This paper gives information on the electrical and physiochemical characteristics of six different types of palm oil compared with traditional mineral oil. We found that natural processed crude palm oil (PO-C) had a higher resistance to AC breakdown voltage than other types of palm oil, including traditional mineral oil. The results of the positive lightning impulse voltage test for PO-C were still the highest compared to other types of palm oil, including traditional mineral oil, at 58.26%. The summarised dissipation factors of all tested crude palm oils were significantly higher than those of mineral oils, which will make the palm oil less insulating, especially in PO-A palm oil (36.197%), where the values were higher than those of other oils, while mineral oil has a slightly increased dispersion factor. For relative permittivity, all palm oils were compared, and it was found that PO-C had a lower relative permittivity than the other oils. In terms of physical and chemical properties, in the moisture content test on all oils, PO-C had the percentile with the highest moisture content decrease of 58.74%. In the case of testing the surface tension value, it was found that traditional mineral oil had the highest value (48.46 m/Nm) when compared to palm oil. On the other hand, the acidity in traditional mineral oil is the lowest (0.03 mg KOH/g) compared to all palm oils. Results from studies demonstrate the possibility of using natural processed crude palm oil, or PO-C, as a replacement for traditional mineral oil. This is consistent with the results of electrical properties that show PO-C is higher than other types of palm oil and includes traditional mineral oil.

Mineral oil (MO) has been widely used in transformers because of its reasonable price and acceptable properties. Currently, alternative liquid insulations have been proposed with some excellent properties. Therefore, the mineral oil characteristics need to be enhanced for high performance to compete with such alternative liquid insulations. This paper aims to study the power frequency dielectric dissipation factor (tanδ) along with polarization and depolarization current (PDC) characteristics of the mineral oil-based nanofluid and natural ester (NE)-based nanofluid. Besides, Fourier-transform infrared spectroscopy (FT-IR) measurement of the test specimen was performed. Titanium dioxide (TiO₂) and zinc oxide (ZnO) with 0.01 and also 0.03 volume concentration were used to mix with the unmodified liquid to create liquid insulation-based nanofluid. Then, unmodified mineral oil, unmodified natural ester, mineral oil-based nanofluid, and natural ester-based nanofluid were comparatively investigated. Tanδ at power frequency and the PDC characteristics of such liquids were examined. For PDC measurement, TiO₂ clearly affected the polarization current and conduction loss factor, whereas ZnO evidently impacted the depolarization current and polarization loss factor. In the case of FT-IR test results, there was no significant change when the modified insulating liquid was compared with those of the unmodified insulating liquid.

This paper introduces a statistical classification in regression model to classify partial discharge (PD) patterns into four categories in corona: high voltage side in air, corona at low voltage side in air, surface in air, and internal discharge. There are nine independent variables from fingerprint analysis which mainly are skewness, kurtosis, asymmetry and cross correlation following Phi - q - n PD patterns. The variable independent data are divided into two group: the forms for training the model and the pattern for test the model. The created algorithm investigates the best of parameter group from all of independence variables for creating the regression model. In this work, the group of five best parameters for classification are selected. The results show, that only five parameters in the model has a good performance to classify PD pattern with the classification accuracy of 100%.

With the increasing adoption of lithium-ion batteries in energy storage systems, accurately monitoring the State of Health (SoH) of retired batteries has become a pivotal technology for ensuring their safe utilization and maximizing their economic value. In response to this need, this paper presents a highly efficient estimation model based on the multi-input metabolic gated recurrent unit (MM-GRU). The model leverages constant-current charging time, charging current area, and the 1800 s voltage drop as input features and dynamically updates these features through a metabolic mechanism. It requires only four cycles of historical data to reliably predict the SoH of subsequent cycles. Experimental validation conducted on retired Samsung and Panasonic battery cells and packs under constant-current and dynamic operating conditions demonstrates that the MM-GRU model effectively tracks SoH degradation trajectories, achieving a root mean square error of less than 1.2% and a mean absolute error of less than 1%. Compared to traditional machine learning algorithms such as SVM, BPNN, and GRU, the MM-GRU model delivers superior estimation accuracy and generalization performance. The findings suggest that the MM-GRU model not only significantly enhances the breadth and precision of SoH monitoring for retired batteries but also offers robust technical support for their safe deployment and asset optimization in energy storage systems.

This paper addresses the challenges of accurately estimating the state of health (SOH) of retired batteries, where factors such as limited historical data, non-linear degradation, and unstable parameters complicate the process. We propose a novel SOH estimation model based on an Integrated Hierarchical Extreme Learning Machine (I-HELM). The model minimizes reliance on historical data and reduces computational complexity by introducing health indicators derived from constant charging time and charging current area. The hierarchical structure of the Extreme Learning Machine (HELM) effectively captures the non-linear relationship between health indicators and battery capacity, improving estimation accuracy and learning efficiency. Additionally, integrating multiple HELM models enhances the stability and robustness of the results, making the approach more reliable across varying operational conditions. The proposed model is validated on experimental datasets collected from two Samsung battery packs, four Samsung single cells, and two Panasonic retired batteries under both constant-current and dynamic conditions. Experimental results demonstrate the superior performance of the model: the maximum error for Samsung battery cells and packs does not exceed 2.2% and 2.6%, respectively, with root mean square errors (RMSEs) below 1%. For Panasonic retired batteries, the maximum error remains under 3%.

This paper presents results of properties of Silicone Rubber (SiR) and Ethylene Propylene Diene Monomer (EPDM) insulation study, e.g. ageing, hydrophobic when they are under polluted dust in Thailand with ozone, UV and temperature. The results showed that the insulation resistance of SiR and EPDM under low ozone environment were sharply and significantly decreased in the first three days no matter they were exposed to various kinds of polluted condition or they were original. When the duration of ageing was prolonged, the insulation resistance trends to be reduced. In addition, the insulation resistance of SiR and EPDM under high ozone environment were slightly lower than that of low ozone environment. The insulation resistance of SiR under UVA, UVC and temperature at 30 °C and 50 °C were significantly decreased in the first three days when they were exposed various kinds of polluted dust including original. When the duration is prolonged the insulation resistance trends to be reduced. The insulation resistance of EPDM when under UVA, UVC and temperature at 30 °C and 50 ° C were slightly decreased during the first 3 days. When the time is prolonged, the insulation resistance remains constant.