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AI in Wind Energy

School of Energy · Intermediate to Advanced

From Wind Resource to AI-Powered Turbines, Wind Farms & Grid Integration

Modern wind assets produce dense operational telemetry — SCADA tags for power, rotor speed, pitch/yaw, temperatures, vibration and alarms; met-mast and lidar campaigns; NWP and satellite fields; and inspection imagery from drones and ground cameras. The industry challenge is turning that data into reliable forecasts, early failure signals, controllable set-points and bankable O&M decisions under grid-code and market constraints.

This intermediate-to-advanced track goes beyond introductory renewables literacy. You work with hybrid NWP–ML forecasting horizons, power-curve and availability analytics, residual-based anomaly detection for gearbox / generator / bearings, deep-learning inspection pipelines, wake-aware farm control ideas, and coordination of wind with storage and demand flexibility for system services.

You also frame onshore vs offshore differences (access, corrosion, marine metocean, cable and foundation risk), understand why curtailment, ramp events and forecast error matter commercially, and connect AI outputs to work-order prioritisation, spare-part strategy and lifecycle / repowering decisions — alongside AI in Solar and AI in Renewable Energy Systems.

Technical focus areas

Forecasting science

Intra-hour to day-ahead / medium-range; uncertainty and ramp prediction; NWP post-processing.

Asset intelligence

SCADA feature engineering, power-curve modelling, health indices and remaining-useful-life thinking.

Inspection AI

RGB / thermal / UAV imagery; defect taxonomies; detection, segmentation and triage workflows.

Farm & grid

Wake interaction, active power control, forecast-aware dispatch and storage co-optimisation concepts.

What you will learn

  • Map turbine architecture and SCADA signal families to AI use cases (power, loads, pitch/yaw, vibration, temperatures)
  • Build hybrid forecasting workflows: NWP bias-correction, site adaptation, quantile / ensemble uncertainty
  • Model expected power vs wind (power curves, density corrections) and isolate performance shortfalls
  • Design PdM pipelines: residuals, multivariate anomaly scores, alarm rationalisation and work-order ranking
  • Apply Computer Vision for blade / tower defects with confidence thresholds and human-in-the-loop review
  • Interpret wake losses, layout trade-offs and farm-level active power / curtailment strategies
  • Differentiate onshore vs offshore AI constraints (access logistics, corrosion, metocean uncertainty)
  • Connect forecasts to markets / schedules: ramp risk, imbalance exposure and reserve needs
  • Coordinate wind with BESS / demand flexibility for firming and peak-shaving logic
  • Evaluate data quality, labelling, drift and MLOps concerns for production wind analytics
  • Use AI outputs in feasibility, availability guarantees, OPEX modelling and repowering cases
  • Communicate model limits, false-positive cost and operational ownership to engineering stakeholders

Curriculum modules

  • 1. Wind Energy Systems, Data Architecture & AI Problem Framing

    Turbine drivetrain and control overview (rotor, gearbox or direct-drive, generator, converter, pitch/yaw); atmospheric boundary-layer concepts; IEC-style power-curve thinking; SCADA historians vs event logs; labelling failures and defining prediction horizons (minutes to weeks).

    • Signal taxonomy, sampling rates, missingness and sensor drift
    • Supervised vs unsupervised vs hybrid PdM problem types
    • KPIs: availability, capacity factor, specific yield, forecast skill scores
  • 2. Hybrid NWP–ML Wind Resource & Generation Forecasting

    Short-term (intra-hour / hours-ahead), day-ahead and medium-range forecasting. Feature sets from NWP, local sensors and lagged SCADA; post-processing and site calibration; probabilistic forecasts (quantiles, ensembles); ramp and extreme-event awareness; evaluation with MAE, RMSE, CRPS and pinball loss at a practitioner level.

    • Spatio-temporal models and multi-turbine / farm aggregation
    • Uncertainty for trading, scheduling and reserve planning
    • Linking long-term resource assessment to operational forecast systems
  • 3. SCADA Analytics, Power-Curve Modelling & Performance Optimization

    Expected-vs-actual power analysis; density and turbulence corrections; derating / curtailment detection; icing effects; controller set-point and pitch behaviour analytics; identifying underperformance clusters across a fleet.

