Wind Energy Analytics & Predictive Maintenance
AI-powered turbine performance monitoring. Predict gearbox failures months in advance, optimize wake interactions, and maximize energy yield with SCADA-based analytics.
Physics-Informed Wind Turbine Analytics
NuraVolt's Wind Analytics platform brings the same physics-informed ML approach proven in our PV monitoring to wind energy. By analyzing existing SCADA data, we detect gearbox and bearing failures 3-12 months before they occur—saving $200K-500K per avoided failure. Our power curve analysis quantifies underperformance with IEC-compliant methods, while wake modeling optimizes farm-level output.
Built on Normal Behavior Modeling (NBM), our platform learns what "healthy" operation looks like for each turbine, then flags deviations that indicate developing faults. No additional sensors required—we work with the temperature, power, and wind speed data you already collect. Transfer learning from our PV experience accelerates deployment and improves accuracy.
Critical Issues We Solve
Unplanned Gearbox Failures: $200K-500K repair costs plus weeks of downtime that could be prevented with early detection
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 or optimization
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 optimization opportunities
OEM Data Lock-In: Limited access to analytics beyond what turbine manufacturers provide
Advanced Wind Monitoring Capabilities
Comprehensive wind analytics platform for onshore and offshore wind farms, built on proven physics-informed ML approaches.
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.
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 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 behavior patterns for each turbine, we detect the subtle temperature anomalies that precede gearbox and bearing failures—providing months of lead time for maintenance planning.
Optimize Your Wind Operations
Start with a pilot on a few turbines. We'll prove the value with your actual SCADA data and provide ROI projections for farm-wide deployment.