Wind turbine owner's roundtable:
Big Data & Predictive Analysis

24 OCT - Session 1
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How is this done?
What a relief… to sit down at the same table with only other wind turbine owners. The best possible conditions to have, what the Global Head of O&M at Enel said about 2017: “A productive and transparent dialog between all of us”. If you are tired of sales pitches, then this is your event: only wind turbine owners & no sales pitches. This is your best oasis to have the true version of end-user experiences from the wind industry.  Feel free to explore which topics & models will be discussed, who has booked and evaluations from 2017

Description:
Big data has been a buzz word for several years but given tools and methods allwos the owners to take a more practical view on these possibilities. Wind turbine owners have been collecting data and analyzing assets to detect turbines’ failures or low performance at earlier stages. The industry is also getting more advanced on developing prognosis tools and systems. A huge challenge is to understand and detect faults and failures from very large amounts of data. To make this challenge even more advanced the owners also try to predict faults and failures with as much leadtime as possible. At this rountable we will capture the current challenges that owners have within Big Data and predictive analysis and also look closer at various techniques to detect fault and failures, including machine learning.  

Agenda suggested for discussions:

  • Which characteristics should we have on the input data to the data analytical algorithms of a predictive maintenance system?
  • How to extract value from operating data?
  • How predictive analyses can provide valuable information to shed light on the root causes and mechanisms leading to failure?
  • How failure data derived from maintenance and spare-part records can be used for weak-point analysis.
  • Which tools and methods are at hand for the owner to do data analysis for early fault detection in wind turbines
  • Are machine learning models for fault detection and turbine performance in the far away future on in reach for the owner?
  • Is Big Data ready to help us for life time extension?
  • Is Big Data enough to predict component failures
  • Real load measurement for predictive real lifetime
  • Using of neuronet for predictive analysis
  • How can big data be used to predict component failure?
  • What are people monitoring to identify componant failure risk - activation, turbulence intensity, tower acceleration etc? how is it being used to predict risk of failure?
  • Small customer fleet to gauge a prediction on OEM's wont share lifetime predictions, Ways to model predictions
  • How can the number of hours technicians Need to work on turbines be lowered by using Big Data Information? How can damages to turbines be forseen?
  • How can we, as an asset manager, improve the outsourced O&M using big data and predictive analysis. Shouldn't we involve our O&M partner, and find a win-win? Is there already 'real/proven' experience of the benefit of Big Data & Predictive Analysis?

Case Studies presented and discussed by:

  • José Vidal, Enel Green Power
  • Roland Flaig, Wind Operations - Head of EU North at E.ON - Sweden
  • Jacek Aronowski, Deputy Director Operations Department at PGE ENERGIA ODNAWIALNA S.A. - Poland
  • Johannes Derneryd, Engineer WindPower Plants at Stena Renewables - Sweden
  • Eoin O'Donovan, Asset Manager at Brookfield Renewable Energy Group - Ireland

Your top challenge – directly included
You and your most relevant challenges will be directly included in this roundtable discussion. Prior to the event you will share your top challenge and your input for other’s top challenges – within the topic of this roundtable. When you arrive you will find a booklet with your top challenge at the top of one page and with suggested approaches from your peers around this roundtable underneath. There will be one such page for each participant and this will be the foundation for you discussions.

Companies to be found at various roundtables in 2018:

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