wind farm data management and analysis north america

Day 1 - Tuesday 14 March 2017

Registrations And Refreshments 


Chair’s Welcome And Opening Remarks

Benjamin Rice, Operations Engineering Manager, Pattern Energy

How to Handle Big Data And Use it Effectively To Achieve Greater Efficiency For AEP (Annual Energy Production)


Panel Session: How Have We Used Big Data So Far And What Else Can We Do To Improve Efficiency

  • Examples of breakthrough moments with data sets

  • Why data modelling will continue to exponentially increase efficiency

  • What role is data likely to play in future construction of wind farms

Jaimeet Gulati, Associate Director – Global Operations Performance Management, EDPR 

Nenad Keseric, Operations Manager, Statoil


Presentation: Making The Most Of Your Big Data To Ensure Maximum Use 

  • Ensuring you get reliable data and knowing what the limitations are

  • How to get more data out of OEMs and increase the number of tags

  • Getting data that is representative of your site for scalable figures

  • Integrating external data and extracting its value for added analysis in fleet efficiency

David Korim, Asset Performance Manager, GE Renewables


Case Study: How To Be Smart With Your Data To Reduce Downtime

  • Improving site and maintenance workflow with O&M data

  • How to reduce down time and plan operations for least impact

  • Weather forecasting to avoid bad conditions and aborted maintenance operations

  • Predictive maintenance to reduce visits and choosing the least disruptive times

  • Using historical data and feedback loops to save on time and cost

Gerry Lindauer , Manager of Performance Assurance, Siemens


Morning Refreshments And  Networking Opportunities


Data Networks: Blending And Integrating Data from Multiple Sources

  • Getting Data from turbines to provide full transparency for reporting purposes

  • Validating human data to eradicate errors

  • Supervisory controls being used today

To join this session please contact Dominic Coyne:

Big Analytics Made Easier: The Increasing Trend Of Automation Predictive Maintenance


A Users Experience: How To Make Big Analytics Easier 

  • The usability of the software available

  • What you can achieve with heavy processing power and large data sets

  • How most software works and what is most important for different parties using it

  • Testing results and comparisons with other systems

Senior Representative, IBM


The Analytical Value In Remote Monitoring And Sensing 

  • Using Historical Data to re-prioritise troubleshooting steps

  • Creating feedback loops for new reference data

  • Ensuring a future knowledge base for ‘go to fixes’ in the future

To join this session please contact Dominic Coyne:


Lunch And Networking Opportunities 



The Automation Arms Race, Creating Solutions for Asset Managers

With the increasing numbers of solutions and analytical firms, asset managers are faced with big choices:

  • What software and platforms are available?

  • What is the right information to bring to a performance engineers dashboard?

  • What data should be standardised or automated and how does this vary with each task?

  • How do firms offering automation/analytics propose to help?

Josh Fausset , Manager – Renewable Energy, OGE Energy

Amir Zohar, Director, Suzlon  


Specific Approaches For Failure Forecasting And Predictive Maintenance

  • Finding failures and difference types of discovery methods, configurations and failure modes

  • What happens pre failure: creating data for future cycles

  • Challenges of setting up systems to tag the data

  • Closing the loop to use failure data to feedback for more effective operation and maintenance in the future

Ninochska Maldonado-Bosthworth , Lead Fleet Engineer, EDF RE


Condition Monitoring And Artificial Neural Networks

  • Building an array of sensors that can measure vibrations across the drive train

  • Using the array to detect anomalous behaviour and indentify faults

  • Automation in determining severity of faults

  • What really is condition monitoring: time domain vs. frequency domain

To join this session please contact Dominic Coyne:


Afternoon Refreshments And  Networking Opportunities

Finding The Business Case For Investment From Pre Construction Site Data


Using Preconstruction And Wind Resource Assessment Data To Reduce Risk In Projects and Create Further Investment 

  • Why it is important to properly understand/reconcile differences from prediction and actual operating site data

  • How do you validate models and lower accuracy uncertainty

  • Creating scalable data for forecasting performance output once wind farm is built

  • Positively correlated relationship of preconstruction data and level of investment

Chad Ringley, Director – Energy Analytics, Pattern Energy


Data Analytics Method For Understanding Turbine Underperformance

  • Why site specific turbine performance differs from the warranted power curve?
  • The power deviation matrix approach and a few others in discussion
  • What can a machine learning/data analytics method offer?
  • Kernel PLUS method for understanding and predicting site-specific turbine performance.

Yu Ding, Professor, Texas A&M University

Hoon Hwangbo , Ph.D. Student, Texas A&M University

Owner Operator Closed Door Round Tables


Delegates will select one of the following peer-to peer discussions from the strategic or technical streams listed below. You will move into small break-out groups where discussions will be off the record and led by an expert on the given topic. Delegates are encouraged to bring along their considered ideas and questions in order to facilitate a meaningful and in depth discussion:

Stream 1 - Strategic

A. How operators tend to take unnecessary risk from limited data campaigns 

B. Real concrete examples and ways to encourage investment in data from upper level executives 

Host:  Ninochska Maldonado-Bosthworth, Lead Fleet Engineer, EDF RE

C. How to manage data teams and monitoring systems for best results? 

Host:  Dushyant Tank, Reliability Engineer, Pattern Energy

Stream 2 - Technical

D. Utilising your analytics team to create your own reference data

E. Where can data improvements and further analysis be made?

Host: Shantanu Mahajan, Asset Performance Manager, EDP Renewables

F. Streamlining the adoption of artificial neural networks


Roundtable Feedback

Delegates return to the main plenary room to hear the discussion leaders sharing the main outcomes of their discussions.


Chair’s Closing Remark

Benjamin Rice , Operations Engineering Manager, Pattern Energy


Close Of Forum Day 1