Posters

Abstract

While AI-driven anomaly detection in wind turbines is now commonplace, operators still face a critical challenge: quantifying the future operational and economic impact of an unfolding issue. The inability to accurately forecast potential production losses and resource requirements hinders effective maintenance prioritization and turns minor faults into significant financial liabilities. This critical information gap often leads to reactive decision-making, where the true cost of an issue is only understood after the losses have been incurred. This work introduces a novel framework that leverages a Large Language Model (LLM) to bridge this gap by delivering predictive impact analysis. By synthesizing data from ongoing alarms, performance degradation patterns, user-uploaded files, and operational context, our system identifies and ranks historically similar maintenance cases from a comprehensive library. The LLM then analyzes these precedent cases to compute a range of probable future outcomes. The system delivers immediate, data-driven forecasts on expected MWh loss, distinguishing between the outcomes of early versus delayed intervention. Furthermore, it provides detailed projections on required hardware, repair work, and associated costs. This empowers operators to move beyond simple anomaly alerts and instead make proactive, financially-informed decisions, transforming maintenance from a reactive necessity into a strategic, value-driven operation.