An S-Transform and Support Vector Machine (SVM)-Based Online Method for Diagnosing Broken Strands in Transmission Lines

During their long-term outdoor field service, overhead transmission lines will be exposed to strikes by lightning, corrosion by chemical contaminants, ice-shedding, wind vibration of conductors, line galloping, external destructive forces and so on, which will generally cause a series of latent faults such as aluminum strand fracture. This may lead to broken transmission lines which will have a very strong impact on the safe operation of power grids that if the latent faults cannot be recognized and fixed as soon as possible. The detection of broken strands in transmission lines using inspection robots equipped with suitable detectors is a method with good prospects. In this paper, a method for detecting broken strands in transmission lines using an eddy current transducer (ECT) carried by a robot is developed, and an approach for identifying broken strands in transmission lines based on an S-transform is proposed. The proposed approach utilizes the S-transform to extract the module and phase information at each frequency point from detection signals. Through module phase and comparison, the characteristic frequency points are ascertained, and the fault information of the detection signal is constructed. The degree of confidence of broken strand identification is defined by the Shannon fuzzy entropy (SFE-BSICD). The proposed approach combines module information while utilizing phase information, SFE-BSICD, and the energy, so the reliability is greatly improved. These characteristic qualities of broken strands in transmission lines are used as the input of a multi-classification SVM, allowing the number of broken strands to be determined. Through experimental field verification, it can be shown that the proposed approach displays high accuracy and the SFE-BSICD is defined reasonably.