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.

Oil–paper insulation is the critical insulation element in the modern power system. Under a harsh operating environment, oil–paper insulation will deteriorate gradually, resulting in electrical accidents. Thus, it is important to evaluate and monitor the insulation state of oil–paper insulation. Firstly, this paper introduces the geometric structure and physical components of oil–paper insulation and shows the main reasons and forms of oil–paper insulation’s degradation. Then, this paper reviews the existing condition assessment techniques for oil–paper insulation, such as the dissolved gas ratio analysis, aging kinetic model, cellulose–water adsorption isotherm, oil–paper moisture balance curve, and dielectric response technique. Additionally, the advantages and limitations of the above condition assessment techniques are discussed. In particular, this paper highlights the dielectric response technique and introduces its evaluation principle in detail: (1) collecting the dielectric response data, (2) extracting the feature parameters from the collected dielectric response data, and (3) establishing the condition assessment models based on the extracted feature parameters and the machine learning techniques. Finally, two full potential studies are proposed, which research hotspots’ oil–paper insulation and the electrical–chemical joint evaluation technique. In summary, this paper concludes the principles, advantages and limitation of the existing condition assessment techniques for oil–paper insulation, and we put forward two potential research avenues.

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.

The resin impregnated paper high voltage bushing (RHB) has explosion-proof and flame-retardant advantages compared with the oil-impregnated paper bushing. However, the RHB failure is frequently caused by high temperatures, so it is essential to investigate the hot spot temperature of the transformer RHB. Thus, the multi-physics field simulation model of the transformer RHB is developed. Furthermore, the hot spot temperature distribution pattern is obtained. In terms of the transformer RHB temperature rise test, the transformer RHB frequency domain spectroscopy (FDS) is carried out under load and oil temperature variables. In addition, relationships between the transformer RHB hot spot temperature and FDS curves are established. It is illustrated that the inversion method of the transformer RHB hot spot temperature is based on the dielectric loss tangent integral. According to the tanδ curves, the dielectric loss tangent integral and the square of the dielectric loss tangent integral are used as the characteristic parameters for the inversion of the transformer RHB hot spot temperature. The results show that both characteristic parameters have a well-fitting exponential correlation with the transformer RHB hot spot temperature. In addition, this conclusion enables non-destructive measurement of the transformer RHB hot spot temperature to improve the thermal reliability of its operation.

Furfural content dissolved in transformer oil is affected by the furfural diffusion in oil-paper insulation. Previous studies focused on the effects of temperature and moisture on furfural diffusion, while the effect of formic acid on furfural is ignored. Given this, the effect of formic acid on the diffusion properties of furfural is studied in this work through molecular dynamics simulations and macroscopic experiments. First, oil-cellulose-furfural molecular models with different formic acid mass fractions are built, molecular dynamics simulations are performed separately. Also, the furfural diffusion coefficients for different molecular models are determined. Then, insulating paper with different formic acid mass fractions is prepared, and the furfural diffusion coefficients are calculated by combining the measured data and Fick’s second law. Finally, the effect of intermolecular interaction energy on the furfural diffusion in the oil-paper insulation is analyzed. The results revealed that formic acid increased the van der Waals force between furfural and insulating oil, which enhanced the adsorption of furfural and promoted furfural diffusion into the oil. This work explains the effect of formic acid on furfural diffusion, and provides a theoretical basis for furfural analysis.

The applicability of existing single aging indicator-based lifespan prediction methods needs further improvement during different aging stages. Considering the aforementioned challenges, this work proposes a two-stage residual lifespan prediction model based on both methanol and furfural. Firstly, the changes in methanol and furfural contents in oil are examined concurrently with the aging duration. The paper insulation's life is divided into different aging stages by using identified two demarcation points according to the indicator's content. By virtue of this, the two-stage residual lifespan model of the insulating paper is obtained by coupling with the second-order aging kinetic equation. Finally, the validity of the proposed model is proved through a comparison to the conventional method of utilizing a single indicator, with a mean absolute percentage error for insulating paper's residual lifespan of 5.6%. In that regard, the research's contribution is to propose an integrated model for predicting the residual lifespan of insulating paper by using methanol and furfural indicators.