CATEGORIZING DOWNTIMES AND CALCULATING DOWNTIME AND PRODUCTION LOSSES. BEST PRACTICE AND COST-BENEFIT EREDA

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1 CATEGORIZING DOWNTIMES AND CALCULATING DOWNTIME AND PRODUCTION LOSSES. BEST PRACTICE AND COST-BENEFIT EREDA, LYON 2012

2 CONTENTS OF PRESENTATION 1. EREDA 2. GENERAL RESULTS OF DOWNTIMES PER COMPONENT 3. PERFORMANCE ANALYSIS 4. WEAK POINTS OF WIND FARMS 5. CONCLUSION 2

3 1. EREDA RESOURCE ASSESSMENT Site identification Measurement Measurement management Resource assessment Site characterisation Micrositing and verification Technology selection MESOESCALE AND REGIONAL MAPS Elaboration of regional maps Virtual masts / Virtual series Wind and solar radiation assessment PROJECT ENGINEERING Project definition Grid studies Support to licensing Project engineering CONSTRUCTION Technical specifications Tender documentation Management of supply Construction supervision Construction Management Inspection and acceptance of works OPERATION AND MAINTENANCE Operation monitoring and support Performance verification Electric studies Adapt to new requirements Inspections of turbines and components Maintenance programming Supervision of maintenance EXPERT REPORTS Claims Loss of profit Root cause reports Damage report 3

4 1. EREDA O&M Exploitation and Support to O&M of Wind Farms Different technologies: Gamesa Acciona Vestas Activities include: Several Countries Management of assets Verification of activities in the assets Monitoring and assessment of Production, Power Curve, Availability, Alarms, Performance verification (Producibility, Reactive Power Regulation) Analysis of production losses 4

5 2. GENERAL RESULTS OF DOWNTIMES PER COMPONENT Source: Fraunhofer IWES Institute 5

6 2. GENERAL RESULTS OF DOWNTIMES PER COMPONENT HYDRAULIC SYSTEM SENSORS REGULATION SYSTEM ELECTRICAL SYSTEM DRIVE TRAIN GEARBOX GENERATOR BLADES Source: Fraunhofer IWES Institute 6

7 2. GENERAL RESULTS OF DOWNTIMES PER COMPONENT Breakdown frequency (%) % % % 6.7% 4.3% 3.5% 3.1% 0.5% 0.5% 0.4% 0.4% 0.3% 0.1% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% Source: EREDA Rated Power of WTG: 1.5 MW 2.1 MW 7

8 3. PERFORMANCE ANALYSIS What is it? Set of techniques for data analysis that allows: Understanding the operation of the wind farm in detail (PC, Q, availability, alarms, etc.) Detecting malfunctions and determining causes Estimating amount of not produced energy To implement continuous monitoring of the wind farm To complement predictive analysis To carry out specific analysis 8

9 3. PERFORMANCE ANALYSIS How does it work? Required info: 10 min data from WTGs and WF meterological mast Alarms Service Reports Production data O&M Contract and Error / Alarm Allocation List Orders by grid manager Log Event of grid and substation 9

10 3. PERFORMANCE ANALYSIS Most important parameters: Time based availability, a t a t T Base T T Unavailabl e Base T base, T unavailable depending on the contract 10

11 3. PERFORMANCE ANALYSIS Most important parameters: Time based availability for wind gaps 100 Reflections on Time based availability, a t : Does 80 SET shutdown affect the availability? And MV switchgear? Is it possible to compare WTG availability of different manufacturers? When WTG is available, how wind speed behaves? No Production Rated Power % Time Availability

12 3. PERFORMANCE ANALYSIS Most important parameters: Reflections on Time based availability, a t : Time based availability, differences from WTG counters and calculated values SERVICE REPORTS WTG COUNTERS ALARMS INFO WTG N of SR Duration Duration of Duration of a of SR t by SR a Failures t N of Stops Failures a t 1 0 0:00: % 0:06: % 1 0:12: % 2 6 9:40: % 2:18: % 19 11:42: % 3 0 0:00: % 0:06: % 0 0:00: % 4 0 0:00: % 0:00: % 0 0:00: % 5 0 0:00: % 0:06: % 0 0:00: % 6 1 0:34: % 0:42: % 1 0:50: % 7 0 0:00: % 0:00: % 0 0:00: % 8 1 0:34: % 0:18: % 1 1:34: % 9 0 0:00: % 0:18: % 1 0:21: % :15: % 2:18: % 3 5:19: % Total/Average 9 15:03: % 6:12: % 26 20:02: % 12

13 3. PERFORMANCE ANALYSIS Most important parameters: Energy based availability, a e ae P P LossP LossP depending on the contract 13

14 3. PERFORMANCE ANALYSIS Most important parameters: Energy losses due to power curve limitations or anomalies Pthe Pact Which was the cause? Alarms provide us with this info 14

15 3. PERFORMANCE ANALYSIS Overall values: How wind farm is working, but WTG Mean v (m/s) Time based availability (%) Production (kwh) Loss Production (kwh) ,001, , ,737, , ,013, , ,094, , ,311, ,019 WIND FARM ,157,453 1,928,899 Where to start to work on??? 15

16 4. WEAK POINTS OF WIND FARMS More important parameters: Availability losses per alarm What is the contribution of each alarm to the total loss of availability? Energy losses per alarm What is the contribution of each alarm to the total loss of energy? 16

