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.

 

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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.   

     

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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

Interested?