Datenanalyse, Methoden und Prozesse für Ertragsmaximierung

Transcription

1 Datenanalyse, Methoden und Prozesse für Ertragsmaximierung Spreewind 2014 Author Date Reference GRAY V1.0

2 Overview History, motivation Infrastructure, software, processes Diagnostics, prognostics and CBM Summary

3 What s So Great About SCADA? Advantages and opportunities provided by available data

4 How Did I Get Here? Optimisation of Engine Reliability Optimisation of Wind O&M

5 Multiple Failure Modes 35 Proportion Of All Failures [%] Germany (WS) [6] Germany (LWK) [6] Denmark (WS) [6] Sweden [7] Finland [7] Germany [7] Ref: Reliawind Electrical System Control System Blades / Pitch Mechanism Gearbox Yaw System Hydraulics Generator Ref: Gray & Watson, Wind Energy 2009 Wind turbine reliability affected by a high volume and frequency of failure modes

6 Wealth of Information Large volumes of information available to support analysis tasks

7 Multiple Applications of SCADA Analytics WS RP BA YM LH HS RS PD 0,0 0,2 0,4 0,6 0,8 1,0 1,2 1,4 1,6 1,8 2,0 Normalised Parameter [-] Production forecasting Failure prognostics Yaw misalignment detection & correction Outlier detection SCADA can be applied for a large range of tasks relating to wind turbine operations

8 Infrastructure, Software, Processes Requirements for effective application of SCADA analytics

9 Next Level SCADA EFFICIENT PROBLEM IDENTIFICATION OPTIMISATION Condition Monitoring, Business Intelligence, Reporting, Analytics SCADA OEM #1 Centralised SCADA Database SCADA OEM #2 SCADA OEM #3 Multiple Information Sources (bill of materials, service reports, CMS etc)

10 Software & Analytics Software should allow the operator to quickly evaluate past, present and future operations

11 Process Integration FOCUS ANALYSE INSPECT CORRECT CONFIRM MONITOR Data analytics must be effectively integrated into operational processes for value generation

12 Practical Examples Diagnostics, Prognostics, CBM

13 Diagnostics & Prognostics Failure mechanisms can be understood, modelled, quantified and monitored

14 Knowledge Database Uptime HARVEST knowledge DB (example: Blade) Structured information storage within an integrated system support continuous improvement

15 Residual [ C] Bearing Temperature [ C] Outlier Detection = Measured = Model Measurement Point [-] Measurement Point [-] Fully automated and accurate outlier detection using physics-based models

16 Survival Probability [%] Proportion of failed turbines [%] Proportion of failed turbines [%] Physics of Failure Failure Rate vs Time Failure Rate vs Time at HIGH LOAD Operational Time [years] Time at Condition [years] Turbine Physics of Failure modelling combined with SCADA improves prognosis accuracy

17 Cost Minimisation Planned vs Unplanned Repair Costs Optimium replacement interval Introduction of well-timed preventative maintenance reduces overall costs

18 Failure Probability [%] Improvement: Recommendations and Potential Recommendations Intermediate replacement, instead of run-to-failure Prioritisation according to accumulated damage Online monitoring (SCADA, alarms) Failure rate tracking and adaptation of plan Failure Probability Planned replacement End of Life online monitoring & regular inspection Time [years] Combination of maintenance scheduling, monitoring and prognostics for minimum total cost

19 Summary Data analytics can support a wide range of optimisation activities Requirements: data infrastructure, software, analytics, process integration Physics based methods provide transparency and are highly effective A combination of diagnostics, prognostics and CBM provides financial gains Power of SCADA: available, plentiful, continuous, detailed, growing

20 Thank you