# Obtaining Rotational Stiffness of Wind Turbine Foundation from Acceleration and Wind Speed SCADA Data

## TL;DR

This paper introduces a cost-effective method to monitor wind turbine foundation health using SCADA data and machine learning.

## Contribution

A novel approach using SCADA data and a convolutional neural network to estimate rotational stiffness without expensive sensors.

## Key findings

- The model estimates stiffness within 7% of ground truth values on average.
- Synthetic data from a generative AI network can detect stiffness drops even if exact values are uncertain.
- The method reduces reliance on costly moment and tilt sensors.

## Abstract

Monitoring the health of wind turbine foundations is essential for ensuring their operational safety. This paper presents a cost-effective approach to obtain rotational stiffness of wind turbine foundations by using only acceleration and wind speed data that are part of SCADA data, thus lowering the use of moment and tilt sensors that are currently being used for obtaining foundation stiffness. First, a convolutional neural network model is applied to map acceleration and wind speed data within a moving window to corresponding moment and tilt values. Rotational stiffness of the foundation is then estimated by fitting a line in the moment-tilt plane. The results obtained indicate that such a mapping model can provide stiffness values that are within 7% of ground truth stiffness values on average. Second, the developed mapping model is re-trained by using synthetic acceleration and wind speed data that are generated by an autoencoder generative AI network. The results obtained indicate that although the exact amount of stiffness drop cannot be determined, the drops themselves can be detected. This mapping model can be used not only to lower the cost associated with obtaining foundation rotational stiffness but also to sound an alarm when a foundation starts deteriorating.