Modélisation d'un plan de maintenance base sur les systèmes multi-agents pour les éoliennes offshore

Transcription

1 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

2 Plan Context Multi-agent model of maintenance Simulation and results Conclusion and perspectives 2

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 3

4 Energy (GW) Development of OWF 4

5 Development of OWF Annual onshore and offshore installation EWEA (EUROPEAN WIND ENERGY ASSOCIATION) 5

6 Development of OWF Onshore historical growth compared to EWEA'S offshore projection

7 Production and size 7

8 UK non-carbon energy production 8

9 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 of Copenhagen, Denmark. Image obtained with thanks from Kim Hansen on Wikipedia 9

10 Maintenance cost Preventive Maintenance (PM) to 0.006( /kwh) Corrective Maintenance (CM) 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 10

11 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

12 Planning of maintenance tasks Use of e-maintenanace (telemaintenance, 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

13 Supervise > Multi-agents model 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 Human Resources Turbines Monitoring S > *..1 Impact Material Resources Select & Order > *..1 Use Weather Depends > Maintenance PM VAM CM PrM CBM 13

14 Turbine agents Each Turbine is characterized by: Power rate (P r ), V cin, V rate and V cout 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 Weather Monitoring Turbine Maintenance Energy 14

15 Failure mode and failure cause Resonances within resistor-capacitor (RC) circuits Poor electrical installation Technical defects Lightning Poor component quality and system abuse Poor system design Production defects Turbulent wind Out-of-control rotation Icing problem in extreme weather High vibration level during overload Frequent stoppage and starting Particle contaminations High loaded operation conditions Improper installation (60%) High/Low temperature Corrosion Vibration Electrical Control Blade Failures Yaw System Gearbox Hydraulic Generator windings, Short-circuit Over voltage of electronics components Transformers Wiring damages Damages Cracks Breakups Bends 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 Leakages Weather Human Technical 15

16 Degradation model Wind speed Random phenomena Turbine State State Wave high Lightning Degradation EHF Production Energy Temperature Weather conditions EHF Time Maintenance Informations from other turbines EHF i k + 1 = 0 if f i (k) = 1 EHF max if M i k = 1 γ i. EHF i k deg td deg tr otherwise 16

17 Non-linear degradation on a turbine vs maintenance strategy Turbine 33_CBM Turbine 33_CM Turbine 33_Hybride Turbine 33_SM 17

18 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 Monitoring Weather Turbine M_ resources 18

19 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 Monitoring Weather Resource maintenance 19

20 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 Monitoring Weather Maintenance CM CBM SM Resources 20

21 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 Turbines Weather Monitoring Resources Maintenance 21

22 Cost model GC = is cbm C init + C sm + C cbm + C cm + C down + C dg 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

23 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

24 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 24

25 Results: Cost 25

26 Results: produced energy 26

27 Results : Number of maintenance tasks Maintenance strategy CBM/CM (915) Number of CBM (888) 97% Maintenance strategy SM/CM (1433) Number of SM (1336) 93% Number of CM (27) 3% Number of SM (0) 0% Maintenance strategy CBM/SM/CM (1487) Number of CBM (0) 0% Number of CM (97) 7% CBM/CM SM/CM Hybrid Number of CBM Number of SM Number of CM Total Cost Number of CBM (239) 16% Number of CM (14) 1% Number of SM (1225) 83% [CIE ] 27

28 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 28

29 Failure rate and downtime per sub-system 29

30 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 30

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