Sessions | WindEurope Electric City 2021


Reducing operational costs onshore

Onshore wind Technical and Scientific Programme

When: Wednesday, 24 November 2021, 10:45 - 12:15
Where: Auditorium A11

Session description

Operations and maintenance (O&M) costs still represent a significant fraction of the overall levelized cost of energy (LCoE) from wind turbines. Better estimates of loads and remaining useful lifetime (RUL) can facilitate longer component lifetimes and further reduce LCoE. To reduce O&M costs and make more informed choices requires well-placed measurements, smart use of data and improved turbine models.
This session will:
• Explore the use of artificial intelligence to make use of turbine data to predict RUL and make informed O&M decisions.
• Investigate the use of both wind turbine Supervisory Control and Data Acquisition (SCADA) and higher frequency Condition Monitoring System (CMS) data for predictive maintenance.
• Present sensor solutions for monitoring potential faults and predicting possible failure.
• Unpick the hype around ‘digital twins’ and show what they can actually do in practice.

Session chair

Simon Watson

Professor of Wind Energy Systems and Director of DUWIND, TU Delft


Investigation of the Measurability of Selected Damage to Supporting Structures of Wind Turbines

Johannes Rupfle

Research Associate, Technical University of Munich

SCADA analysis for wind turbine aging interpretation

Ludovico Terzi

Technology Manager, ENGIE

Estimation of rotor and main bearing loads using artificial neural networks

Amin Loriemi

Scientific Assistant, Chair for Wind Power Drives

Practical applications of digital twins: case studies from the real world

Edoardo Cicirello

Wind Domain Expert at GreenPowerMonitor, a DNV company, DNV

AI-based condition monitoring and predictive maintenance framework for wind turbines

Janine Maron

Analyst, WinJi AG


Deep Learning-based predictive maintenance for improving wind turbines reliability

Federica Bertoni

Digital Data Scientist, Falck Renewables SpA


Service Optimization of Wind Turbine Drive Trains - a fast track from CMS IoT signals to service solutions

Mihail Ivanov

Product Manager Digitalisation, ZF Wind Power


Eating though yaw motors to deliver fatter performance

Chris Hansford

Senior Engineer, DNV

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