Day 1 - Wednesday 18 October 2017
08:30

Registration And Refreshments

09:00

Chair’s Opening Remarks

Thomas Pump, Head Of Asset Information Systems, E.ON

Using Data To Reach The Full Potential Of Your Wind Farm

09:15

Keynote Address: Using Data To Improve O&M Efficiency And Drive Down The LCOE

  • What has moved on with data capabilities?

  • Utilising automation and smart systems to reduce the number of times you send employees offshore

  • Planning operational activities based on weather forecasting

  • Driving down costs without eroding the certainty in level of results

  • How to increase the life of a turbine and decrease maintenance required for less OPEX expenditure

  • How can you demonstrate return on investment for data initiatives?

Jonas Beseler, Asset Management, Global Tech I Offshore Wind GmbH

09:45

The 1st Open Data Windfarm : “La Haute Borne”

  • Strategically making digitalisation a major focus area in the transformation for greater efficiency

  • Why ENGIE decided to make public the data of the “La Haute Borne” wind farm and what is it all about?

  • Bringing together a community for improved wind turbine operation and developing wind farm services

  • Capitalizing on the increasingly large amount of data available

Nicolas Girard, Head of R&D & Technical Support, ENGIE Green

10:15

Applying Operational And Event Data To Understand The Turbine’s Performance And Reliability Behaviour

  • Why data is needed to describe continuous condition of wind turbines
  • Identifying abnormal behaviour to assess and improve performance
  • Enabling predictive maintenance by association analyses

Stefan Faulstich,  Reliability Analyst,  Fraunhofer IWES

10:45

Morning Refreshments And Networking

11:15

Case Study: Improving Wind Farm Data Quality, Performance Diagnostics And Issue Resolution

Tim Naylor,  Director, Envision

Forecasting Failure And Performance Degredation With Better Measurement Tools And Analysis

11:45

Creating Opportunities For LCOE Reduction By Employing Data, Reliability, and Innovative Tools In Wind Turbine Electrical Components

  • Why electrical components in wind turbines are important for LCOE reduction?

  • What kind of data is required for degradation performance and reliability assessment of electrical components?

  • What are the state-of-the-art tools for design of electrical components to fulfill a specific reliability target?

Huai Wang, Associate Professor, Aalborg University

12:15

The Future Of LiDAR On Operational Wind Farms: OWA Power Curve Tests With LiDAR – Evaluating Nacelle And Floating LiDAR To Validate Power Curves

  • A look at analysis of wind turbine performance tests using LiDAR in a number of use cases – nacelle based, TP based and floating.
  • An overview of the process and the lessons learned.
  • Analysis of the uncertainty with LiDAR relative to traditional cup anemometry.
  • Recommendations and best practices for undertaking power performance tests with LiDAR.
Michael Stephenson, Offshore Wind Associate, Carbon Trust
12:45

Networking Lunch

13:45

Measurement And Optimisation Of Blade Angle Deviations And Quantification Of Subsequent Performance Improvements

  • How to measure blade angle deviations pros/cons of different systems on the market
  • Development of an calculation method to quantify the performance improvements after correction of the blade angles
    • Impact of blade angle misalignments on the measured wind speed on top of the nacelle
    • Is the performance correlation with neighbouring turbines a reasonable approach to quantify the improvement?
  • Verification of the developed calculation method using a LiDAR for the complete characterization of the incoming wind field

Dr Thomas Burchhart, Fleet Performance Analyst, VERBUND Hydro Power GmbH

Understanding Preventative Maintenance, Prognostics And Artificial Intelligence To Improve O&M Activities

14:15

Predictive Analytics Of Turbines – Combining Multiple Data Sources

  • How 1 0min SCADA data is analyzed to identify turbines with deviating signals and applying machine learning methods
  • Integrating vibration measurements with the SCADA data to improve drivetrain analysis
  • Indentifying serious issues early before expensive parts are affected and long downtimes occur
  • Downtime identification and categorisation processes
  • Connecting SAP data to streamline the work process and enrich the detections

Tobias  Winnemöller, Asset Optimization Wind -  Asset & Pipeline Management, E.ON

14:45

Turning Predictive Analysis Into Preventative Maintenance: Fleet-Based Operational Optimisation As An A.I. Task

  • Getting enough good data for the task
  • How combining data sources can result in better predictions
  • Turning data alarms into workable task orders
  • Using signals from vibrations, scada data, temp data and pressure data to get a more effective overall picture of maintenance required

Elena Tsiporkova,  EluciDATA Innovation Lab & OWI-Lab,  Sirris

15:15

Networking Break And Afternoon Refreshments

15:45

Applying Machine Learning And Complex Algorithms For Easy Data Computing And Better Results

  • Addressing underlying issues and difficulties in machine learning
  • Best techniques and real life case study examples
  • Using machine learning to crunch the predictive maintenance data for greater efficiency
  • Could machine learning be employed in security of data?
  • Why an algorithm is only as good as the data used and the problems of applying machine learning to real world applications?
16:15

Speed Networking

Realising the importance of connecting with your peers, we organised a moderated networking session where delegates are prompted to meet others in brief 3/4 minute rounds. The moderator will be keeping track of time and announcing participants when to switch partners.

Make sure you bring lots of business cards along with you!

16:45

Chair's Closing Remarks 

Thomas Pump,  Head Of Asset Information Systems, E.ON

17:00

End of Day 1