MODELING OF MAINTENANCE STRATEGY OF OFFSHORE WIND … · MODELING OF MAINTENANCE STRATEGY OF OFFSHORE WIND FARMS BASED MULTI-AGENT SYSTEM IRISE/CESI –France CIE44 & IMSS’14 Proceedings,

MODELING OF MAINTENANCE STRATEGY OF OFFSHORE WIND FARMS BASED MULTI-AGENT SYSTEM

IRISE/CESI – France

CIE44 & IMSS’14 Proceedings, 14-16 October 2014, Istanbul / Turkey

Plan

• Context • Renewable energy• Importance of wind energy ( especially offshore wind energy)• Energy cost • Maintenance cost and reduction

• Failure rate of OWF• Most important part• Failure cause and failure mode • Relation between cost and down time in offshore wind farms

• Multi-agent model of maintenance • Maintenance policies • Cost model • Simulator

• Simulation and results • Simulations• Results

• Conclusion and perspectives

2CIE44 & IMSS’14 Proceedings, 14-16 October 2014, Istanbul / Turkey

Context: Renewable energy

• The renewable energy are the best alternative to replace the conventional energy ( Oil, coal, nuclear, etc )

• Solar and wind energies are the most reputed renewable energies

• Offshore wind energy is a very interesting way to produce energy

• Political strategies

• Technological advances


Development of OWF

En

erg

y (

GW

)


Development of OWF

Annual onshore and offshore installation EWEA (EUROPEAN WIND ENERGY ASSOCIATION)


Development of OWF

Onshore historical growth 1994–2004 compared to EWEA'S offshore projection 2010–2020


Offshore Wind farms (OWF)

• The OWF is expected to be the major source of energy

• European countries are leader (117GW)

• Characteristics : • higher wind speeds • smoother, less turbulent airflows; • larger amounts of open space; • the ability to build larger, more cost-effective

turbines (6 to 10 MW)• Cost of installation of offshore turbines is more

important than onshore• Cost of maintenance is very important in OWF

Middelgrunden wind farm outsideof Copenhagen, Denmark. Imageobtained with thanks from KimHansen on Wikipedia


Objective : Maintenance Cost reduction

• Simulation of the behavior of all parts of an offshore wind farm during a to accomplish a maintenance task.

• Evaluation of several maintenance policies

• Maintenance optimisation


Planning of maintenance tasks

• Use of e-maintenanace (tele-maintenance, augmented/virtual reality, … )

• Management of transport of spar parts and personnel of maintenance (beats, helicopters, etc)

• Management canes dimension and position

• Storage centers management


Multi-agents model

10

Maintenance

Turbines Weather

Monitoring

*..1 Use

Su

pe

rvis

e >

*..1 Impact

De

pe

nd

s>

Select & Order >PM

CM CBMPrM

VAM

MaterialResources

Human

Resources

S >

• Each turbine is considered as an agent

• 5 agents type of maintenance: • Preventive maintenance • Corrective Maintenance• Condition Based Maintenance• Video-Assisted Maintenance• Proactive Maintenance

• 1 agent representing the weather

• 1 monitoring agent

• Resources agents • Human resources • Material resources


Turbine agents • Each Turbine is characterized by:

• Power rate (Pr), Vcin, Vrate and Vcout

• State indicator: On/Off, in_maint• Performance: EHF, MAR, inspection delay• Component: Elec_sys, Yew_system, Gearbox,

Hydraulic, Blade• Production: energy, Peff = P * energy and

energy depends of ehf

• Behavior • Produce • Degrade ( time)

• Interactions • Weather degrade the turbine and control the

level of production • Maintenance repair the turbine and increase

the Equipment Health Factor • Monitoring inspect the turbine

11

Turbine

Weather

Energy

Maintenance

Monitoring


Offshore Wind farms (OWF) “Example”

• DOWEC wind farm

• 80 turbines, 6MW each => 480MW

• North sea at the location “NL7”, 50 Km offshore

• Equipped with 50MT mobile crane

• In each nacelle there is 1MT crane

• A supplier with an Offshore Access System is used to transport personal and small components

DOWEC 2003


Failure mode and failure cause

13

Electrical

ControlYaw

SystemGearbox HydraulicBlade

Failures

Lightning

Poor electrical

installation

Technical

defects

Resonances within

resistor-capacitor

(RC) circuits

Icing problem

in extreme

weather

High vibration

level during

overload

Particle

contaminations

Frequent

stoppage and

starting

High loaded

operation conditions

High/Low

temperature

Corrosion

Vibration

Improper

installation (60%)

