Agentic AI for wind farm operations

Wind turbines fail loudly but underperform quietly. SanchAI monitors every turbine's data continuously and explains the root causes of losses your alarms never catch, so your team spends its time fixing problems.

Why wind O&M needs more than SCADA alarms

A wind farm's SCADA system is built to report status and protect equipment. Finding lost revenue was never its job. Alarms trigger on hard limits such as overspeed or grid faults, but a turbine can run for months at 95% of its potential without tripping anything, and across a fleet those few percent compound into significant lost generation. Meanwhile, when something does go wrong, operators face the opposite problem: alarm floods where one root cause buries itself under hundreds of consequential alarms.

SanchAI's agents were built for both failure modes. They continuously model what each turbine should be producing and investigate deviations the moment they appear. When alarms flood in, they collapse the noise down to causes. Our wind domain expertise comes from 24+ years inside turbine OEMs, and the analysis reflects how experienced wind engineers troubleshoot.

One turbine, thirty days, and the gap explained

0% 50% 100% 0 5 10 15 20 25 Wind speed, m/s Rated Gap: yaw misalignment 2.1% of annual energy, recoverable
  • OEM curve
  • Expected at this site
  • Actual, last 30 days
  • Gap, attributed to yaw
Actual operating points against the curve this turbine should follow at this site's air density. The shortfall between 5 and 12 m/s is attributed to yaw misalignment and sized in annual energy. Illustrative data.

What SanchAI does for wind fleets

Power curve deviation detection

Every turbine's actual output is continuously compared against the OEM power curve and an AI-predicted curve adjusted for air density and site conditions. Drift from blade degradation, pitch issues, or firmware changes is flagged before it becomes months of lost energy.

Alarm flood triage

A single grid event can generate hundreds of SCADA alarms across a wind farm. SanchAI groups them by root cause, separates symptoms from origins, and cross-references the PLC manual to produce a per-turbine digest of the critical errors and their remedies.

Yaw and pitch misalignment

Static yaw misalignment of a few degrees can cost 1–3% of a turbine's annual energy production. SanchAI detects nacelle-to-wind misalignment, stuck blades, and actuator faults from operational data, sizes the generation impact, and ranks turbines by recovery potential, with no lidar campaign required.

Component temperature forecasting

Gearbox, generator, and bearing temperatures across seven drivetrain and electrical components are scored against their OEM thresholds every 12 hours, with breach forecasting so a developing failure is caught while intervention is still cheap and plannable.

Resource versus equipment

Weibull, wind-rose, and wind-index analysis against 20-year ERA5 baselines separates a poor wind month from a turbine problem, and layout-based wake analysis quantifies the energy each downstream turbine loses to its neighbours.

Breakdowns and availability

Turbines in breakdown are ranked by generation at risk against live wind and followed to closure, and every lost megawatt-hour is attributed to grid curtailment, downtime, derating, or performance, so you know what was recoverable and who is accountable.

Frequently asked questions

Can SanchAI work with our turbine OEM's SCADA system?
Yes. SanchAI connects to all major wind SCADA systems through OPC UA, Modbus, and vendor interfaces, and works across mixed-OEM fleets.
Can SanchAI send the team a daily report?
Yes. Describe the report once and an agent runs it on schedule, for example a 6:00 AM turbine underperformance digest with probable causes, or a 10:00 AM list of turbines in breakdown ranked by generation at risk, delivered by email to the people who act on it.
How does SanchAI detect underperformance that SCADA alarms miss?
SCADA alarms fire on fixed thresholds. SanchAI instead models each turbine's expected behavior from its own history and its peers under the same wind conditions, then investigates statistically significant deviations. That is how it catches underperformance no fixed threshold would ever flag.
Do we need to install new sensors on our turbines?
No. SanchAI works with the operational data your turbines already produce. Additional sensors can be integrated if you have them, but they are not required.

Learn how the SanchAI platform works, read what agentic AI means for renewable operations, or explore solutions for solar and BESS.

Put your turbine data to work

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