Machine Learning Approach to Detect Faults in Anchor Rods of Power Transmission Lines

Abstract

Detecting faults on the anchor rods of power transmission line towers is an essential procedure for guaranteeing the safety, operability, and availability of the electric power system. Such a measure aims to minimize the risk of tower collapse and the associated costs due to the lack of energy, injuries to people, and damage to the reputation of the companies involved. In this letter, we report an alternative noninvasive and nondestructive detection technique through the acquisition of electromagnetic scattering parameters. A novel machine learning (ML) approach is proposed in order to achieve the advantages of such a technique and to deal with the respective drawbacks due to the complexity of signals acquired. A strategy to artificially increase the number of relevant samples in the dataset through the simulations of computational models is performed. Different ML classification algorithms are compared in the task of detecting structural faults in anchor rods in order to define the most suitable to this application. Finally, experimental results show that the methodology proposed achieves a high detection accuracy, surpassing other known methods.