Valarie Hines: 2013 Sandia National Laboratoies Wind Plant Reliability Workshop

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Data Needs for Benchmarking Your Reliability Performance

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Valarie Hines: 2013 Sandia National Laboratoies Wind Plant Reliability Workshop

  1. 1. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000. Data Needs for Benchmarking Your Reliability Performance Sandia National Laboratories Continuous Reliability Enhancement for Wind (CREW) Project Valerie Hines, Lead Reliability Analyst Alistair Ogilvie, Project Lead Cody Bond, Data Team SAND Report # 2013-6547C
  2. 2. Sandia National Laboratories Exceptional Service in the National Interest 2 Wind Energy Technologies Department FOCUS  Industry needs  Reducing energy cost  Promoting large-scale deployment of clean, affordable energy GOALS  High fidelity modeling  Blade design to eliminate barriers  Increased energy capture & improved efficiency  Increased system reliability  Testing at reduced cost
  3. 3. CREW: Continuous Reliability Enhancement for Wind 3 Goal: Create a national reliability database of wind plant operating data to enable reliability analysis  Protect proprietary information  Enable operations and maintenance cost reduction  Increase confidence from financial sector and policy makers  Benchmark reliability performance  Track operating performance at a system- to-component level  Characterize issues and identify technology improvement opportunities Sandia partners with Strategic Power Systems (SPS), whose ORAPWind® software collects real- time data from wind plant partners Key Objectives: Method:
  4. 4. Performance Dashboard  Cloud based online analysis – 24x7  RAM and Performance data analysis  One minute statistical data – everyone else uses 10 minute data  ORAP® Transformed data  Fault / Event analysis  Industry benchmarks  IEC / IEEE Availability reporting  NERC GADS reporting  Data Completeness and Quality monitoring metrics ORAPWind.spsinc.com 4
  5. 5. Presentation Content  Preliminary Results for the 2013 CREW Benchmark • Results not yet finalized • October 1: Benchmark published (http://energy.sandia.gov/crewbenchmark) • Sept. 24-25: Sneak peak at “Optimizing Wind Power O&M”  Illustrations of 3 very different kinds of reliability benchmark metrics and graphs • Spell out the data needs for each 5
  6. 6. General Data Types  Raw SCADA • Key data streams: Operating or fault state, Wind speed, Power • Recorded very rapidly (on the order of seconds)  Summarized SCADA • Raw SCADA that has been statistically summarized – CREW uses mean, standard deviation, minimum, and maximum; and sometimes mode (most common) for non-numeric values like state • Recorded at regular intervals (on the order of minutes) – CREW uses industry-standard 10 minutes  (SCADA) Events • Summarized downtime records, with a start and end date • Shows symptom; generally less detail about root cause  Work Orders • Summarized downtime records, with a start and end date • Includes more detail about root cause  Analysis Timeframe (Easy to forget!) • Time period over which data was collected and analyzed – May be different for different plants or turbines 6
  7. 7. Time Accounting Data Needs  Raw SCADA • Select the best ONE state/fault for each moment in time • Map each state/fault to one time category – IEC’s Availability standard (61400-26-1) provides a great information model • Sum time in each category  Analysis Timeframe • Information Unavailable comes from knowing how much time there was and subtracting how much time is already accounted 7 Event & SCADA Data Source:
  8. 8. Power Curve Data Needs  Summarized SCADA • Create discrete “buckets” for wind speed and power, to allow grouping • Count the number of SCADA time periods in each combination of wind speed bucket and power bucket • Plot Power bucket vs. Wind Speed bucket, with dot size (or other 3-D option) proportional to the count of SCADA periods 8 Event & SCADA Data Source:
  9. 9. Event Frequency vs. Downtime Data Needs  SCADA Events • Map each state/fault to a wind turbine system (or component) • Count total number of events for each system; Sum time for each system  Analysis Timeframe  Work Orders • Combining SCADA with good work orders can reduce “Wind Turbine (Other)” 9 Event & SCADA Data Source:
  10. 10. Observations  Even basic benchmarking requires some deep up-front thought • How to organize time (information categories) • How to organize wind turbine (system breakdown) • How to map existing data to desired results  CREW Benchmark results are stabilizing • 2013 Benchmark is looking similar to 2012 Benchmark  Electronics Work Orders are still key • Biggest reliability impact is still from the “Other” system • Automated benchmarking for more detailed root cause relies heavily on electronic work orders 10
  11. 11. Accessing More Information  The 2012 Benchmark presentation and companion technical report are at http://energy.sandia.gov/crewbenchmark  Sandia keeps an archive of our past wind plant reliability publications at http://energy.sandia.gov/?page_id=3057#WPR  All wind plant owners, operators and OEM’s are invited to participate. Please contact:  The data in the CREW database is proprietary to our partners. We are not able to disclose non-aggregated data. • Due to a large volume of requests and limited funding, Sandia is not able to provide customized subsets of aggregated data outside the Department of Energy’s Energy Efficiency and Renewable Energy program. • Strategic Power Systems, our corporate partner in this effort, may be able to assist with more information about wind plant reliability. For more information, please contact SPS’ Jim Thomas. 11 Jim Thomas, ORAPWind® Project Manager Strategic Power Systems, Inc. Jim.Thomas@spsinc.com (704) 945-4642 Valerie Hines, Lead Reliability Analyst Sandia National Laboratories vahines@sandia.gov (925) 294-6490

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