h( ) function -
H Hessian matrix
H hub height -
k shape parameter of the Weibull distribution function -
K material parameter of the SN-curve -
L characteristic value of load
-
k
likelihood function
L(θ ) -
loglikelihood function -
LL(θ )
L(X ) extreme annual load effect -
L
LOAD load effect
() Weibull distribution -
P
W
P probability of failure -
f
target probability -
P
T
P reference probability -
t
P -
probability of failure conditional on event E
f|E
-
probability of θ conditional on ε
Pθε|
( )
-
p () probability of wind conditions c conditioned on mean wind speed V
cV|
-
P probability of failure conditional on an event E , mean wind speed v
f|E,,vc
and specific wind conditions c
R characteristic value of resistance -
K
event duration -
t
e
T return period -
t
X stochastic variable -
θ statistical parameters in probability distribution functions F (x|θ) -
X
Y stochastic variable in hierarchical stochastic model -

P survival probability -
s
L load effect -
L likelihood function -
max
LL loglikelihood function -
max
L(X ) extreme annual load effect -
L
n number of stochastic variables -
R resistance  -
model uncertainty -
R
s standard deviation -
T duration s
design lifetime y
T
L
reference period y
T
t
stochastic variable Student t distributed with n degrees of freedom -
T
n
V wind speed m/s
V extreme coherent gust magnitude over the whole rotor swept area m/s
cg
V largest gust magnitude with an expected return period of 50 years m/s
gust
V wind speed at hub height m/s
hub
-
random variable representing an environmental parameter X
W
X
stochastic variables -
X, X,…
realisations of stochastic variables -
x, x,…
model uncertainty -
X
model
X load stochastic variables -
L
extreme load effect during event E -
X
E
X model uncertainty stress calculation -
Str
Y' test results -
*
design point for stochastic variable no i -
u
i
W vector of stochastic variables representing different environmental  -
parameters
z design parameter -
Z standard normal distributed stochastic variable -
α wind shear power law exponent -
reliability index -
β
ε
data or evidence -
δ model uncertainty -
stress range -
∆σ
uncertainty on the Palmgren-Miner damage summation model -
Δ
ΔP annual probability of failure -
f
γ partial safety factor for resistances -
M
partial safety factor for resistances -
γ
R
partial safety factor for loads -
γ
f
λ annual failure rate -
F
rate of events -
λ
E
μ mean value / expected value -
ρ air density
kg/m
ρ correlation coefficient between X and Y -
XY
statistical parameter in distributions function -
θ
θ wind direction change magnitude -
cg
extreme direction change magnitude -
θ
e
rate of cycles at stress range -
ν
i /y
σ equivalent stress range -
eq
hub-height longitudinal wind velocity standard deviation m/s
σ
hub-height longitudinal wind velocity standard deviation as a m/s
σ
stochastic variable
σ mean stress MPa
R
Φ standard normal probability distribution function -
standard normal density function -
φ
precision -
τ
lnR
3.2.2 Abbreviated terms
COV coefficient of variation
BEM boundary element method
DEL damage equivalent load
DLC design load case
ECD extreme coherent gust with direction change
EDC extreme wind direction change
EOG extreme operating gust
ETM extreme turbulence model
EWM extreme wind speed model
EWS extreme wind shear
FEM finite element method
FLS fatigue limit state
FMEA failure mode and effect analysis
FORM first order reliability method
FRT fault ride through
IFORM inverse first-order reliability method
LVRT low voltage ride through
MCMC Markov Chain Monte Carlo
MTBF mean time between failures
NTM normal turbulence model
NWP normal wind profile model
PSHA probabilistic seismic hazard analyses
RBI reliability- or risk-based Inspection planning
RNA rotor nacelle assembly
S-N stress-cycle curve for fatigue of materials
SORM second order reliability method
ULS ultimate limit state
4 Principal elements
4.1 General
The following subclauses specify essential requirements to the use of probabilistic design
measures in order to ensure the structural and mechanical integrity of wind turbines. The
specification of requirements applies to the design, siting and reassessment of a wind turbine
system and its components, accounting for influences of the manufacturing, operations,
maintenance, and environmental conditions.
This document provides appropriate methodologies, probabilistic models, and requirements for
probabilistic assessment of components in line with IEC 61400-1. Probabilistic assessment is
an alternative to the semi-probabilistic (deterministic) assessment procedures given in
IEC 61400-1. The reliability assessment is performed for individual components and failure
modes . The designer can select components and failure modes for probabilistic assessment,
while the remaining failure modes shall be verified using traditional semi-probabilistic methods.
It is allowed to do a full probabilistic analysis or use probabilistic methods to calibrate specific
partial safety factors for use in semi-probabilistic assessment.
The main steps in a reliability assessment for a selected component and failure mode are the
following:
a) Determine appropriate component class and minimum annual reliability index (see 4.2).
b) Model specification
i) Formulate limit state equations (see Clause 6).
ii) Develop appropriate probabilistic models for the variables in the limit state equations
(see Clause 5 and additionally Clause 8 for siting).
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This document does not include requirements for the system reliability, thus the system reliability obtained
through the requirements for individual components/failure modes is implicitly accepted.
c) Reliability analysis
i) Perform a reliability analysis and compute the annual reliability index (see
Clause 7).
ii) Assess the accuracy of the computation and perform sensitivity studies (see Clause 7).
d) Assessment
i) For design situations: Perform a design optimization loop oversteps b) and c) until a
design is found where the minimum reliability is reached or exceeded in all years of the
design lifetime.
ii) For siting: The component has adequate reliability on a site if the minimum reliability is
reached or exceeded in all years of the design lifetime.
iii) For reassessment: The component has adequate reliability for continued operation for
as many years as the minimum reliability is reached or exceeded.
4.2 Minimum reliability level and component classes
For ultimate limit states , each component shall meet or exceed the minimum reliability levels
specified in Table 1 for its respective component class (consequence of failure). If human health
and/or environment is at risk, a site-specific risk assessment according to IEC TS 61400-31 (or
equivalent) should be performed. The minimum reliability level can be achieved through a
combination of design, prescribed scheduled maintenance and parts replacement, and health
monitoring. Minimum reliabilities for special safety class wind turbines (as defined in
IEC 61400-1:2019, 5.3) should be agreed between the manufacturer, customer and relevant
authorities. Minimum reliability levels for serviceability limit states should be defined based on
their consequences, see ISO 2394 and (JCSS PMC 2001).
Table 1 – Minimum reliability requirements
Component class Maximum allowable Minimum annual
annual probability reliability index
of failure
-3
Component class 1 3,1
-4
Component class 2 3,3
5 × 10
-4
Component class 3 3,7
For both fatigue and ultimate limit state, the minimum is given for
the year with the largest annual failure probability (for fatigue
conditional on surviving until given year)

4.3 Limit states
For the failure modes selected for probabilisti
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