Abstract
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.
Highlights
AngrisaniThe 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 [1,2].In addition, city blackouts resulting from the collapse of cable-stayed structures cause a financial loss in the order of millions of dollars for companies and industries
At the current moment of the investigation, it is a problem to face the shortage of data for training deep learning models
It means that the framework is ready to provide data, but it takes time until there are enough data for training the neural network
Summary
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 [1,2]. City blackouts resulting from the collapse of cable-stayed structures cause a financial loss in the order of millions of dollars for companies and industries. Current traditional solutions, such as ground inspection by foot, seem ineffective since in-site periodic inspections are sometimes not enough to detect hidden defects in such structures [3]. The constant online sampling of data in the cable traction allows real-time monitoring of the guyed tower health, which helps to prompt personnel to act immediately. The proposed framework allows addressing risks of collapse in guyed tower structures.
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