Statoil Wind O&M data monitoring, analysis and simulation Dr. Nenad Keseric

Transcription

1 Statoil Wind O&M data monitoring, analysis and simulation Dr. Nenad Keseric Statoil MPR Renewables, Operations Strategy and Support Norcowe, Science meets industry, Bergen Classification: Internal

2 Statoil Renewables- Building our portfolio: Maximise value in offshore wind Up to 9 GW I Increase I Portfolio 2.3MW Hywind Demo 317MW 1.1 Twh / yr* Sheringham Shoal Up to 560 MW Dudgeon 30 MW Hywind Pilot Park Dogger Bank /17 *total average production for Scira 2

3 The O&M as important area within the complete set of business processes Operation and maintenannce 3

4 Map the full value chain integrated Operations/Asset Management approach Operation & Maintenance Production & Dispatching Plan Weather forecasting Production planning O&M planning Do Safe work Personnel and cargo logistics Job execution Monitoring operation Check Process control and optimisation Deviation analysis Act Corrective actions Settlements Improvements Large potential 4 Classification: Internal

5 Balanced Asset Performance Management To run a wind farm as efficient as possible, it is important to build performance based culture. KPI measurements on three levels: High level: set of (KPIs) following closely all aspects of running the park For the Critical processes in the Wind Operating Model a set of Performance Indicators (PIs) are defined Lowest and most detailed level: a set of Critical Parameters (CPs) are defined to follow the O&M processes in the wind farm closely The targeting and planning goes from the top level and down while the analysing and reporting of the performance indicators goes from the lowest level and up 5

6 Close Collaboration with service providers Integrated Operation IO is the integration of people, process, and technology to make and execute better decisions quicker. IO is enabled by the use of real time data, collaborative technologies, and multidiscipline work flows. Plan for short meeting points, called arenas, with fixed participation and fixed agenda. All participants should have access to same data to be able to prepare Databases and data management

7 Benefits of sharing common goals Measure performance... and follow up results and deviations Owners Monthly KPIs for board follow-up Internal KPIs and Performance indicators KPIs Process indicators Targeting and planning Board meeting Monthly operation meeting Weekly operation meeting Daily operation meeting Analysing and reporting Critical parameters If you can not measure it, you can not improve it (Lord Kelvin)

8 Analysis and reporting KPI to be followed up daily, weekly and monthly Source: Wind Farm Management System 8 Classificati on: Internal

9 What happens when we do not acknowledge each others competence areas Gearbox failure: 4 months of lost production due to wrong decision. Lack of owner involvement Lack of incentives to cooperate will in the end hurt the entire wind industry Detailed analysis of historical vibration data show that alarm should have been raised 26. December Allowing ample time to plan mitigating actions and exchange

10 Bazefield- Operation monitoring and analysis 10

11 Hywind- world first and biggest floating turbine 2.3MW WTG in operation since 2009 Located 10km off Norwegian coast at 200m water depth In operation since September 2009 Produced 40 GWh since start-up Capacity factor: Record of 50.2 % in 2011 Overall 41.4% Very good numbers! Maximum wind speed of ca.44m/s and maximum wave height of ca.19m Performance has been good Øyvind Hagen/Statoil

12 Example Hywind production during a storm conditions 24 hour period during storm Dagmar, Dec 2011 Avg. wind speed 16 m/sec Max wind speed 24 m/sec Max significant wave height 7.1m, ie max wave height ~ 12m Power production 96.7% of rated 12

13 Statoil Energy Forecasting System Reliable weather forecasts needed safe marine operations and as basis for nominations and trading Forecast error can not be avoided but it can be minimised and Energy Forecasting and Planning System is using Neural Networks and state of the art methods providing reliable and accurate forecast to Trading department. Terrain Wind farm Flat 9-12 % Complex % Highly complex < 20 % Classificati on: Internal

14 Probabilistic production/wind/wave forecast Original forecast Photo from Anders Wikborg. Statoil Calibrated forecast - with uncertainty 14 - Internal

