Do you recognize one or more of the following topics when managing your wind turbine plants?

Continuous business plan monitoring

Unveil cause of lost revenue in your portfolio

Harvest the power of 1-sec data

Improved assessment of turbine dynamic response enables AI learning in greater detail

Losses

Account for different losses (electrical losses,...)

True resource at the site

Merge operational data (SCADA, Lidar, Satellite-based wind measurements) with simulation for true insights in expected free wind speed

Asset underperformance

Detect underperforming assets by building on IEC power curves and learning methods

Wake effects

Wake effect calculated by means of advanced physical simulation merged with wake distributions calculated from SCADA data

Curtailment insights

Calculate true loss due to different curtailment events

Availability

Calculate availability using IEC or any other generic classification approach in addition to contractual

Maintenance

Improved assessment of turbine dynamic response enables AI learning in greater detail

Digital performance monitoring and diagnostics can bring direct and indirect value to your wind portfolio

Increased revenue

Increased availability

  • Lost production calculation
  • Classification of curtailments and stops

Modules

Monitoring
Availability

Improved performance

  • Component insights
  • Asset benchmarking
  • Maintenance planning
  • Automated allocation support
  • RDSPP logic for component allocation of events

Modules

Performance

Objectivised compensation

  • Detect underperforming WTs
  • Farm-wide benchmarking
  • Numeric wake & modelling

Modules

Deration

Extended lifetime

  • Curtailment decision support
  • Performance assessment
  • Fast detection of failing components
  • Insights into differences between true resource and expected from resource assessment
  • Classified event history 

Modules

Wind resource
Load history

Production optimisation

  • Optimized production planning based on forecasting

Modules

Wind forecast

Cost savings

Operational efficiency

  • Report customisation
  • Seamless data handling

Modules

Availability
Monitoring

Maintenance optimization

  • Maintenance scheduling
  • Preventive maintenance

Modules

Performance

Penalty avoidance

  • Have detailed knowledge of expected production through dedicated forecast
  • Avoiding penalties by tracking and optimizing curtailment strategies

Modules

Deration

Liquidated damages management

  • Availability calculations
  • Maintenance efficiency assessment

Modules

Wind resource
Load history

Our functional modules deliver continuous improvement on the complete lifecycle of your wind portfolio

Resource

  • Resource statistics
  • Sensor check (actionable)
  • Virtual metmast (WRF & AI-based)
  • Virtual metmast (learned farm model)

Availability

  • Smart event detection engine (machine learning)
  • Smart outage annotation (machine learning)
  • Fully independent contractual availability
  • RDS PP ontology based technical fleet availability

Machine

  • Subcomponent anomaly detection
  • Fleet-based anomaly reasoning
  • Maintenance actions classifications
  • Easy to interprete alarms
  • Resource statistics
  • Sensor check (actionable)
  • Virtual metmast (WRF & AI-based)
  • Virtual metmast (learned farm model)

LTYA

  • Operational LTYA reassessment
  • Contract KPI decision support

Performance

  • Farm-wide wake modelling using physics-based models
  • Gaussian learning of farm wake
  • IEC-based power curves
  • Performance monitoring using deeplearned power curves

Load History

  • Subcomponent anomaly detection
  • Fleet-based anomaly reasoning
  • Maintenance actions classifications

Production forecasting

  • Maintenance planning
  • Meteorological ensemble
  • Model output
  • Machine learning
The LivLiner Wind Analytics technology has enabled us to automatically identify performance and diagnostics actions from our high-frequency data that enabled continuous O&M improvements.
Bjorn ReynaertO&M Manager - Norther

Livliner & 3E join forces to offer a cutting-edge analytics suite for the wind industry

  • Agile analytics pipeline developer
  • Building on +10 years of academic research focused on the wind turbine machine: performance and condition monitoring
  • Supporting offshore wind farms since 2011
  • Methods trained and improved on fleet of >100 offshore
    turbines of different sizes from 3MW – 9MW
Founded by Jan Helsen,
Prof. at Vrije Universiteit Brussels
windturbine
  • Experienced company with long track-record in platform development
  • Dedicated expertise focused on the interaction between the machine and its environment: resource and performance monitoring
  • Experience with dealing with portfolios of onshore
    and offshore turbines