Insulators play a crucial role in electric power systems by isolating high-voltage currents and supporting conductors. However, insulators exposed to outdoor conditions for extended periods are susceptible to faults caused by contamination, aging, and damage, leading to flashovers that may result in grid outages and economic losses. Accurate assessment of insulator condition is paramount for the safe operation of power systems. Non-contact monitoring methods, which do not interfere with the normal operation of the power system, have become mainstream in insulator condition monitoring, contrasting with traditional contact-based detection. Among these methods, optical imaging technology has garnered significant attention due to its non-contact, non-destructive, high sensitivity, and intuitive characteristics. Optical imaging technology captures reflections, transmissions, and absorptions in different spectral bands to gather information about the surface and interior of insulators, thereby determining the type of faults they may exhibit. This article reviews optical imaging detection methods applied in the field of insulator condition assessment, including infrared thermography, ultraviolet imaging, visible light imaging, and some promising optical monitoring technologies. Additionally, the advantages and limitations of these methods under various insulator fault conditions are analyzed, and challenges in industrial applications are discussed. The purpose of this review is to provide a comprehensive and systematic reference for the application of optical imaging technology in insulator condition assessment, aiming to enhance the efficiency and safety of power system operation and maintenance.

This paper focuses on resolving the storage issue of correlation matrices generated by kriging surrogate models in the context of electromagnetic optimization problems with many design variables and multiple objectives. The suggested-improved kriging approach incorporating a direct algorithm is able to maintain memory requirements at a nearly constant level while offering high efficiency of searching for a global optimum. The feasibility and efficiency of this proposed methodology are demonstrated using an example of a classic two-variable analytic function and a new proposed benchmark TEAM multi-objective Pareto optimization problem.

When a high-speed train approaches an insulated split-phase section embedded between neighbouring power supply arms carrying powers with different phases, the vehicle-mounted vacuum circuit breaker (VCB) needs to be switched off to avoid short-circuit fault during the phase-changing procedure. At the moment of switching the VCB contacts, the arc is triggered easily, accompanying with overvoltage impulse. As the core of the whole system, the traction transformer frequently suffers the invasion of the overvoltage impulse, which possibly causes partial discharge or breakdown accidents. To evaluate the impact of overvoltage on the transformer, it is essential to observe the overvoltage distribution at different positions in the transformer. As an equivalent circuit model of the traction transformer involving the spatially distributed capacitance is built based on the improved capacitor network methodology (ICNM), the partial discharge risk in the transformer has been assessed. Ultimately, the multi-physics coupling models based on the finite element method have been launched for observe the specific distribution of the multiphysics field for verifying the validity of equivalent circuit model based on ICNM. These works lay the theoretical foundation for guiding the design of insulation and the thermal dissipation settings of the traction transformer, considering the threat brought from impulse.

So far, silicone rubber (SiR) insulators have been widely used in the power industry all over the world, due to their outstanding insulation properties with high reliability. In practice, some of the insulators are applied in the remote places with harsh environmental conditions such as high altitude, high temperature and high humidity etc. Herein, the currently-existing aging mechanisms have been investigated, as some novel insights into these testing and evaluation methods have been proposed. Via the investigation, it can be found that thermal oxygen aging tends to occur, when insulators are exposed to an environment surrounded by hot air. To analyse the different aging mechanisms of SiR insulator sheds, four types of aging testing and evaluation methods have been systematically summarized, based on the surface structural, chemical, optical, and electrical properties. The conclusion is that non-destructive and non-contact methods, such as HSI, along with the multi - source information fusion methods, are becoming increasingly popular in the assessment of silicone rubber insulator sheds aging. These research efforts provide assurance for reducing the incidence of pollution flashover accidents in power transmission lines and ensuring the safe and stable operation of power transmission and transformation equipment.

Pollution flashover of insulators has been viewed as a common issue existing in the area of power system for a long term. However, traditional detection methods such as equivalent salt density and leakage current can hardly satisfy the requirements of the progressing smart grid. Herein, a novel highly-efficient method is proposed for classifying the pollution degree detection of insulators based on hyperspectral technology. The insulating sheets and actual insulators were set as the research object and their hyperspectral images are obtained. Moreover, the data for describing the characteristic wavelengths and the G color components of the samples were extracted from the images as the characterization of the equivalent salt deposit density (ESDD) and non-soluble deposit density (NSDD) respectively. The ESDD and NSDD detection model is established based on the multiple linear regression (MLR) algorithm and the random forest (RF) algorithm. Ultimately, the proposed model reaches detection accuracy of above 84% on the insulating sheets and 75% on the actual insulators, which can be viewed as acceptable. The hyperspectral imaging technique has considerable potential for the non-contact detection of ESDD and NSDD.

Strong ultraviolet (UV) radiation is one of the main factors that lead to the aging of composite insulators of transmission lines in high-altitude areas, which has brought security risks to the operation of power systems. The aging status information obtained by traditional insulation surface aging detection methods is indirect and limited, or time-consuming and labor-intensive. In this article, a non-contact non-destructive testing evaluation method based on hyperspectral technology is proposed to quickly detect the UV aging state of composite insulator surface. First, the hyperspectral imaging platform is used to image a number of continuous bands on the insulator surface. Combining the aging calibration data, a locally reserved projection-convolutional neural network (LPP-CNN) model is established to extract the characteristic bands of the aging surface and provide a way to accurately evaluate the surface UV aging degree of insulators. The model has a high resolution of aging degree, which provides a new idea and theoretical basis for realizing non-contact non-destructive online detection of surface aging under UV light.