WIND FARMS BASED MULTI-AGENT SYSTEM IRISE/CESI France Plan Context - - PowerPoint PPT Presentation

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WIND FARMS BASED MULTI-AGENT SYSTEM IRISE/CESI France Plan Context - - PowerPoint PPT Presentation

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CIE44 & IMSS14 Proceedings, 14-16 October 2014, Istanbul / Turkey MODELING OF MAINTENANCE STRATEGY OF OFFSHORE WIND FARMS BASED MULTI-AGENT SYSTEM IRISE/CESI France Plan Context Renewable energy Importance of wind energy (

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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

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SLIDE 2

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

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

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SLIDE 3

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

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

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SLIDE 4

Development of OWF

Energy (GW)

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

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SLIDE 5

Development of OWF

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

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

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SLIDE 6

Development of OWF

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

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

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SLIDE 7

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 outside

  • f Copenhagen, Denmark. Image
  • btained

with thanks from Kim Hansen on Wikipedia

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

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SLIDE 8

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

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

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SLIDE 9

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

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

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SLIDE 10

Multi-agents model

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Maintenance

Turbines Weather Monitoring

*..1 Use

Supervise >

*..1 Impact Depends > Select & Order >

PM CM CBM PrM VAM

Material

Resources

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

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

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SLIDE 11

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

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Turbine Weather Energy Maintenance Monitoring

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

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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

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

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SLIDE 13

Failure mode and failure cause

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Electrical Control Yaw System Gearbox Hydraulic Blade Failures Lightning Poor electrical installation Technical defects

Resonances within resistor-capacitor (RC) circuits Icing problem in extreme weather High vibration level during

  • verload

Particle contaminations Frequent stoppage and starting High loaded

  • peration 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

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

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SLIDE 14

Degradation model

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2 4 6 8 10 12 1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 115 121 127 133 139 145 151 157 163 169 175 181 187 193 199 205 211 217 223 229 235 241 247 253 259 265 271 277 283 289 295 301 307 313 319 325 331 337 343 349 355 361 EHF Time (day) Turbine 33 Turbine 57

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

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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
  • f production
  • Weather defines the window of intervention of

maintenance team

  • Monitoring inspect the weather windows

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Weather Turbine Monitoring M_ resources

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

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SLIDE 16

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

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Resource maintenance Monitoring Weather

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

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SLIDE 17

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

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Maintenance Resources Monitoring Weather

SM CM CBM

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

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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

  • f maintenance to use

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Monitoring Maintenance Weather Resources Turbines

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

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SLIDE 19

Cost model

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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:

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

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SLIDE 20

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.

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

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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

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

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Results: Cost

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

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Results: produced energy

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

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SLIDE 24

Results : Number of maintenance tasks

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Number of CBM (239) 0% Number of SM (1225) 93% Number of CM (14) 7%

Maintenance strategy SM/CM

Number of CBM (239) 97% Number of SM (1225) 0% Number of CM (14) 3%

Maintenance strategy CBM/CM

Number

  • f CBM

(239) 16% Number of SM (1225) 83% Number of CM (14) 1%

Maintenance strategy CBM/SM/CM

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

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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

  • f its big number of maintenance tasks
  • multi-agent approach and a hybrid strategy generates very

interesting answers

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

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SLIDE 26

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
  • ne day

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