AI serving wind farms: from smart control to sustainable performance

In 2023, wind power covered nearly 18% of the European Union’s electricity production, establishing itself as a significant driver of the energy transition (open in new tab).

Yet, one fact remains: wind turbine maintenance is currently one of the most significant cost items in a wind farm’s lifecycle—high maintenance costs, unexpected breakdowns, and difficulties accessing turbines lead to substantial production losses (especially for offshore farms). In Europe, operating and maintenance costs represent around 20% to 25% of total lifetime costs for a wind power plant. Globally, one analysis estimates that onshore wind generated nearly $15 billion in operating and maintenance costs in 2019, including about $8.5 billion linked to unplanned repairs (open in new tab).

Faced with this reality, how can AI optimize wind farm maintenance and control? Why is it emerging today as a key solution to reduce costs, increase production, and ensure a prosperous future for wind energy?

From reactive to planned maintenance: AI reshapes wind farm economics

Continuous monitoring and failure prediction are essential levers to contain costs and limit production interruptions. This is precisely where artificial intelligence adds value: leveraging vast amounts of available data to transform how wind farms are operated.

A modern wind farm generates a massive and varied volume of data: meteorological (wind speed and direction, air temperature), machine-related (component temperature, vibrations, current intensity, nacelle status, blade pitch and orientation), as well as high-resolution images from drones or ground cameras, sometimes supplemented by radars or wildlife detection systems. SCADA systems typically record dozens of variables every ten minutes for each turbine, representing millions of data points annually across an entire farm.

Thanks to these data, AI enables real optimization levers:

• Improve farm availability: by detecting failures earlier and planning interventions, AI reduces unexpected downtime and increases actual production time.
• Boost electricity output: by optimizing turbine settings (orientation, blade pitch) and limiting wake effects, AI extracts more energy from wind without additional infrastructure.
• Reduce maintenance costs: autonomous drones and computer vision can cut inspection costs by up to 70%. Teams intervene at the right time, on the right components, reducing unnecessary visits and emergency repairs.
• Better manage environmental impacts: integrating sensors (radars, cameras, AI) to limit wildlife collisions makes wind turbine operation more sustainable and acceptable.

AI-driven optimization is not just a technological “plus” but a structured response to strong economic and operational constraints already faced by operators.

Optimized maintenance and automated inspection: AI sets the new standard

Artificial intelligence processes signals generated by the farm (temperature, vibrations, current intensity, etc.) to predict future failures using unsupervised classification and anomaly detection algorithms capable of spotting patterns that indicate potential breakdowns (open in new tab) —such as abnormal vibration variations, gradual generator overheating, or characteristic sounds of a damaged blade.

Prediction accuracy depends on the quantity and quality of historical training data. Unlike traditional monitoring software, AI models based on machine learning are not static—they learn and adapt over time to new variables and issues encountered by the farm.

Alongside sensor and microphone data, autonomous drones equipped with high-resolution cameras inspect blades and nacelles, producing thousands of images. (open in new tab) These images are analyzed by computer vision models that automatically detect cracks, erosion, corrosion, or dirt deposits. The AI analysis results are then integrated into a cloud-based Asset Performance Management (APM) platform, which converts detections into clear, prioritized alerts.

Instead of manually reviewing hundreds of photos, technicians receive a targeted list of anomalies with precise locations and severity levels. From there, humans take over: deciding on immediate intervention for critical defects, scheduling follow-up for minor damage, or correcting false positives to improve AI predictions. AI filters and accelerates work, helps prioritize, but operational decisions remain the technician’s responsibility, preserving their central role in maintenance.

Compared to traditional inspections—often lengthy, costly, and sometimes dangerous, requiring system shutdown—AI significantly reduces turbine downtime and directs technicians only to areas needing intervention. Predictive maintenance thus drastically reduces unplanned outages.
Benefits include longer component lifespan (replaced only when necessary), lower maintenance costs, and improved overall wind farm availability.

Several industrial players already offer operational services. SkySpecs (open in a new tab), Aerones (open in a new tab), and Flyability (open in a new tab) automate drone inspections and provide image analysis pipelines powered by AI models to locate defects and prioritize interventions. Industry reports estimate that computer vision inspections take about 30 minutes versus nearly two hours for traditional inspections (open in a new tab), which also require mandatory turbine shutdown for safety reasons.

AI as a lever against wake effect challenges

In large wind farms with dozens or hundreds of turbines, wake effects can significantly reduce total electricity production and accelerate equipment wear. Optimizing turbine layout is therefore a top priority.
AI intervenes at two levels:

Design phase: simulating different layout scenarios to identify the optimal spatial configuration for energy production. Optimization algorithms test thousands of possible arrangements considering dominant wind speeds and directions, seasonal variability, environmental features (hills, forests, nearby buildings and villages), physical constraints (minimum safety distances between turbines), and economic considerations (installation costs). These approaches improve overall farm yield even before installation.

Operational phase: AI coordinates blade orientation and pitch across multiple turbines. For example, a front-row turbine may deliberately misalign slightly from the optimal flow to improve energy productivity for turbines behind it. This collective control reduces wake-related losses and increases total farm output, especially offshore where turbine rows are long and regularly exposed to this phenomenon. Recent studies and experiments show that AI-assisted control strategies can deliver significant energy gains. WindESCo (open in a new tab)’s Swarm software, deployed at Milford I & II wind farms in Utah (USA), with 165 turbines totaling about 300 MW, is one of the first large-scale examples of this technology applied to an operational farm. Results show an annual electricity production increase of +2.7%.

Generative AI as a daily ally for maintenance technicians

The arrival of generative AI models further optimizes Asset Performance Management. Beyond “technical alerts,” platforms now offer technicians a conversational interface. They can interact in natural language with an AI agent that explains diagnostics case by case and suggests tailored actions based on the diagnosis and site context.

The AI agent guides the technician, but the latter remains in control: they can adjust or correct recommendations based on experience and site knowledge. This two-way interaction adapts interventions to real-world conditions and enhances action relevance. The AI agent’s feedback loop is continuously enriched by technician interactions, improving alert accuracy, diagnostic relevance, and guidance effectiveness over time.

For example, in the case of a generator anomaly, the guided intervention would proceed as shown in the following diagram.