AI Offshore Wind Operations Optimization

Improves offshore wind farm performance by optimizing curtailment, wake steering strategies, and operational setpoints using AI.

The Problem

“Reduce offshore wind downtime and O&M costs”

Organizations face these key challenges:

Unplanned failures and long mean time to repair due to weather-limited access, scarce vessels, and offshore safety constraints

Fragmented data across SCADA, condition monitoring, CMMS, metocean, and OEM systems, limiting timely and consistent decision-making

Inefficient logistics and scheduling (vessel selection, technician routing, parts staging) causing avoidable downtime and high day-rate spend

Impact When Solved

Increase farm availability by 1–3 percentage points through earlier fault detection and risk-based maintenanceReduce O&M costs by 5–15% via optimized vessel/crew dispatch, fewer repeat visits, and better spares planningLower downtime from major component events by 10–25% and cut emergency logistics/expediting costs by 10–20%

The Shift

Real-World Use Cases

AI Condition Monitoring for Wind Turbines

This AI framework monitors wind turbines to detect any problems early, helping to prevent energy losses.

anomaly detectionemerging

8.5

AI-Driven Predictive Maintenance for Wind Turbines

This AI system monitors wind turbines continuously to predict when they might fail, allowing for repairs before breakdowns happen.

anomaly detectiongrowing

8.0

Predictive Maintenance for Offshore Wind Turbines

This AI predicts when parts of wind turbines might fail, allowing for timely maintenance.