Poor

system

design

Poor component

quality and

system abuse

Production

defects

Turbulent

wind

Out-of-control

rotation

Leakages•Damages

• Cracks

• Breakups

• Bends

●Generator windings,

●Short-circuit

●Over voltage of

electronics components

●Transformers

●Wiring damages

•Cracking of yaw drive shafts,

• Fracture of gear teeth,

• Pitting of the yaw bearing race

• Failure of the bearing mounting

bolts

•Wearing,

• Backlash,

• Tooth breakage

Weather

Human

Technical


Degradation model

14

0

2

4

6

8

10

12

1 7

13

19

25

31

37

43

49

55

61

67

73

79

85

91

97

10

3

10

9

11

5

12

1

12

7

133

139

145

151

157

163

169

175

181

18

7

19

3

19

9

20

5

21

1

21

7

22

3

22

9

23

5

24

1

24

7

25

3

25

9

26

5

27

1

27

7

28

3

28

9

29

5

30

1

30

7

31

3

31

9

32

5

33

1

33

7

34

3

34

9

35

5

36

1

EH

F

Time (day)

Turbine 33

Turbine 57


Weather agent • It is characterized by :

• Vs (wind speed) probabilistic variation regarding the season

• Hs (high of waves) probabilistic variation regarding the season and the Vs

• Lightning : appears randomly regarding the season • Visibility: appears randomly regarding the season • W1: Vs < 8 m/s and Hs < 1.5 m • W2: Vs < 12 m/s and Hs < 2 m

• Behavior • Update (time) • Degrade

• Interactions • Weather degrade the turbine and control the level

of production • Weather defines the window of intervention of

maintenance team• Monitoring inspect the weather windows

15

Weather

Turbine

Monitoring

M_ resources


Resources agents

• Material resources:• Characteristics

• Number of big boats

• Number of small boats

• Number of Cranes

• Spares

• Behaviors • Degradation

• Update (maintenance)

• Human resources: • Characteristics

• Experience

• Engineer

• Technicians

• Behavior • Get experience

• Update

16

Resource

maintenance Monitoring

Weather


Maintenance agents • Maintenance:

• Characteristics • It is executed at fixed dates • Needed engineers • Needed technicians • Needed cranes • Needed boats • Needed weather window:

• Weather window > W2 → No maintenance action • W1 < Weather window ≤ W2 → AVM telemaintenance• Weather window ≤ W1 → PM, CM, PrM, CBM

• Time of execution

• Behaviors • Get resources• Repair• Release resources

• Interactions • Monitoring maintenance order

17

Maintenance

Resources Monitoring

Weather

SM

CM CBM


Monitoring agent

• Characteristics • Make order in the agents behaviors • Criterion : age, risk level, emergency • Need actions • Concerned turbine • Used maintenance Behaviors

• Behaviors • Monitor • Select • Order

• Interactions • The monitoring agent inspects the

characteristics of the other agents and select the turbine to maintain and the kind of maintenance to use

18

Monitoring

Maintenance

Weather

Resources

Turbines


Cost model

19

Where:

• NT: the number of turbine in the farm

•Nsm, Ncbm and Ncm: the number on systemic, condition-based and corrective maintenance respectively

during the considered period (T unite of time)

• Xsm, Xcbm and Xcm are the decision variable where it is equal to

•

• is an indicator of the state of the turbine

• : measures the degradation level of the turbine tr at time i.

It is computed as follow:


Simulation • Development on NetLogo

• Possibility of defining: • The number of turbines in the farm• The size of maintenance teams

(engineers and technician) • The number of material resources

• Observations: • The generated energy • Weather variation • Turbines stats

• Green : normal mode • Orange : degraded mode • Red : failed mode • Black : in maintenance

• Maintenance agents

• Simulation step = 1 day.


Experimentations

•Size of park : 80 turbine •5 boats, 5 cranes. •5 engineers and 10 technicians • Three types of maintenance strategies are tested:

• SM + CM • CBM + CM • CBM + SM + CM•Weather parameters regarding season: • Wind speed: real data (Le Havre airport)• Wave high : random generation • Lightning : random generation


Results: Cost


Results: produced energy


Results : Number of maintenance tasks

24

Number of

CBM (239)

0%

Number of

SM (1225)

93%

Number of

CM (14)

7%

Maintenance strategy

SM/CMNumber of

CBM (239)

97%

Number of

SM (1225)

0%

Number of

CM (14)

3%


CBM/CM

Number

of CBM

(239)

16%

Number of

SM (1225)

83%

Number of

CM (14)

1%


CBM/SM/CM


Conclusion

• The results clearly show that the hybrid strategy allows the most power to be generated by the farm and the least costly in spite of its big number of maintenance tasks

• multi-agent approach and a hybrid strategy generates very interesting answers


Perspectives

• Try other method of selection (selection of turbine and maintenance methods)

• Use independent resources agents

• Use autonomous agent for each part of the turbine

• Development of a serious game to learn maintenance of OWF.

• Use the simulation to optimize the position of turbines, the team size, and turbines model,…

• reducing the simulation time period to 30 minutes rather than one day

26

27

Documents

MODELING OF MAINTENANCE STRATEGY OF OFFSHORE WIND … · MODELING OF MAINTENANCE STRATEGY OF OFFSHORE WIND FARMS BASED MULTI-AGENT SYSTEM IRISE/CESI –France CIE44 & IMSS’14 Proceedings,