Upload
others
View
7
Download
0
Embed Size (px)
Citation preview
Blade-pitch Control SystemDegradation Model
Jinrui Ma, Mitra Fouladirad, Antoine Grall
Outline
1 Background
2 Framework – Blade-pitch control system life time prediction
3 Stochastic process application on wind turbine pitch control system
Model description
Deterioration process of actuator
Healthy indicator
Case study
Conclusions
2/22 Jinrui Ma, Mitra Fouladirad, Antoine Grall UTT - Modelisation et Surete des Systemes (LM2S)
Background introduction
Cost expensive
Located off-shore or at remote place
Unattended working condition
Effected by wind behavior
3/22 Jinrui Ma, Mitra Fouladirad, Antoine Grall UTT - Modelisation et Surete des Systemes (LM2S)
Background introduction°
Figure – Changes of power and pitch angleover wind speed
Figure – Pitch behavior
4/22 Jinrui Ma, Mitra Fouladirad, Antoine Grall UTT - Modelisation et Surete des Systemes (LM2S)
Outline
1 Background
2 Framework – Blade-pitch control system life time prediction
3 Stochastic process application on wind turbine pitch control system
Model description
Deterioration process of actuator
Healthy indicator
Case study
Conclusions
5/22 Jinrui Ma, Mitra Fouladirad, Antoine Grall UTT - Modelisation et Surete des Systemes (LM2S)
Framework – Life time prediction of blade-pitch control system
wind sequence
Mean wind speed identification model
Search the historical deterioration information of blade-pitch control system from data base
Match
Blade-pitch control system deteriorationindicator calculation
Blade-pitch control system RUL prognosis
Failure alert
Blade-pitch control system maintenance intervention Maintenance policy
Wind turbine simulator
Wind class A Wind class B Wind class C Strange wind profile……
NO
YES
NO
YES
Wind turbulence intensity identification model
SCADA
RUL of blade-pitch control system prediction model
Maintenance part
Note: V----Mean wind speed
6/22 Jinrui Ma, Mitra Fouladirad, Antoine Grall UTT - Modelisation et Surete des Systemes (LM2S)
Framework – Blade-pitch control system degradation model
Time
Re
ma
inin
g u
se
ful life
Va
lue
of
ind
ica
tor
Fault
Alert
T1 T2 T3 T4 T5
7/22 Jinrui Ma, Mitra Fouladirad, Antoine Grall UTT - Modelisation et Surete des Systemes (LM2S)
Outline
1 Background
2 Framework – Blade-pitch control system life time prediction
3 Stochastic process application on wind turbine pitch control system
Model description
Deterioration process of actuator
Healthy indicator
Case study
Conclusions
8/22 Jinrui Ma, Mitra Fouladirad, Antoine Grall UTT - Modelisation et Surete des Systemes (LM2S)
Control system of wind turbine
Rotorgenerator
controller
Hydraulic pitch Control system
Wind Generated power, generator rotational speed
Pitch angle
Measurement pitch angle
Pitch
Figure – Control system of wind turbine
9/22 Jinrui Ma, Mitra Fouladirad, Antoine Grall UTT - Modelisation et Surete des Systemes (LM2S)
Model description - Wind speed model*
Wind speed sequence u(t) can be considered as the combination of its 10min’s mean value u(t) and its fluctuation uf (t).
u(t) = u(t) + uf (t)
duf (t) = a(uf , t)dt + b(uf , t)dwt
a(uf , t) = − ufΛ
b(uf , t) = ( 2σ2
Λ)
12
σ is the standard deviation
Λ is the integral time scale
*Calif, R. (2012). PDF models and synthetic model for the wind speed fluctuations based on the resolution of Langevin equation.
Applied energy, 99, 173-182.
10/22 Jinrui Ma, Mitra Fouladirad, Antoine Grall UTT - Modelisation et Surete des Systemes (LM2S)
Model description - Blade pitch actuator model
A fault-free hydraulic pitch system is a piston servo system which can bemodelled by a seconde order dynamic equation*
β + 2ζωnβ + ω2nβ = ω2
nβr
β - measurement blade-pitch angle
βr - reference blade-pitch angle from pitch control system
ωn - natural frequency
ζ - damping ration
The deterioration is considered in the hydraulic pitch actuator, it can bemodeled by changing ωn and ζ.
failure free case : ζ = 0.6, ωn = 11.11 rad/s.
High air content in the oil : ωn = 3.42 rad/s.
Hydraulic leakage : ζ = 0.9, ωn = 5.73 rad/s.
*Merritt, H. E. (1967). Hydraulic control systems. John Wiley & Sons.
11/22 Jinrui Ma, Mitra Fouladirad, Antoine Grall UTT - Modelisation et Surete des Systemes (LM2S)
Model description - Blade pitch controller model
The blade-pitch angle commands are computed by using aproportional-integral (PI) control on the speed error between rated generatorspeed and the filtered generator speed.
Wind Turbine
Low-pass filter
Integrator Saturate Integral Integral Gain
Proportional Gain
Pitch LimitSaturation
Pitch RateSaturation
Rated generator speed
Generator rotational speedFiltered generator rotational speed+-
Speed error
+
+Proportional term
Integral term
Pitch angle command
Figure – Flow chart of blade-pitch controller
12/22 Jinrui Ma, Mitra Fouladirad, Antoine Grall UTT - Modelisation et Surete des Systemes (LM2S)
Model description – Wind Turbine simulator
The blade-pitch actuator and blade-pitch controller are implemented withinMatlab/Simulink environment.
