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.

中文翻译：

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n 基于物联网的系统，用于监测架空电力线中拉线塔的健康状况

架空输电线拉杆塔倒塌是导致电网故障的主要原因之一，电力公司需要支付巨额的高额罚款。通过这种方式，当前的工作提出了一种新颖而完整的框架，用于远程监控拉线塔中的机械应力。该框架方法包括用于数据转发的网状网络和用于提高低功耗和有损网络性能的神经网络。该方法还考虑了在传感器融合技术中使用多个传感器。因此，由于框架促进了远程监控和预防措施，拉线电缆塔倒塌的风险降低了。此外，所提出的方法使用多个输入变量融合，例如加速度计和张力传感器，来估计塔的位移。