IET Nanodielectrics最新文献

This research study investigates the influence of various nanoparticles on the dielectric breakdown voltage, oil dissipation factor, viscosity, and thermal conductivity of nanofluids. Nanofluids were prepared using synthetic ester oil as the base fluid, and three nanoparticles, silicon carbide (SiC), boron nitride (BN), and zirconium dioxide (ZrO₂), were added at different concentrations (0.125 wt%, 0.250 wt%, and 0.375 wt%), which are basically the nano-sized powder that can be blended in the oil. The dielectric breakdown voltage testing was conducted to evaluate the electrical performance of the nanofluids. Additionally, rheological measurements were performed to study the kinematic viscosity, while thermal conductivity was determined using appropriate techniques. The enhancements in each property were evaluated and compared for the different nanoparticle concentrations and types. Previous studies focused only on the investigation of the electrical properties of nanofluids. However, in the present study, the electrical as well as thermo-physical characterisation of nanofluids is performed and analysed as they directly affect the cooling performance of transformers. The results provide dielectric and thermo-physical characterisation that exhibit excellent insulation and cooling functionalities and valuable insights into the potential applications of nanofluids as dielectrics in various high-voltage electrical equipment. ZrO₂ and SiC nanoparticles exhibited a reduction in the oil dissipation factor. SiC consistently improved breakdown voltage (Bdv), while ZrO₂ nanoparticles showed concentration-dependent effects, enhancing Bdv at low concentrations but degrading it at higher ones. Unexpectedly, nanoparticle dispersion and lubrication effects can lead to viscosity reductions, countering conventional expectations. Surprisingly, at the highest concentration, the thermal conductivity decreases compared to the lower nano-concentrations in synthetic ester oil.

Nano-modified electrical insulating fluids are a promising new family of insulating oils with enhanced characteristics. They can significantly improve many properties, such as fire point, breakdown voltage, partial discharge inception voltage and thermal conductivity etc. However, nanoparticles have raised concerns about the possible harm to human health and the ecosystems, but the environmental impact of nano-modified insulating oils is far more complicated than that. Following the recent research results on the stability of nano-modified particles, the authors introduce environmental aspects that have not attracted attention so far, such as the possible loss of stability of the insulating oil, mechanical erosion problems in parts of the electrical transformer and problems in recycling processes that may turn waste nano-modified insulating oils into an unwanted feed stock for recycling industries. An improved method for the environmental risk assessment (RA) of nano-modified insulating oils, based on an existing model for the RA of nanoparticles, is proposed. The authors reflect the complicated nature of the nanoliquids, mainly due to the stability of the element, which seems to have a paramount role on their environmental impact and is neglected by the current approach in RA.

This contribution presents a two-step hybrid diagnostic approach, combining k-means clustering for subset formation, followed by subset analysis conducted by human experts. As the feature input vector has a significant influence on the performance of unsupervised machine learning algorithms, seven feature input vectors derived from traditional methods, including Duval pentagon method, Rogers ratio method, three ratios technique, Denkyoken method, ensemble gas characteristics method, Duval triangle method, and Gouda triangle method were explored for the subset formation stage. The seven proposed individual methods, corresponding to the seven feature input vectors, were implemented using a dataset of 595 DGA samples and tested on an additional 254 DGA samples. Furthermore, a combined technique based on a support vector machine was introduced, utilising the diagnostic results of the individual methods as input features. From training and testing, with diagnostic outcomes of 91.09% and 90.94%, the combined technique demonstrated the highest overall diagnostic accuracies. Using the IEC TC10 database, the diagnosis accuracies of the proposed diagnostic methods were compared to existing methods of literature. From the results obtained, the combined technique outperformed the proposed individual methods and existing methods used for comparison.

