期刊
SENSORS
卷 21, 期 18, 页码 -出版社
MDPI
DOI: 10.3390/s21186173
关键词
guyed transmission lines; tower collapse; Internet of Things; remote monitoring
资金
- Transmissora Alianca de Energia Eletrica SA (TAESA) [PD-07130-0047]
- ANEEL RD Program
- CNPq [313239/2017-7]
The proposed framework presents a novel approach for remote monitoring of mechanical stresses in guyed towers, utilizing mesh network, neural networks, and sensor fusion techniques to reduce the risk of collapse and estimate tower displacement.
The collapse of overhead power line guyed towers is one of the leading causes of power grid failures, subjecting electricity companies to pay considerable, high-value fines. In this way, the current work proposes a novel and complete framework for the remote monitoring of mechanical stresses in guyed towers. The framework method comprises a mesh network for data forwarding and neural networks to improve the performance of Low-Power and Lossy Networks. The method also considers the use of multiple sensors in the sensor fusion technique. As a result, the risk of collapse of guyed cable towers reduces, due to the remote monitoring and preventive actions promoted by the framework. Furthermore, the proposed method uses multiple input variable fusions, such as accelerometers and tension sensors, to estimate the tower's displacement. These estimations help address the structural health of the tower against failures (i.e., loosening of the stay cables, displacement, and vibrations) that can cause catastrophic events, such as tower collapse or even cable rupture.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据