17 4. WEAK POINTS OF WIND FARMS Alarm Repetition number Time (hours) Average duration (hours) Unavailability associated to the alarm (%) Lost MWh Economic Loss ( 80 /MWh) ( ) Mech TowerVib Stop 2,674 1, % 2, , Pitch CAN ComFail 2, % , Mech Cable Autowind 2,104 1, % 1, , PID PowerLowerThanWindSpeed 1, % , Pitch SafetyTestActiv 1, % , Elec ParkControlStop 1,454 1, % , Pitch Akku Voltage LowStop 1, % , Mech Rpm DiffStop 1, % , FSS Fault % ,

18 4. WEAK POINTS OF WIND FARMS WTG Alarm Repetition number Time (hours) Average duration (hours) Unavailabi lity associated to the alarm (%) Lost MWh Economic Loss (80euros/M Wh) ( ) 1 Extreme tilt moment: xxx knm % , High voltage Li: xx V % , Low flow to tank blocka: xx bar % , Error azimuth angle % , Extreme yaw moment: xxx knm % , Extr. high volt. Li: xx V % , Blade i could not be released % , Extreme tilt moment: xxx knm % ,918 2 Lubrication error & res. empty % ,975 2 Extreme yaw moment: xxx knm % ,138 3 Extreme yaw moment: xxx knm % , Low oil-level, pitch hydraulic % , Trip Q8 Feedback fejl % , Extreme tilt moment: xxx knm % ,

19 Availability Reactive maintenance Elec SafetyChainStop Pitch FreqConvPitch ErrStop Elec Yaw DirectionStop Mech TowerVib Stop Mech Wind Sector Angle Elec CurrentAsymmetryLevel Retrofit MVswitch Panel Fault stop Pitch Angle SPDifferenceStop Pitch EmergencyRun Mech Cable Autowind FSS Fault Elec CurrentSoftstarterHigh Pitch CAN ComFail Pitch Akku Voltage LowStop Elec YawSensor ErrStop Elec FB YawCCW Err Mech RpmFR1 CNT DiffStop Rest Availability Alarm unaivailability 4. WEAK POINTS OF WIND FARMS 100.0% 2.34% 99.3% 2.08% 98.6% 1.82% 97.9% 1.56% 97.2% 1.131% 1.189% 1.30% 96.5% 1.04% 95.8% 0.78% 95.1% 0.508% 0.496% 0.428% 0.52% 0.299% 0.249% 0.228% 0.215% 0.211% 0.207% 0.197% 0.182% 0.165% 94.4% 0.26% 0.097% 0.091% 0.084% 0.084% 0.083% 0.075% 93.7% 0.00% Unavailability Accumulated availability 19

20 Total Economic Loss Mech TowerVib Stop Reactive maintenance Pitch FreqConvPitch ErrStop Elec SafetyChainStop Mech Wind Sector Angle Mech Cable Autowind MVswitch Panel Fault stop WindHighStop Elec ParkControlStop Elec CurrentAsymmetryLevel Pitch Angle SPDifferenceStop Service Pitch EmergencyRun Elec Yaw DirectionStop FSS Fault stolen cable Pitch Akku Voltage LowStop Rest Economic Loss ( ) 4. WEAK POINTS OF WIND FARMS 1,600,000 1,583,238 1,400,000 1,200,000 1,000, , , , , , , , , ,655 84,796 81,587 73,828 66,622 63,615 56,101 53,398 48,789 44,891 40,031 23,408 20, ,

21 4. WEAK POINTS OF WIND FARMS 0.864% Stop Time for subsystem (%) 1.029% 1.397% 2.223% 1.699% 2.753% 0.642% 0.646% 0.155% 0.022% ELECTRICAL CABINET SYSTEM SCHEDULED MAINTENANCE 7.462% % SUBSTATION ALIEN TO WTG HYDRAULIC SYSTEM AND PITCH CONTROL NACELLE INSTRUMENTATION SYSTEM % BUILT-IN SYSTEMS YAW SYSTEM GEARBOX SYSTEM COMPLETE NACELLE WIRING UPGRADES % GENERATOR SYSTEM MID-VOLTAGE SWITCHGEAR HIGH SPEED SHAFT COUPLING SYSTEM 21

22 4. WEAK POINTS OF WIND FARMS Economic Loss 0.497% 0.557% 1.072% 0.980% 3.320% 0.430% 0.237% ELECTRICAL CABINET SYSTEM 8.658% 6.491% % SUBSTATION SCHEDULED MAINTENANCE ALIEN TO WTG NACELLE INSTRUMENTATION SYSTEM BUILT-IN SYSTEMS % HYDRAULIC SYSTEM AND PITCH CONTROL GENERATOR SYSTEM % YAW SYSTEM COMPLETE NACELLE WIRING % UPGRADES GEARBOX SYSTEM MID-VOLTAGE SWITCHGEAR 22

23 CONCLUSIONS Experience shows that detailed analysis of performance of the wind farm require an effort which is widely compensate by benefits. Performance tests can detect 1-5% losses EREDA owns computational algorithms and dedicated software for systematical analysis. Categorizing production and availability losses by alarms provides the ground to address efforts to the most critical aspects of wind farm exploitation. 23

24 Thank you for your attention Cristobal López EREDA S.L.U. Paseo del Marqués de Monistrol 7, Madrid Tel: Fax: Móvil: Fernando de la Blanca EREDA S.L.U. Paseo del Marqués de Monistrol 7, Madrid Tel: Fax: Móvil: Teresa Santonato EREDA S.L.U. Paseo del Marqués de Monistrol 7, Madrid Tel: Fax: Móvil: