The precise location of insulators in infrared images is of great significance for insulator condition monitoring and fault diagnosis. Due to the characteristics of insulators themselves and the use of handheld infrared cameras, insulators usually appear in infrared images with different aspect ratios and main axis orientations. Therefore, it is very important and necessary to make full use of the prior knowledge of the insulator itself to accurately locate it. However, most of the existing methods use axial horizontal detection boxes to detect insulators, which cannot take into account the characteristics of the insulator well. When there are large overlapping areas of two horizontal detection boxes, the nonmaximum suppression algorithm may lead to missed detection of the object. To further improve the accuracy of the detection algorithm, this article makes full use of the prior features carried by the insulator itself, and optimizes faster region-based convolutional neural networks (R-CNN) from five aspects: rectangular box representation, feature extraction, candidate box generation, anchor design, and feature alignment. An oriented detection model for infrared images of insulators is constructed. Comparative experiments with a variety of mainstream detection methods were carried out on the constructed infrared dataset. The results show that the proposed method is superior to other models in detection accuracy. When the intersection and union ratio is 0.5, the average precision reaches 95.08%. In addition, it can also effectively predict the shape and angle information of insulators in complex scenes, laying a beneficial foundation for subsequent diagnosis automation tasks.

Chemical compounds dissolved in insulating oil, as indicators can excellently monitor the paper aging condition, which has attracted increasing interest in areas of transformer condition monitoring and fault diagnosis. Because of their outstanding features, such as good correlation with the degree of polymerization of cellulose paper and the aid of non-destructive online monitoring, chemical indicators have been effectively used for transformer condition assessment. In this study, a comprehensive, in-depth insight into the indicators of the aging of insulating paper from aging characteristics, physico-chemical characteristics, shortcomings of various compounds, generation pathways and mechanisms, and monitoring technologies are provided. It is expected that these chemical indicators can provide better guidance for the evaluation of paper insulation performance and transformer aging. In addition, the latest research progress, as well as current challenges and future prospects are also outlined. This study provides a theoretical basis and reference for chemical indicators in the fields of microscopic formation mechanism, diffusion equilibrium phenomenon, and insulation aging state assessment.

Infrared thermal imaging technology plays an important role in evaluating the operating condition of electrical equipment. This article focuses on two critical environmental factors, namely, wind speed and ambient temperature. They interfere with infrared detection and fault diagnosis by affecting the actual temperature of the fault area. In this article, we use the principle of heat transfer to explore the affected conditions and the changing laws of diagnostic indicators. We provide a solution for diagnosing the live equipment fault area after compensating for the infrared temperature. Both the simulation and experimental results show that the method can obtain the temperature of the equipment fault area under different environmental conditions for different electrical equipment failures when they meet the temperature compensation conditions. The method can eliminate the influences of wind speed and ambient temperature on the results of infrared temperature measurement and achieve a unified solution to the problem of multiple types of electrical equipment. This breaks through the barriers of infrared temperature measurement applications limited by the on-site environmental conditions. The proposed method can serve to further improve the existing evaluation standards. The results of the article are useful for troubleshooting equipment and improving the accuracy of the infrared diagnosis of live equipment.

The evaluation of the state of transformer oil-immersed insulation based on the traditional methods becomes unreliable due to the nonuniform aging effect. To address this issue, a novel model for accessing the aging information of transformer oil-immersed insulation at the hotspot is proposed. In this article, frequency-domain spectroscopy (FDS) is selected as the carrier for characterizing the aging states of oil-immersed insulation. The multiobjective function for reversing the FDS of the hotspot is constructed based on the dielectric response equivalent circuit. Then, the nondominated sorting genetic algorithm with the multiconstraint scheme is proposed to solve the multiobjective function, by which the FDS data of the transformer hotspot region are acquired. The verification results indicate that the average standard deviation between the computed FDS of the hotspot region and the measured data is less than 0.26. The contribution of this article is in the exploration of the proposed model as a potential tool to extract the FDS data of the hotspot region. This will promote a more reliable aging evaluation of the transformer oil-immersed paper insulation at the hotspot.

Due to the continuous expansion of grid-connected renewable energy sources (RESs), the local-based methods of islanding detection are facing new challenges. We propose a communication-based detection method based on topology graph path query that can achieve improved results. This method uses existing communication technology and intelligent electronic devices (IEDs) in distribution networks to construct a distributed islanding information system. The bidirectional breadth first search (B-BFS) algorithm is used to conduct an offline path accessibility query to establish the decision islanding path (DIP) database. Then the method can quickly obtain results of islanding detection from online verification when the state of the line changes. On this basis, according to the topological structure and operation characteristics of the distribution network, we propose to use the Dijkstra algorithm based on closed-loop constraints to query the shortest path and select appropriate tie switch. This can achieve the goal of preventing islands by maintaining connectivity through synchronous switch operations. The proposed method has been verified on testing systems, and the results show that: (1) The distributed islanding information system has outstanding performance in solving islanding problems; (2) The proposed detection method has a convincing real-time performance; (3) The method of islanding prevention can fundamentally avoid the emergence of unplanned islands and their subsequent negative effects.

The method using ethanol to evaluate the cellulose insulation aging condition of oil-immersed transformers has been proposed. At present, the dominating method for detecting ethanol in insulating oil is to use headspace–gas-chromatography–mass-spectrometry (HS-GC-MS). However, the problem of quantitative inaccuracy will be sometimes encountered in the actual detection process due to improper instrument parameter setting and improper manual operation. In this study, as an aging marker, ethanol in transformer insulating oil was separated by using VF-624 ms capillary column. The effects of gas-chromatography–mass-spectrometry (GC-MS) optimization conditions, headspace equilibrium temperature, headspace equilibrium time and standard solution preparation method on the determination of ethanol content in oil were discussed, and optimized measures were proposed. The experimental results showed that the measurement can be more accurate under the headspace temperature of 80 °C and the headspace time of 40 min, and relative standard deviation percentage (RSD%) could reach to 4.62% under this condition. It was also pointed out that, for the preparation of standard solution, the method which controlled the sampling volume of anhydrous ethanol by microliter syringe could make the peak area of ethanol chromatogram have a better linear relationship with the standard curve. Under the similar linear range, the goodness of fitting curve without diluting process could be as high as 0.9993, while the method of preparing the stock solution and diluting stepwise to obtain the fitting curve only had a goodness of 0.9910. The method was validated by standard addition recovery test, and the recovery values obtained were between 90.3% and 95.8%. The optimized method is of great significance for the measurement of ethanol dissolved in insulating oil.