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.

In order to explore the partial discharge characteristics of 10 kV XLPE insulated cable (hereinafter referred to as ‘XLPE cable’), first six typical defects are made on the main insulation according to the requirements of the installation process and the key process in which quality problem easily occur. They are: The main insulation has a dent, the main insulation has a knife wound, the main insulation has a metal tip, the main insulation surface has impurities, the high voltage end has a metal tip, and the stripping of the semi‐conductive fracture is irregular. Then, the voltage step up test is carried out, and the test is monitored by the local discharge detection system. The test results show that the typical defects caused by the installation of 10 kV XLPE cable joint will produce serious local discharge phenomenon, and the initial discharge voltage is low and the stable discharge quantity is large.

Detection of corona discharge of the transmission line can reflect its health status and provide warning information for maintenance. Some existing detection methods are flawed and cannot be used widely. This study presents a new method by measuring the electromagnetic wave caused by corona discharge with RF antenna. Then a circularly polarised log‐periodic antenna was designed by making two pairs of log‐periodic antennas 90° orthogonal. Finally, this antenna was simulated and manufactured to detect electromagnetic signals, the result shows that the time domain signal of corona discharge is a high frequency oscillation wave and its fast Fourier transform main distributes between 200 and 350 MHz.

Laser-induced breakdown spectroscopy (LIBS) is widely used for online quantitative analysis in industries due to its rapid analysis and minimal damage. However, challenges like signal instability, matrix effects, and self-absorption hinder the measurement accuracy. Recent approaches, including the internal standard method and crater limitation method, aim to improve the stability but suffer from high computational demands or complexity. This study proposes a method to enhance LIBS stability by utilizing craters formed from laser ablation without external cavity assistance. It first improves the plasma temperature calculation reliability using multiple elemental spectral lines, after which electron density calculations are performed. By fitting plasma parameter curves based on laser pulse counts and using a laser confocal microscope for crater analysis, stable plasma conditions were found within crater areas of 0.400 mm² to 0.443 mm² and depths of 0.357 mm to 0.412 mm. Testing with elemental spectral lines of Ti II, K II, Ca I, and Fe I showed a significant reduction in the relative standard deviation (RSD) of the LIBS spectral line intensity, demonstrating an improved signal stability within specified crater dimensions.

Strikes of lightning, corrosion of chemical contaminants, ice shedding, wind vibration of conductors, lines galloping and damage due to external forces may induce some fatal accidents such as broken transmission lines. The common means to be used for latent faults inspection in transmission lines is manually and periodically examined by workers from commercial electrical power company. With the development of both the artificial intelligence technologies and the smart grid, the method of detecting broken strands in transmission line by inspection robot with detectors is a method with good prospect. In this paper, a method with eddy current transducer carried by robot for detecting broken strands in transmission line is developed and the broken strands in transmission line identification approach based on S-transform is proposed. The proposed approach utilizes 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 broken strands identification confidence degree is defined with 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 line are the input of support vector machine with multi-classification, and then the number of broken strands can be determined. Through field experimental verification, it can be concluded that the proposed approach shows high accuracy and the SFE-BSICD is defined reasonably. Copyright © 2012 John Wiley & Sons, Ltd.