This paper presents a novel approach for power transformer incipient fault diagnosis through the analysis of dissolved gas in oil. The proposed approach is implemented for improving the diagnosis accuracy by dissolved gas analysis (DGA) of power transformer based on the combined use of a multi-classification algorithm self-adaptive evolutionary extreme learning machine (SaE-ELM) and a simple arctangent transform (AT). On the one hand, the SaE-ELM algorithm has the ability to approximate any nonlinear functions with its structure parameters, i.e. hidden node biases and output weights, optimized self-sufficiently. On the other hand, the AT can alter the data structure of the experiment data, which will enhance the generalization capability for SaE-ELM as well as other machine learning algorithms. Thus, the combination of SaEELM and AT can complement each other and improve the diagnosis accuracy from the aspect of both algorithm and data structure. The performances of the proposed approach are compared with that derived from ANN, SVM, and ELM methods, respectively. Experimental results with both published and power utility provided data indicate that the developed approach can significantly improve the accuracies for power transformer fault diagnosis.

As aerospace, electrified railway, weapon equipment manufacturing, and other fields have leapt forward, the operating environment of current-carrying friction pairs is becoming increasingly severe, and research on the current-carrying friction and wear theory and its vital technologies are progressively in demand. This study summarizes the relevant research on the current-carrying friction and wear. In this study, the essential characteristics and classification of current-carrying friction and wear are summarized, the effect of working parameters on current-carrying friction and wear performance is clarified, and the generation mechanism, failure mechanism, and factors of current-carrying friction and wear are emphatically investigated. Moreover, the mechanism of arc generation and the effect of environmental conditions and surface facial masks on the friction and wear process are summarized. This paper also introduces the preparation technology of a conductive wear-resistant self-lubricating material, the main factors affecting the conductive wear-resistant property of the coating, and the action mechanism. The simulation and prediction results of the current-carrying friction and wear temperature field and the wear amount are presented. Finally, the problems in the current-carrying friction and wear research are classified, and future research directions in this field are proposed. The future’s critical development and improvement directions are also proposed from the aspects of developing coating quality evaluation equipment, optimizing the coating quality, and studying the coating self-lubricating mechanisms.

Composited cable terminals are critical for reliable power delivery of traditional and traction power supply systems. During its operation, the cable terminal continually undergoes complex circumstances where humidity, temperature, field strength, mechanical stress and other factors continuously change at different levels. In extreme cases, defects or even failures may occur in the cable terminals, leading to catastrophic accidents. This makes the condition monitoring and defect detection of composited cable terminals become an imperative need for the power industry. The “state‐of‐the‐art” technology of cable terminal condition assessment is focussed and a systematic review of the current research progress of cable terminal condition assessment from the operation circumstances to failure mechanisms is provided. It covers both online/offline condition monitoring methods and defects detection approaches. In addition, challenges and future research directions for cable terminal condition assessment are also addressed. It is concluded that the non‐destructive and non‐intrusive methods like terahertz imaging and ultrasonic testing, and multi‐source information fusion methods as well as the digital twin technology have been gaining popularity for cable terminal defect inspection. It is expected the presented work can provide a global field of vision for further advancement of both scholars and industrial engineers in this field.

Moisture damp has been identified as one of the most harmful agents of multilayer composited insulation, which can seriously accelerate its degradation, cause unexpected failure, and even lead to catastrophic accidents. This article proposes a nonintrusive method for moisture inspection of multilayer insulation structure based on terahertz (THz) technology with a focus on the cable terminal of the electric multiple units (EMUs). First, a three-layer scattering model is established based on the actual structure of the composite cable terminal, and theoretical analysis is performed to reveal the potential application mechanism. Based on that, an artificial model of a three-layer structure is then built on which the insulation damp test is carried out. Finally, the THz spectrum and THz imaging tests are performed. Results show that the frequency-domain signal can effectively identify the moisture inside the multilayer composited insulation. The imaging results in the frequency domain of the sample can accurately determine the geometric parameters of the damp area. By further analyzing the time-domain spectroscopy, the thickness of insulation affected by moisture can be identified quantitatively. This article verifies the feasibility of THz technology in inspecting the moisture dampness of multilayer composite insulation and its potential application in the damp condition assessment of EMU cable terminals.

Cable terminals of the high-speed electric multiple units (EMUs) are prone to undertake insulation delamination defects due to their special operating conditions. Such a defect is difficult to identify and it may pose a great threat to the safety of the EMUs. To achieve accurate detection of insulation delamination defects, this article proposes a terahertz (THz)-based approach that combines the spectrum of the THz wave and THz imaging techniques. Specifically, a synthetic multilayer composite insulation structure with an air gap is firstly built to simulate the internal delamination defect of the cable terminal. Then THz tests in both time and frequency domains are performed on the synthetic structure. Experimental results show that the delamination defect of the multilayer insulation structure can be quickly detected by observing the absorption spectrum in the THz frequency domain. The location and geometry of the defect can be preliminarily determined by the THz imaging and then further refined through image segmentation. In addition, the thickness of the delamination defect can be quickly calculated by the THz time-domain spectrum (TDS). By combining the THz spectrum and the THz imaging information, the delamination defects are quantitatively detected for the multilayer composite structure or even for the EMU’s cable terminals.

As a critical indicator for assessing the survivability and condition of transformers in a fleet, the transformer health index has attracted attention from both asset owners and international organizations like CIGRE and IEEE DEIS/PES. To provide a systematic and comprehensive review for further study or to guide transformer asset management, this paper summarizes the state-of-the-art of the transformer health index, from the early proposed weighted-score-sum approaches to the more recently proposed artificial intelligence algorithm-based methods. Firstly, different methods for determining the transformer health index are reviewed. Each of these is specified as belonging to a certain type on the basis of its formulation and composition schematic. Subsequently, the steps to determine each type of health index are summarized, and examples derived from literature are provided for further illustration. Comparisons are finally carried out in order to better understand the pros and cons of different types of transformer health index, and the future development trends for transformer health indexes are also discussed. This work can serve as a valuable reference for the survivability and condition assessment of transformers in the power industry.