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Real-Time Anomaly Detection for Smarter Wind Farm Operations.

Energy
TagsProduction FloorSustainabilityEnergyAI ModelsMLOpsOpsStructured Data
Energy
TagsProduction FloorSustainabilityEnergyAI ModelsMLOpsOpsStructured Data

A leading European renewable energy producer partnered with ML6 to transform how its wind turbine fleet is monitored. We developed a scalable AI-powered anomaly detection platform that continuously analyzes operational data across hundreds of turbines, enabling earlier detection of component degradation, more efficient maintenance planning, and greater fleet reliability through production-ready MLOps.

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wind farm - CASES

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ML6 developed an AI-powered anomaly detection platform that continuously monitors hundreds of wind turbines, identifying early signs of component degradation before failures occur. By combining scalable MLOps with explainable machine learning, the solution enables predictive maintenance, reduces manual monitoring, and helps maximize fleet reliability.

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About this client

The client is a large European energy producer operating an extensive fleet of renewable assets, including several hundred wind turbines distributed across multiple sites. These assets are monitored through high-frequency SCADA data and play a critical role in the client’s renewable energy portfolio.

 

Asset managers are responsible for maintaining availability and reliability across the fleet, while minimizing maintenance costs and unplanned downtime. As the fleet scales, manual monitoring of component health becomes increasingly impractical.



Impact

  • Fleet-wide monitoring at scale

    Enables asset managers to monitor component health consistently across the entire wind fleet, without being overwhelmed by data volume or false alarms.

  • Automatic anomaly detection

    Automatically highlights turbines that deviate from expected behavior, so teams can focus their attention where it matters most.

  • Reduced manual analysis effort

    Cuts down the time and effort spent manually reviewing data across the fleet.

  • Smarter maintenance prioritization

    Improves prioritization of maintenance actions based on where deviations actually occur.

  • Lower risk of unplanned outages

    Reduces the risk of costly unplanned outages caused by undetected component degradation.

Challenge

Building a scalable and trustworthy anomaly detection capability for wind turbines involved several key challenges.

Turbines operate under different environmental conditions and exhibit varying "normal" behavior. While earlier approaches relied on one model per turbine, scaling this manually quickly became unmanageable as the fleet grew. However, maintaining per-turbine models remained essential for model explainability and trust, as asset managers have limited machine learning experience and found generalized fleet-wide models difficult to interpret.

Solution

We built a two-layer solution that separates defining "normal" from detecting "anomalous."

First, we invested in developing more reliable source data for the measurements. This allowed us to expand our analytical coverage significantly, scaling from evaluating around 120 turbines to nearly 350 turbines. With this expanded foundation, we generated reliable baseline predictions trained exclusively on "healthy" data.

We developed an MLOps architecture that makes managing hundreds of per-turbine models scalable and highly performant. By adopting and actively contributing to the client's MLOps platform, we established a system that handles:

  • Model Management

    Automated storage, registry, and versioning.

  • Compliance & Tracking

    Rigorous artifact logging to ensure upcoming AI-Act compliance (e.g., Article 12 record-keeping requirements).

  • Orchestrated Experimentation

    Enabling fast, incremental development cycles through automated tracking.

The training pipelines (including hyperparameter tuning) and inference pipelines were explicitly designed for scalability on the client's data platform. Anomaly detection results are generated daily in batch and published as governed data products. Furthermore, the setup is inherently asset-agnostic, allowing for the easy onboarding of fleet-wide models in the future if asset managers of different asset types desire it.

Second, on top of these baselines, a physically gated, statistically robust anomaly detection algorithm identifies slow degradation and sudden failures, surfacing them as clear deviation events for downstream use.

Results

Backtests proved that the anomaly detection layer reliably surfaced early signs of component degradation while keeping false positives low, a critical factor for adoption by asset managers. Ultimately, this led to a highly reliable 91% accuracy in the anomaly detection layer.

  • Enhanced Explainability

    Asset managers found the per-turbine models much easier to interpret, directly increasing their trust and adoption of the system.

  • Scalability Realized

    The MLOps implementation successfully mitigated the management overhead of running hundreds of distinct models, turning a previously unmanageable landscape into a streamlined, automated process.

  • Actionable Insights

    Beyond technical performance, the solution fundamentally changed how the wind fleet is monitored. Instead of manually scanning healthy assets, teams now receive concise, actionable signals that point directly to emerging issues.

The solution is now fully deployed and running in production. To ensure seamless adoption, the results and anomaly alerts are automatically transferred to the asset managers' platform of choice for daily asset management. This enables earlier interventions, more efficient maintenance planning, and a concrete step toward scalable predictive maintenance across renewable assets

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Sam Clauwaert 6
Sam Clauwaert Industry Director Energy & Utilities

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Sam Clauwaert 6
Sam Clauwaert Industry Director Energy & Utilities