Wind Analytics
AI-based solution, co-created by 3E and Livliner, for automated production loss identification and diagnosis, providing forward-looking insights and actionable recommendations to boost wind asset performance.
WHAT WE DO
Wind Analytics - LivLiner inside
Advanced analytics solutions examine your operational data (preferably high-frequency up to 1”) as well as with wind resource and forecast.
Using state-of-art smart algorithms enhanced by machine learning algorithms, 'Wind Analytics' provides insights on wind assets leading to operational or strategic decisions for performance improvement as well as cost saving.
HOW CAN WE HELP YOU
Digital Performance Monitoring and Diagnostic
Continuous improvement on complete lifecycle of the wind portfolio
Resource
✓ Resource statistics: Wind speed, wind direction, turbulence intensity…
✓ Sensor check: Anomaly detection on wind vanes or anemometers
✓ Virtual met-mast (WRF & AI – based / learned farm model)
Availability
✓ Smart event detection engine (machine learning)
✓ Smart outage annotation (machine learning)
✓ Fully independent (customisable) contractual availability
✓ RDS PP ontology based technical fleet availability
✓ Stop analyser: losses breakdown
Performance
✓ Farm-wide wake modelling using physics based models
✓ Gaussian learning of farm wake
✓ IEC based power curves
✓ Performance monitoring using deep-learned power curves
✓ Deviation (degradation) detection from normal behavior
Machine Health
✓ Subcomponent anomaly detection: failure prediction
✓ Fleet-based anomaly reasoning
✓ Maintenance actions classifications
✓ Reasoning engine to link anomalies to failure modes
✓ Pitch strategy learning
Load history
✓ Lost production quantification (deep-learning)
✓ Load history characterisation from events
✓ Subcomponent load history characterisation
Long Term Yield Assesment
✓ More accurate LTYA reassessment using operational data
✓ Contract KPI decision support
Production Forecasting
✓ Model output statistics (MOS) combined with WRF models for improved forecast
✓ Meteorological ensemble forecasting
✓ Maintenance (outage) planning / optimisation based on forecast models
Deration
✓ Deration (curtailment) detection using status logs or time series data
✓ Statistics on lost production due derating as well as contractual calculations