Abstract
The structural condition of the cable-stayed towers anchorage on power transmission lines requires constant monitoring. Maintenance routines must be able to identify faulty anchor rods and substitute them to avoid tower collapses and power delivery interruptions. Modern statistical diagnostic systems based on machine learning requires the generation of several distinct rod sample signals to be trained, which is a time-consuming process in typical electromagnetic solvers. Aiming to generate samples in a feasible time span, this paper presents a modeling strategy based on the cascade analysis of the transmission line composed by the anchor and the reference rods, interconnected by a dedicated high-frequency connector. Each distinct transmission line part is modeled by a quadrupole as a partial ABCD matrix. The complete response for each experimental setup is obtained from the cascaded-multiplying of the partial matrices. The proposed modeling proved to be accurate and provides a faster way to obtain the S-parameters from distinct faulty rods if compared to the traditional methodologies using electromagnetic tools.
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