SESSION 1: Deriving value from Big Data
Session Chair: Claudia Puyals, Senior Analyst, Performance Assessment, IBLA, AWS Truepower
Session Description
In this session, several Big Data experts will explain the application of Big Data to the wind industry and how wind farm operators can take advantage of its full potential. Big Data contains valuable information, and its proper analysis can give owners and operators a deep insight into plant performance while revealing key elements for O&M cost reduction. During the session, the audience will learn how to overcome the complexity of Big Data analysis and will discover a number of innovative applications to get more efficient plants, effective power curve tests and improved power forecasting.
Learning objectives
- Innovative Big Data applications for the wind industry
- Analysis techniques for Big Data
- Performance improvement and costs reduction through Big Data
Speakers
- Bruno Pinto, R&D and Data Analysis Manager, Sereema
Combining IoT and Big Data Analytics to improve Wind Farm Performance - Joana Mendes, Post Processing Applications Scientist, UK Met Office
The EME – Power Forecasting Improvement Research Project - Erik Salo, Research Assistant, University of Strathclyde
Value from free-text maintenance records: converting wind farm work orders into quantifiable, actionable information using text mining - Axel Albers, Managing Director, Deutsche WindGuard
Power Curve Evaluation Based on Limited Wind Speed Range - Mark Stephens-Row, New Markets Sales Engineer, The Weather Company, an IBM Business
The big data of weather and predictive maintenance
SESSION 2: Enhancing turbine performance using data
Session Chair: Lars Landberg, Director, Group Leader, Renewables, Strategic Research and Innovation, DNV GL
Session Description
This session will explain various ways of enhancing turbine performance using data. We will get perspectives from many places around the globe, and illustrate findings with real wind-farm data from thousands of wind farms. Methods for enhancing the performance will include root-cause analysis, ways of using yaw control, and data-based and data-derived systems.
Learning objectives
- Understand what turbine performance is
- Learn how data can be used to enhance turbine performance
- See how real wind-farm-derived data can be used to enhance turbine performance
Speakers
- Peng Hu, Engineer, Longyuan Beijing Wind Power Engineering Technology Company
Monitoring O&M data from 11,443 turbines worldwide: approaches, challenges and lessons learned - Samuel Davoust, Senior Engineer, Wind & Performance, GE Renewable Energy
From real wind to actual power output - Jesus Navarro, AMOS Manager, DNV GL
Root cause analysis or why this is happening to me - Nicolas Quievy, Wind Technology Manager, ENGIE
Production gain assessment due to improved dynamic yaw control settings - Henrik Pedersen, Manager Site and Energy Assessment, Siemens Gamesa
Findings on the model based diagnostics implementation
SESSION 3: Lowering operational costs
Session Chair: Mike Anderson, Former Chief Technical Officer, RES Ltd.
Session Description
The under- or over-performance of a wind farm can be attributed to many causes: wind speed, turbine performance, accessibility or a poor estimate of the operational loss adjustment factors. This session will review a number of methodologies using the review of historical operational data to improve the accuracy of the future energy yield of a project. For offshore wind farms, accessibility is seen as a significant potential source of energy loss and, therefore, we will discuss strategies which attempt to mitigate this loss.
Learning objectives
- Innovative techniques for understanding operational data.
- An understanding of the German FGW TR10.
- Novel vessel and access technologies for offshore wind farms.
Speakers
- Martin Strack, Manager Site and Energy Assessment, Deutsche WindGuard Consulting GmbH
Guideline on Operational Data Assessment - Henrik Sundgård Pedersen, Senior Wind Energy Consultant, EMD International A/S
Towards Revenue Assessment - Simon Courret, Wind and Marine Energy Specialist , ENGIE
Wind Energy Yield Assessment, A look-back analysis on ENGIE plants - Fernando Sevilla Montoya, Senior Engineer, DNV GL
Improving offshore wind farm performance through novel access strategies
SESSION 4: Back to the future: from post-construction yield analysis to life extension
Session Chair: Hans Ejsing Jørgensen, Head of section Meteorology & Remote Sensing Program manager – Siting & Integration, DTU
Session Description
This session is divided into two parts. Firstly, we will look back on the performance of wind farms using different data-sets, real-time computing and data analysis to provide surveillance and control for how wind farms are performing.
Secondly, we will look into the future, using tools that are investigating the wind farm load patterns with the aim of increasing the lifetime of wind farms, estimating the remaining life time of a wind farm, and redesigning O&M strategies for wind farm lifetime extension.
Speakers
Part 1 – Post-construction yield analysis and reanalysis
- Nathan Hill, Technical Lead, Lloyd’s Register
Reconciling the past with the present: the impact of developments in reanalysis data on pre- and post-construction yield analysis - Peter Enevoldsen, Product Owner, Envision Energy
Leveraging Envision Energys EnOS IoT platform towards automated post-construction yield analysis for benchmarking and improving the accuracy of the Greenwich Systems yield predictions
Part 2: from performance analysis to life extension decisions
- Bart Dujczynski, Principal, verdantf; Managing Director, Proventus Renewables Ltd., From performance analysis to life extension decisions. Should we extend? How are life extension decisions taken?
- Francesco Vanni, Senior Engineer, DNV GL
An online digital twin for real time calculation of remaining life - Philipp Stukenbrok, Head of Sales & Marketing, 8.2 Consulting AG
Lifetime extension reports and the redesign of O& M concepts for aging turbines - Nikolay Dimitrov, Senior Researcher, DTU
Surrogate Modelling of load and power output variation in wind farms
SESSION 5: Innovations in operations & hybrid systems
Session Chair: Christian Jourdain, Head of Marketing, Services, Siemens Gamesa
Session Description
Participants will learn more about some of the innovations tested in operations. Illustrated with examples, you will understand how O&M diagnostics can be more and more automatized thanks to machine learning and fine-tuned SCADA analysis, which will maximize O&M efficiency. In addition, you will learn more about various onshore and offshore wind-storage systems and how this impacts project’s ROI and LCOE.
Learning objectives
- Understand how machine learning can lead to higher decision automating
- How much you can get from the data you already have
- Learn more about storage
Speakers
- Asun Padros Razquin, Innovation Project Manager, Acciona Energia Practical examples of storage solutions in renewable energies: the Acciona experience
- Peter Enevoldsen, Product Owner, Envision Energy
Examining the business incentives for investments in coupled wind – storage systems - Jack Double, Operating Wind Farm Analyst, K2 Management
SCADA analysis might be the best return on investment you ever get - Elizabeth Traiger, Senior Researcher, Group Technology and Research, Renewables, DNV GL
Machine Learning for automated detection of wind farm underperformance
