• Energy Research

The operating temperature and the ampacity are important parameters to reflect the operating state of cross-linked polyethylene (XLPE) submarine high voltage (HV) cables, and it is of great significance to study the electrothermal coupling law of submarine cable under the seawater flow field. In this study, according to the actual laying conditions of the submarine cable, a multi-physical coupling model of submarine cable is established based on the electromagnetic field, heat transfer field, and fluid field by using the COMSOL finite element simulation software. This model can help to analyze how the temperature and ampacity of the submarine cable are affected by different laying methods, seawater velocity, seawater temperature, laying depth, and soil thermal conductivity. The experimental results show that the pipe laying method can lead to the highest cable conductor temperature, even exceeding the maximum heat-resistant operating temperature of the insulation, and the corresponding ampacity is minimum, so heat dissipation is required. Besides, the conductor temperature and the submarine cable ampacity have a linear relationship with the seawater temperature, and small seawater velocity can significantly improve the submarine cable ampacity. Temperature correction coefficients and ampacity correction coefficients for steady-state seawater are proposed. Furthermore, the laying depth and soil thermal conductivity have great impact on the temperature field and the ampacity of submarine cable, so measures (e.g., artificial backfilling) in areas with low thermal conductivity are needed to improve the submarine cable ampacity.

The predictive model of aging indicator based on intelligent algorithms has become an auxiliary method for the aging condition of transformer polymer insulation. However, most of the current research on the concentration prediction of aging products focuses on dissolved gases in oil, and the concentration prediction of alcohols in oil is ignored. As new types of aging indicators, alcohols (methanol, ethanol) are becoming prevalent in the aging evaluation of transformer polymer insulation. To address this, this study proposes a prediction model for the concentration of alcohols based on a genetic-algorithm-optimized support vector machine (GA-SVM). Firstly, accelerated thermal aging experiments on oil-paper insulation are conducted, and the concentration of alcohols is measured. Then, the data of the past 4 days of aging are used as the input feature of SVM, and the GA algorithm is utilized to optimize the kernel function parameter and penalty factor of SVM. Moreover, the concentrations of methanol and ethanol are predicted, after which the prediction accuracy of other algorithms and GA-SVM are compared. Finally, an industrial software program for predicting the concentration of methanol and ethanol is established. The results show that the mean square errors (MSE) of methanol and ethanol concentration predictions of the model proposed in this paper are 0.008 and 0.003, respectively. The prediction model proposed in this paper can track changes in methanol and ethanol concentrations well, providing a theoretical basis for the field of alcohol concentration prediction in transformer oil.

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 measurement of polarization and depolarization currents (PDC) based on time–domain response is an effective method for state assessment of cellulose insulation material in oil-immersed electrical equipment. However, the versatility of the data obtained at different temperatures is limited because of the temperature dependence of the PDC. In this respect, the universal conversion of PDC data at different temperatures is an essential aspect to improve the accuracy of the determination of insulating properties of cellulose materials immersed in the oil. Thus, an innovative temperature conversion method based on polarization time-varying current (PTC, obtained by multiplying the polarization current and time) is proposed in this article. In the current work, the PTC data at different temperatures are obtained from the oil-immersed cellulose pressboards with different moisture. Afterwards, the functional model based on the power series theory is used to simulate the PTC data, through which the coefficients of the power series are found related to the test temperature of the PTC and the moisture content (mc%) of the oil-immersed cellulose pressboards. Furthermore, the functional relationship among moisture, test temperatures, and the feature parameter calculated by these coefficients is established. Thus, the PTC data at various temperatures can be calculated by the established function. The potential application ability of the proposed method is verified by comparing the calculated results with the measured results obtained from the various samples.