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?

09:45

Maintenance And Reliability Data And Getting The Best Out Of Drone Inspections

  • How can you use data from maintenance activities to improve operations?
  • What can be done on a data front to support technicians going up towers?
  • How to get maintenance data in a structured way so that an algorithm can use it
  • What is done with the data from drone inspections? and what impact does it have in monitoring the health of a turbine?
10:15

Latest Analysis Techniques: How Are Failing Turbines Detected?

  • Can you create relative comparisons with the rest of your fleet?
  • Predictive analytics using weather forecasts to plan maintenance
  • What data is required for recognising underperforming turbines?
    • What tools do you need to have in place?
    • What analysis platforms do you need?
    • How to get to the root cause of the problem and find an actionable recommendation for technicians on what to fix
10:45

Morning Refreshments And Networking

11:15

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

Forecasting Failure And Performance Degredation With Better Measurement Tools And Analysis

11:45

Understanding Power Performance Degradation And Possible Power To Assess Performance Over Lifetime

  • Exploiting SCADA to understand how the performance of a turbine develops over its lifetime 
  • Taking ageing and erosion data into consideration when calculating power output
  • Best techniques for reducing maintenance required whilst maintaining optimal power output 
  • The possible power: what a wind farm could produce without environmental restrictions 
12:15

Getting Better Wind Measurements For Improving The Output Of Your Assets 

  • Employing more accurate anemometers for enhanced wind predictability and using wind flow and mast flow models

  • Comparing the output vs. the predicted power and using recursive loops to fit your wind farm output

  • Comprehending the difficulties of calculating loads and wind speeds in complex/mountainous terrains to better predict performance? 

  • Desktop linear flow models vs. cfd models with climatological physics

  • Maintaining uncertainty at an acceptable level and lifting environmental restrictions to increase performance
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

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, Team Leader Data Innovation, Sirris

14:45

Condition Monitoring Utilising Diagnostics, Prognostics And Sensor Flow

  • Understanding fixed analysis and images to recognise specific error, deviation and pattern matching
  • Using virtual simulation models whilst combining scada and vibration data to assess for maintenance needs and possible malfunctions
  • Learning to interpret data earlier and faster to spot potential failures and reduce risk
  • How to predict what will happen based on measurements in the turbines
  • Monitoring temperature from gearboxes and generators from scada data to optimise production
  • Electrical component monitoring
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

Is The Wind Industry Ready For Artificial Intelligence (A.I.)?

  • What have we learned in A.I that can be implemented to improve wind industry operations and maintenance?
  • Tools that can be used in the A.I. suite alongside machine learning network to network
  • Allowing enough time for machines to learn from mistakes
  • Why the need for cloud computing in order for A.I. to work
  • Why its application to the real world is so difficult and how data quality slows progress and ability to learn
  • What work is currently being carried out using neural networks?
16:45

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!
17:15

Chair's Closing Remarks 

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

17:30

End of Day 1