Predict gearbox failures, quantify wake losses, and recover yield — on your turbine SCADA. Or get a one-off Wind Performance Audit.
Normal Behavior Modelling on your existing SCADA — temperature, power, wind speed. We flag developing drivetrain faults 3–12 months out, quantify wake losses on the farm map, and price recovery turbine by turbine.
Physics-informed wind turbine analytics.
NuraVolt's wind analytics platform brings the same physics-informed ML approach proven on PV to wind energy. Analysing your existing SCADA, we detect gearbox and bearing failures 3–12 months before they occur — saving €200K–500K per avoided failure. Power-curve analysis quantifies underperformance with IEC-compliant methods, and wake modelling optimises farm-level output.
Built on Normal Behavior Modelling: the platform learns what "healthy" operation looks like for each turbine, then flags deviations that indicate developing faults. No additional sensors required — temperature, power, and wind-speed data you already collect.
Critical issues we solve
Unplanned Gearbox Failures: €200K–500K repair costs plus weeks of downtime that early detection prevents.
Hidden Underperformance: Power-curve degradation from blade erosion, yaw misalignment, or pitch errors going undetected.
Unquantified Wake Losses: 10–20% of potential output lost to wake interactions without visibility.
Reactive Maintenance: Fixing failures after they happen instead of preventing them proactively.
Bearing Failures: Generator and main-bearing failures that escalate to major component damage.
Curtailment Confusion: Difficulty distinguishing grid curtailment from actual performance issues.
Farm-Level Blind Spots: Per-turbine monitoring missing aggregate patterns and optimisation opportunities.
OEM Data Lock-In: Limited access to analytics beyond what turbine manufacturers provide.
Advanced wind monitoring capabilities
Comprehensive analytics for onshore and offshore wind farms, built on physics-informed ML.
Power Curve Analysis
Compare actual power output against OEM curves with air density correction. IEC 61400-12-1 compliant bin method analysis quantifies underperformance and estimates AEP losses.
Gearbox Predictive Maintenance
Normal Behavior Modeling (NBM) trains on healthy SCADA data to predict expected temperatures. Large residuals between predicted and actual indicate developing faults 3-12 months in advance.
Wake Effect Modeling
Jensen/Park analytical model with wake superposition. Calculate farm-level power accounting for upstream turbine wakes. Direction sweep analysis reveals optimal operating conditions.
Anomaly Detection
Isolation Forest unsupervised detection identifies operating points that deviate from expected power curve behavior. Pinpoints curtailment, icing, yaw misalignment, and degradation.
Drivetrain Health Monitoring
Multi-component monitoring: gearbox bearing temperature, oil temperature, generator bearings (drive-end and non-drive-end). Trend analysis estimates remaining useful life.
SCADA Integration
Connects to existing turbine SCADA systems. Uses standard signals: wind speed, power, rotor speed, pitch angle, nacelle temperature. No additional sensors required.
Multi-Channel Alerts & Automated Reports
Configurable notifications via SMS, Email, Teams, Google Chat, and WhatsApp. Scheduled KPI reports with turbine health summaries. Emergency curtailment commands on critical events.
Supported wind configurations
From single turbines to utility-scale farms, onshore and offshore.
Onshore Wind
Utility-scale and community wind farms
Offshore Wind
Higher capacity factors, wake optimization
Hybrid Wind+Storage
Integrated dispatch optimization
Multi-Turbine Farms
Farm-level analytics and optimization
AI/ML features that add value
Physics-informed models catch the issues that standard SCADA alarms miss.
Power Curve Efficiency
Calculate efficiency (actual/expected) for every 10-minute interval. Bin method creates reference curve from clean data. Detect underperformance with statistical confidence.
Normal Behavior Modeling
LightGBM models trained on healthy operation predict expected temperatures. Z-score monitoring detects anomalies. Multi-component tracking for gearbox and generator bearings.
Wake-Aware Forecasting
Jensen wake model with linear expansion. Sum-of-squares wake superposition for multiple upwind turbines. Direction-dependent wake loss estimation for farm power forecasting.
RUL Estimation
Trend analysis on temperature residuals estimates remaining useful life. Linear regression on daily residual means projects days to failure threshold.
Built for the growing wind market
Global wind capacity is growing rapidly, with offshore wind especially expanding in Europe, Asia, and emerging markets. As turbines get larger and more complex, predictive maintenance becomes essential to avoid catastrophic failures.
Our SCADA-based approach requires no additional hardware investment. By learning normal behaviour patterns per turbine, we detect the subtle temperature anomalies that precede gearbox and bearing failures — months of lead time for maintenance planning.
Optimize your wind operations.
Start with a pilot on a few turbines. We integrate with your SCADA, prove the value with your actual data, and provide ROI projections for farm-wide deployment.