Blade-pitch controller
Blade-pitch actuator
wind speed sequence
FASTNERL-5MW
Figure – FAST/Simulink-based wind turbine simulator coupled blade-pitch actuator deterioration model
FAST - The Fatigue, Aerodynamics, Structures and Turbulence software is a wind turbine simulator designed by the US National
Renewable Energy Laboratory.
13/22 Jinrui Ma, Mitra Fouladirad, Antoine Grall UTT - Modelisation et Surete des Systemes (LM2S)
Outline
1 Background
2 Framework – Blade-pitch control system life time prediction
3 Stochastic process application on wind turbine pitch control system
Model description
Deterioration process of actuator
Healthy indicator
Case study
Conclusions
14/22 Jinrui Ma, Mitra Fouladirad, Antoine Grall UTT - Modelisation et Surete des Systemes (LM2S)
Deterioration process of actuator
The deterioration of actuator can be modelled by a stochastic process. ACompound Poisson Process is a good candidate to model the deterioration.
Blade-pitch system carries out instructions only when wind speed exceeds the rated wind speed
Deterioration appears uniquely when blade-actuator implements the action
The increase deterioration level is independent of the past level of the deterioration and it is random
Figure – Blade-pitch action simulation
15/22 Jinrui Ma, Mitra Fouladirad, Antoine Grall UTT - Modelisation et Surete des Systemes (LM2S)
Outline
1 Background
2 Framework – Blade-pitch control system life time prediction
3 Stochastic process application on wind turbine pitch control system
Model description
Deterioration process of actuator
Healthy indicator
Case study
Conclusions
16/22 Jinrui Ma, Mitra Fouladirad, Antoine Grall UTT - Modelisation et Surete des Systemes (LM2S)
Healthy indicator
By considering the operational conditions of variable wind speed anduncertainty, a dynamic healthy indicator based on real time operational data isproposed.
PitIndt0 =
∑T+t0t=t0|Ωt − ΩtRef | /ΩtRef
T
Ωt is turbine’s rotational speed at time t
ΩtRefis the fault-free turbine’s rotational speed at time t
T is the calculation interval
17/22 Jinrui Ma, Mitra Fouladirad, Antoine Grall UTT - Modelisation et Surete des Systemes (LM2S)
Outline
1 Background
2 Framework – Blade-pitch control system life time prediction
3 Stochastic process application on wind turbine pitch control system
Model description
Deterioration process of actuator
Healthy indicator
Case study
Conclusions
18/22 Jinrui Ma, Mitra Fouladirad, Antoine Grall UTT - Modelisation et Surete des Systemes (LM2S)
Case study
Actuator’s deterioration simulation
excessive air/oil ratio can reduce the natural frequency of pitch actuator
the nature frequency of actuator ωn decreases to 3.42rad/s
the deterioration range of ωn is [0 , 7.69]
X (t) =
N(t)∑i=0
Yi , t ≥ 0
N(t), t ≥ 0 - a Poisson process
Y1, Y2,· · · - independent, identically distributed random variables independent of N(t), t ≥ 0
assume that Yi , i = 1, 2, · · · follows a uniform distribution
X (t) represents the accumulated deterioration at time t on ωn .
19/22 Jinrui Ma, Mitra Fouladirad, Antoine Grall UTT - Modelisation et Surete des Systemes (LM2S)
Case study
0 600 1200 1800 2400 3000 3600 4200 4800 5400 6000
3
5
7
9
11
12
Time (s)
ω n
Trajectory1Trajectory2Trajectory3Trajectory4Trajectory5
Figure – Deterioration trajectories of ωn
1 2 3 4 5 6 7 8 9 100
1
2
3
4
5
6
7
Data Point
PitIn
d −10m
in
Trajectory 1Trajectory 2Trajectory 3Trajectory 4Trajectory 5
Figure – Health indicator PitInd calculated per 10 min for each deterioration trajectory
20/22 Jinrui Ma, Mitra Fouladirad, Antoine Grall UTT - Modelisation et Surete des Systemes (LM2S)
Outline
1 Background
2 Framework – Blade-pitch control system life time prediction
3 Stochastic process application on wind turbine pitch control system
Model description
Deterioration process of actuator
Healthy indicator
Case study
Conclusions
21/22 Jinrui Ma, Mitra Fouladirad, Antoine Grall UTT - Modelisation et Surete des Systemes (LM2S)
Conclusions
A model of hydraulic blade-pitch system considering controller’sdeterioration has been implemented in a wind turbine simulator based onFAST software
A health indicator based on wind turbine operational real data isproposed to estimate the deterioration of hydraulic blade-pitch actuator.
This indicator can well reflect the deterioration.
22/22 Jinrui Ma, Mitra Fouladirad, Antoine Grall UTT - Modelisation et Surete des Systemes (LM2S)
Thank you for your attention !
23/22 Jinrui Ma, Mitra Fouladirad, Antoine Grall UTT - Modelisation et Surete des Systemes (LM2S)