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 2 10 th 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 3 10 th Conference MOSIM, 07 November 2014
Development of OWF Energy (GW) 4 10 th Conference MOSIM, 07 November 2014
Development of OWF Annual onshore and offshore installation EWEA ( EUROPEAN WIND ENERGY ASSOCIATION ) 5 10 th Conference MOSIM, 07 November 2014
Development of OWF Onshore historical growth 1994 – 2004 compared to EWEA'S offshore projection 2010 – 2020 6 10 th Conference MOSIM, 07 November 2014
Production and size 7 10 th Conference MOSIM, 07 November 2014
UK non-carbon energy production 8 10 th Conference MOSIM, 07 November 2014
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 Middelgrunden wind farm outside turbines (6 to 10 MW) of Copenhagen, Denmark. Image obtained with thanks from Kim • Cost of installation of offshore turbines is more Hansen on Wikipedia important than onshore • Cost of maintenance is very important in OWF 9 10 th Conference MOSIM, 07 November 2014
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 Maintenance Weather reliability of the OWF position concept Conditions turbine Maintenance plan/ cost 10 10 th Conference MOSIM, 07 November 2014
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 11 10 th Conference MOSIM, 07 November 2014
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 12 10 th Conference MOSIM, 07 November 2014
Multi-agents model • Each turbine is considered as an agent *..1 Impact Turbines Weather • 5 agents type of maintenance: • Preventive maintenance Depends > • Corrective Maintenance Supervise > • Condition Based Maintenance • Video-Assisted Maintenance • Proactive Maintenance Maintenance Select & Order > PM Monitoring VAM • 1 agent representing the weather PrM CM CBM • 1 monitoring agent S > • Resources agents *..1 Use Human Material • Human resources Resources Resources • Material resources 13 10 th Conference MOSIM, 07 November 2014
Turbine agents Weather • Each Turbine is characterized by: Monitoring • Power rate (P r ), V cin , V rate and V cout Turbine • State indicator: On/Off, in_maint • Performance: EHF, MAR, inspection delay Energy • Component: Elec_sys, Yew_system, Gearbox, Hydraulic, Blade Maintenance • 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 E quipment H ealth F actor • Monitoring inspect the turbine 14 10 th Conference MOSIM, 07 November 2014
Failure mode and failure cause Resonances within Production Poor component Icing problem Frequent Improper resistor-capacitor defects in extreme installation (60%) quality and stoppage and weather starting (RC) circuits system abuse Turbulent High/Low Poor electrical wind Poor High vibration temperature Particle installation system contaminations level during overload design Corrosion Technical Lightning Out-of-control High loaded defects Vibration rotation operation conditions Electrical Blade Yaw Gearbox Hydraulic Control System Failures • Damages • Wearing, ● Generator windings, Leakages • Cracking of yaw drive shafts, • Cracks • Backlash, ● Short-circuit • Fracture of gear teeth, • Breakups • Tooth breakage ● Over voltage of • Pitting of the yaw bearing race • Bends electronics components • Failure of the bearing mounting ● Transformers Weather bolts ● Wiring damages Human Technical 15 10 th Conference MOSIM, 07 November 2014
Degradation model Turbine Wind speed State State Random phenomena Wave high Lightning Degradation EHF Production Energy Temperature EHF Weather conditions Maintenance Informations from other turbines Time 0 𝑗𝑔 𝑔 𝑗 ( 𝑙 ) = 1 𝑗𝑔 𝑁 𝑗 𝑙 = 1 𝐹𝐼𝐺 𝑗 𝑙 + 1 = 𝐹𝐼𝐺 𝑛𝑏𝑦 𝛿 𝑗 . 𝐹𝐼𝐺 𝑗 𝑙 − deg 𝑢𝑒 −𝑒𝑓 𝑢𝑠 𝑝𝑢ℎ𝑓𝑠𝑥𝑗𝑡𝑓 16 10 th Conference MOSIM, 07 November 2014
10 12 0 2 4 6 8 Non-linear degradation on a turbine vs maintenance strategy 1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 151 161 171 181 Turbine 33_CBM 191 201 211 221 231 241 251 261 271 281 Turbine 33_CM 291 301 311 321 331 341 351 361 Turbine 33_Hybride 371 381 391 401 411 421 431 441 451 461 471 Turbine 33_SM 481 491 501 511 521 531 541 551 561 571 581 591 601 611 621 631 641 651 661 671 681 691 17
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 Turbine • W1: Vs < 8 m/s and Hs < 1.5 m • W2: Vs < 12 m/s and Hs < 2 m Monitoring • Behavior Weather • Update (time) • Degrade M_ resources • 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 18 10 th Conference MOSIM, 07 November 2014
Resources agents • Material resources: • Characteristics • Number of big boats Weather • Number of small boats • Number of Cranes • Spares • Behaviors maintenance Monitoring • Degradation Resource • Update (maintenance) • Human resources: • Characteristics • Experience • Engineer • Technicians • Behavior • Get experience • Update 19 10 th Conference MOSIM, 07 November 2014
Maintenance agents • Maintenance: • Characteristics Weather • It is executed at fixed dates • Needed engineers • Needed technicians Monitoring Resources • Needed cranes • Needed boats Maintenance • 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 CM CBM • Get resources SM • Repair • Release resources • Interactions • Monitoring maintenance order 20 10 th Conference MOSIM, 07 November 2014
Monitoring agent Weather • Characteristics • Make order in the agents behaviors • Criterion : age, risk level, emergency Turbines Monitoring • Need actions • Concerned turbine Maintenance • Used maintenance Behaviors • Behaviors Resources • 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 21 10 th Conference MOSIM, 07 November 2014
Cost model 𝐻𝐷 = 𝑗𝑡 𝑑𝑐𝑛 × 𝐷 𝑗𝑜𝑗𝑢 + 𝐷 𝑡𝑛 + 𝐷 𝑑𝑐𝑛 + 𝐷 𝑑𝑛 + 𝐷 𝑒𝑝𝑥𝑜 + 𝐷 𝑒 Where: • NT : the number of turbine in the farm • N sm , N cbm and N cm : the number on systemic, condition-based and corrective maintenance respectively during the considered period (T unite of time) • X sm , X cbm and X cm 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: 22 10 th Conference MOSIM, 07 November 2014
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. 23 10 th Conference MOSIM, 07 November 2014
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