15 New techniques- Visual Analysis of Multi- Dimensional Data (CMR) «The purpose of visualization is insight, not pictures» - Ben Shneiderman 1999

16 Visual Analytics «Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces.» -- Thomas and Cook, 2005 Main characteristics: Main purpose: confirm/reject hypotheses Directed Automatic methods Operator/User is steering / controlling One, two, 3D and N-dimensions

17 Focus + Context Coordinated Multiple Views

18 Density of Low production Density of samples with a production lower than 200 MW

19 Selecting curtailed production Analysing all samples with a wind speed higher than 7m/s and a production lower than 0,5 MW. This is the selection used in the next plot

20 Curtailed production Density of samples producing less than 200MW with wind speeds higher than 7m/s

21 Best producing but load on components? Analysing the samples of wind turbines that produce more than average given wind speeds between 7 and 12 m/s.

22 Spatial load (stress) on turbines Density over where loads on turbine stress occurs

23 Advanced selections of Curtailed Power

24 Production Animation Security Classificati on: Internal - Status: D ft

25 Change overview Clear change in the relation Natural fluctuation or systematic effect? 25 Classification: Restricted Security Classificati on: Internal - Status: D ft

26 Lost production focus Fit model to data Estimate parameters Estimate (Wald) confidence intervals for parameters Compare curve fitted to different data subsets If confidence intervals not overlapping, statistical significant difference in fitted curves Classification: 26

27 PhD work on wind park O&M simulation model Marine logistics Vessel weather dependence Vessel capabilities Access technology Vessel movements Coordination between vessels Maintenance Wind turbine Wind turbine reliability Power production Wind park location (lat/lon) Fault diagnostics Work planning Resource allocation Spare part management Vessel charter Wind turbine repair 27

28 Simulation results Breakdown of downtime in causes OPEX per category/y Actual and lost production Time-based and energy-based availability (average and in time domain) 28

29 Optimising maintenance strategy Lead time spare parts Lead time vessel Waiting on available time Waiting on Weather Waiting on available technicians Corrective maintenance downtime Transit to WT Accessing WT Work on WT Accessing vessel Condition based maintenance downtime Transit to base 1 st strategy could be increasing resources, having more vessels and technicians ready when a failure occurs; however, this would probably eat away the potential earnings. 2 nd strategy could be to have other types of vessels that make access to wind turbines less dependent on weather and transit times from onshore bases to the wind park shorter. For example with a mother ship in the park continuously. 3 rd strategy is to do maintenance when the wind is low, i.e. do better planning and forecasting. Energy-based availability is a function of theoretical production at wind speeds between cut in/cut out speeds. Still some failure categories (large ones) are difficult to plan only in times of low wind speeds as the heavy lift vessel charter market and spare part availability are uncertain, and the downtime due to the combination of weather and spare parts is difficult to mitigate unless it is possible to foresee in advance when a failure will occur. Condition based maintenance - we believe that the industry can realize the potential increase in availability. Not only the big data it should be smart data! How to utilize the information we gain from analyzing condition and SCADA data?

30 Summary- status and way forward Still a young industry will only a handful of large scale parks operating. Scarce historical data and experience to investigate reliability of turbines. More cost-effective O&M solutions needed to get OPEX/LCoE cost down! Lack of transparency/supply industry protective attitude hindering collaboration across organisational borders. Very different from the offshore oil and gas industry where suppliers, operators and R&D institutions actively share data and information for the benefit of the industry Requires monitoring and analysis, generate data by simulation model, give operators a tool to handle the risks Using good analysis and decision support tolls will increase certainty (reduce risk) through better planning Still need to rely on technical support from the turbine manufacturer will decrease as ISP s and in-house experience increases OPEX 25 /MWh LCOE 120 /MWh Europe Electricity price Norway 50 /MWh

31 The future is floating! Thank you Dr. Nenad Keseric MPR RE Operations Strategy and Support