A Synthetic Reliability-Aware PINN Benchmark for Offshore Wind Turbine Support-Structure Monitoring with Bayesian Inverse Identification

A new benchmark called Digi Turbine has been introduced for monitoring the structural health of offshore wind turbine support structures using a synthetic reliability-aware Physics Informed Neural Network (PINN). This framework integrates a simplified Euler Bernoulli beam equation with Bayesian inverse identification and First Order Reliability Method (FORM) screening, aimed at improving state estimation from sparse measurements.

The development of Digi Turbine is significant as it addresses the challenges of reliable structural health monitoring in offshore wind energy, which is crucial for optimizing maintenance and ensuring the longevity of wind turbine support structures. By leveraging advanced computational techniques, this benchmark enhances the accuracy and efficiency of monitoring processes.

This initiative reflects a broader trend in renewable energy towards integrating artificial intelligence and data-driven methods to improve forecasting and monitoring capabilities. Similar advancements in renewable energy forecasting and structural dynamics monitoring highlight the ongoing efforts to enhance the reliability and performance of renewable energy systems, particularly in the face of environmental variability and operational challenges.