Evaluation of Multivariable Modeling Methods for Monitoring the Health of Guyed Towers in Overhead Power Lines.

This article proposes a methodology for monitoring the structural stability of each tower of an electric power transmission line through sensor measurements which estimates the different situations that may indicate the need for intervention to prevent the structure collapsing. The extended Kalman filter was adopted to predict the failures, considering sensor fusion techniques such as the displacements of the upper central position of the tower above certain limits. The load of the stay cables is calculated from the natural frequencies, which are determined by the accelerometers connected to the cables. The average value of these forces, which must be higher than a normal limit, were calculated to predict a failure. All guyed towers of a power transmission line thousands of kilometers long will be individually monitored considering the methodology described in this study, which makes this article one of the first relevant research studies in this area. Typically, guyed towers must often be manually inspected to ensure that the stay cables have acceptable pretension to prevent a lack of stability in the transmission line towers.

Evaluation of Machine Learning Methods for Monitoring the Health of Guyed Towers.

This paper presents the development of a methodology to detect and evaluate faults in cable-stayed towers, which are part of the infrastructure of Brazil's interconnected electrical system. The proposed method increases system reliability and minimizes the risk of service failure and tower collapse through the introduction of predictive maintenance methods based on artificial intelligence, which will ultimately benefit the end consumer. The proposed signal processing and interpretation methods are based on a machine learning approach, where the tower vibration is acquired from accelerometers that measure the dynamic response caused by the effects of the environment on the towers through wind and weather conditions. Data-based models were developed to obtain a representation of health degradation, which is primarily based on the finite element model of the tower, subjected to wind excitation. This representation is also based on measurements using a mockup tower with different types of provoked degradation that was subjected to ambient changes in the laboratory. The sensor signals are preprocessed and submitted to an autoencoder neural network to minimize the dimensionality of the resources involved, being analyzed by a classifier, based on a Softmax configuration. The results of the proposed configuration indicate the possibility of early failure detection and evolution evaluation, providing an effective failure detection and monitoring system.