    • Feature engineering for multivariate turbine health
    • Benchmarking sister turbines and OEM baselines
    • From insight to control recommendation (active power, yaw sector management concepts)
  • 4. Predictive Maintenance: Drivetrain, Generator, Bearings & Electrical Systems

    Condition monitoring stack: vibration, oil debris / temperature, electrical signatures and SCADA residuals. Anomaly detection (autoencoders, isolation methods, statistical control charts), early-warning lead time vs false alarms, failure-mode libraries for gearbox, main bearing, generator and converter, and integration with CMMS / work orders.

    • Remaining-useful-life (RUL) concepts and survival-style framing
    • Alarm flood reduction and root-cause assist workflows
    • Offshore access cost and decision thresholds for intervention
  • 5. Computer Vision & Intelligent Structural / Blade Inspection

    UAV mission planning constraints; RGB and thermal modalities; defect taxonomies (erosion, cracks, lightning, leading-edge damage, corrosion); detection / segmentation models; severity scoring; dataset bias and labelling quality; human review gates for safety-critical findings.

    • Tower, foundation and transition-piece visual monitoring concepts
    • Change detection across inspection campaigns
    • Closing the loop from CV finding → work package → verification
  • 6. Farm Control, Wake Interaction, Storage & Grid Integration

    Wake physics at an operational level; layout and sector management; farm-level active power control concepts; forecast-informed curtailment; co-optimisation with BESS for firming and peak management; grid-code services awareness; digital-twin and scenario simulation mindset for what-if analysis.

    • Hybrid wind–solar–storage portfolios
    • Ramp management and congestion / curtailment economics
    • Data governance, cybersecurity awareness and production MLOps for energy AI

Skills you build

  • SCADA / historian analytics and energy feature engineering
  • Hybrid NWP–ML forecasting and probabilistic evaluation literacy
  • Power-curve / performance shortfall diagnosis
  • PdM pipeline design: anomalies, health indices, alert triage
  • CV inspection workflow design for blades and structures
  • Wake-aware farm and curtailment decision framing
  • Wind–storage co-optimisation and flexibility concepts
  • MLOps, data quality and model-risk communication for O&M

Who this is for

  • Wind / mechanical / electrical engineers moving into analytics or digital O&M
  • SCADA, CMS and performance engineers seeking AI methods
  • Data scientists / ML engineers entering wind asset intelligence
  • Project and asset managers needing forecast and PdM decision literacy
  • Professionals targeting offshore / hybrid renewable portfolios

Methods & tooling awareness

Conceptual exposure to time-series ML, gradient boosting / deep sequence models, anomaly detection, CV detection/segmentation, WindPRO / WAsP-style resource context, SCADA historians and dashboarding — framed for engineering judgement, not tool certification alone.

Key AI use cases (intermediate–advanced)

Probabilistic Wind & Power Forecasting

Combine NWP fields with site SCADA for multi-horizon forecasts, ramp alerts and uncertainty bands used in scheduling and imbalance risk reduction.

Fleet Performance Benchmarking

Detect underperforming turbines via residual power analysis, controller anomalies and sister-turbine comparisons across wind regimes.

Drivetrain & Bearing PdM

Fuse vibration, temperature and SCADA residuals to raise early warnings with calibrated lead time for planned interventions.

UAV Blade Inspection Automation

Detect and score leading-edge erosion, cracks and lightning damage; prioritise repair packages and verify remediation.

Wake-Aware Farm Control

Use sector management and active power strategies informed by wake and forecast state to lift farm energy and manage loads where applicable.

Wind + BESS Co-Optimisation

Align charge / discharge with forecast error, peak periods and grid constraints to firm variable wind output.

Curtailment & Congestion Analytics

Separate weather-driven shortfalls from grid or market curtailment and quantify commercial impact for asset owners.

Industry perspective

By the end of this track you should reason about a wind asset as a cyber-physical system: aerodynamics and machines produce power; sensors and SCADA produce data; models produce forecasts and health signals; operators and markets convert those signals into set-points, work orders and commercial outcomes.

The learning goal is competence at the interface of wind engineering + ML methods + operational decision systems — ready to contribute to digital O&M, performance analytics and AI-enabled farm / grid integration programmes.

Enquire about AI in Wind Energy

Explore other School of Energy tracks

AI in Renewable Energy Systems
AI in Solar Energy
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Enquire with RIA for batch schedules, mentoring pathways and project support.

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