# Single-Sensor Impact Source Localization Method for Anisotropic Glass Fiber Composite Wind Turbine Blades

## TL;DR

This paper introduces a cost-effective method using a single sensor and deep learning to locate impacts on wind turbine blades, avoiding complex corrections.

## Contribution

The novel approach transforms impact localization into a classification task, eliminating anisotropy compensation and using only one sensor.

## Key findings

- The method achieves 96.9% localization accuracy with a single sensor.
- It reduces costs compared to traditional multi-sensor systems.
- Deep learning leverages material and geometric anisotropy for accurate impact detection.

## Abstract

The wind turbine blade is subject to multi-source impacts, such as bird strikes, lightning strikes, and hail, throughout its extended service. Accurate localization of those impact sources is a key technical link in structural health monitoring of the wind turbine blade. In this paper, a single-sensor impact source localization method is proposed. Capitalizing on deep learning frameworks, this method innovatively transforms the impact source localization problem into a classification task, thereby eliminating the need for anisotropy compensation and correction required by conventional localization algorithms. Furthermore, it leverages the inherent coding effects of the blade’s material and geometric anisotropy on impact sources originating from different positions, enabling localization using only a single sensor. Experimental results show that the method has a high localization accuracy of 96.9% under single-sensor conditions, which significantly reduces the cost compared to the traditional multi-sensor array scheme. This study provides a cost-effective solution for real-time detection of wind turbine blade impact events.