multi-agents pour les oliennes offshore Modeling Of Maintenance - - PowerPoint PPT Presentation

Modélisation d'un plan de maintenance base sur les systèmes multi-agents pour les éoliennes offshore Modeling Of Maintenance Strategy Of Offshore Wind Farms Based Multi-agent System

IRISE/CESI – France

Plan

• Context
• Multi-agent model of maintenance
• Simulation and results
• Conclusion and perspectives

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10th Conference MOSIM, 07 November 2014

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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Development of OWF

Energy (GW)

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Development of OWF

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

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Development of OWF

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

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Production and size

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UK non-carbon energy production

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Offshore Wind farms (OWF)

• The OWF is expected to be the major source of

energy

• European countries are leader (117GW/ 150GW)
• 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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Maintenance cost

• Preventive Maintenance (PM) 0.003 to 0.006(€/kWh)
• Corrective Maintenance (CM) 0.005 to 0.01 (€/kWh)
• The contribution of maintenance cost in the price is 25 to 40%.

Size and reliability of the turbine Maintenance concept OWF position Weather Conditions Maintenance plan/ cost

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Objective : Maintenance Cost reduction

• Simulation of the behavior of all parts of an offshore wind farm

during to accomplish a maintenance task.

• Evaluation of several maintenance policies
• Maintenance optimisation

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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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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

10th Conference MOSIM, 07 November 2014

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

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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

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Degradation model

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Turbine Degradation Weather conditions Wave high Lightning Temperature Wind speed Time Production Maintenance EHF Random phenomena EHF Energy Informations from other turbines State State

𝐹𝐼𝐺𝑗 𝑙 + 1 = 𝑗𝑔 𝑔

𝑗(𝑙) = 1

𝐹𝐼𝐺

𝑛𝑏𝑦

𝑗𝑔 𝑁𝑗 𝑙 = 1 𝛿𝑗. 𝐹𝐼𝐺𝑗 𝑙 − deg𝑢𝑒 −𝑒𝑓𝑕𝑢𝑠 𝑝𝑢ℎ𝑓𝑠𝑥𝑗𝑡𝑓 10th Conference MOSIM, 07 November 2014

Non-linear degradation on a turbine vs maintenance strategy

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2 4 6 8 10 12 1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 151 161 171 181 191 201 211 221 231 241 251 261 271 281 291 301 311 321 331 341 351 361 371 381 391 401 411 421 431 441 451 461 471 481 491 501 511 521 531 541 551 561 571 581 591 601 611 621 631 641 651 661 671 681 691 Turbine 33_CBM Turbine 33_CM Turbine 33_Hybride Turbine 33_SM

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

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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

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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

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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

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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:

𝐻𝐷 = 𝑗𝑡𝑑𝑐𝑛 × 𝐷𝑗𝑜𝑗𝑢 + 𝐷𝑡𝑛 + 𝐷𝑑𝑐𝑛 + 𝐷𝑑𝑛 + 𝐷𝑒𝑝𝑥𝑜 + 𝐷𝑒𝑕

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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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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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Results: Cost

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Results: produced energy

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Results : Number of maintenance tasks

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Number of CBM (0) 0% Number of SM (1336) 93% Number of CM (97) 7%

Maintenance strategy SM/CM (1433)

Number of CBM (888) 97% Number of SM (0) 0% Number of CM (27) 3%

Maintenance strategy CBM/CM (915)

Number

• f CBM

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

Maintenance strategy CBM/SM/CM (1487)

CBM/CM SM/CM Hybrid Number of CBM 888 239 Number of SM 1336 1225 Number of CM 27 97 14 Total 915 1433 1487 Cost 6626 6250 4947

[CIE44 2014]

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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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Failure rate and downtime per sub-system

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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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