In regions characterised by high humidity and abundant vegetation, the external insulation materials utilised in high-voltage transmission and transformation are frequently fouled with a multitude of microorganisms, thereby jeopardising the reliable operation of the power grid. A comprehensive overview of the periodic growth regulation of parasitic algae is presented upon insulating composites across various regions of the world. Additionally, it highlights the methods for quantitative evaluation and accurate prediction of algae coverage degree. Also, the biological contaminates coating process and the artificial flashover test method are summarised, and the effects of algae on the reliability of inorganic and organic materials used for external insulation are compared. It emphasises the dynamic hygroscopic characteristics and cytoelectronegativity of cell secretions as the critical factors that negatively affect the hydrophobicity and flashover performance of silicone rubber insulators by algae. Furthermore, valuable insights into the long-term inhibition of algae growth on polymeric insulators are provided using eco-friendly antibiotic-loaded silica aerogel nanocomposites.

Jurkat cells are immortalised lines of human T lymphocyte cells widely used in research to study leukaemia, signalling of T cells, and immune responses. These cells can be used as models to define the mechanisms of leukaemia and to develop mechanism-based therapies. Jurkat cells can be detected with remarkable accuracy using the recently created Photonic Crystal Fibre (PCF). The suggested design has a hybrid arrangement on its clad surface and a rectangular core. The recently released PCF analyser displays a maximum Relative Sensitivity are 95.81% for Jurkat (type I) and 94.93% for Jurkat (type II), respectively. The Effective Material Loss of 0.0070 cm⁻¹, 0.0080 cm⁻¹, and the Confinement Loss of 9.11 × 10⁻⁹ dB/m, 9.15 × 10⁻⁸ dB/m were also examined for the previously described units. Jurkat cells represent a model of leukaemia that is very malignant and proliferative, representative of aggressive T-cell acute lymphoblastic leukaemia with the ability to progress rapidly and hence poor prognosis for patients. The benefit of applying a suggested PCF sensor to Jurkat cell detection lies in the high sensitivity to refractive index changes, allowing label-free and real-time monitoring of cell interaction. This PCF sensor can offer high light-matter interaction, custom geometry, and biocompatibility for specific and reliable detection of deadly Jurkat cells in biomedical research and clinical diagnostics.

Toughening plays a key role in epoxy resins (EPs) and their composites for high voltage gas-insulated switchgear (GIL) tri-post insulators and receives a lot of attention. However, there are still limited research studies on strain and its distribution for the toughened EPs and composites under tension and especially under high electric fields. Herein, the intrinsically toughening mechanism of EPs (toughening ability: EP-B > EP-A) and their composites with Al₂O₃ (toughening ability: EP-Bcom > EP-Acom) was explored in terms of chemical characterisation by IR and molecular motion via differential scanning calorimetry and dielectric spectra. A low rigid segment content in EPs contributes to the excellent toughness. Two-dimensional digital image correlation (2D-DIC) and three-dimensional DIC (3D-DIC) were utilised to probe strain and its distribution in EPs and their composites under tension and electric fields, respectively. EP-B with more toughness endows it with a larger strain ε_F under tensile fields and a greater strain amplitude |ε_E| under electric fields than EP-A, such as 9278 με at 1 kN, 16.9% greater than EP-A and 9767 με at 10 kV/mm, 19.3% higher than EP-A. In addition, all samples show minus strain under electric fields due to compression. With the introduction of Al₂O₃, EP-Bcom exhibits a ε_F of 2870 με at 1 kN, 69.1% lower than that of EP-B and 49.4% greater than that of EP-Acom, and it provides |ε_E| of 5351 με at 10 kV/mm, 45.2% lower than that of EP-B and 13.2% greater than that of EP-Acom. Further, samples with more toughness deliver more uniform strain distribution whether under tension or electric fields.

This research investigated the influence of temperature and humidity on the giant dielectric (GD) properties of 1% indium tin oxide and 1% Ta₂O₅ co-doped TiO₂ ceramics sintered at different temperatures. A single phase of rutile TiO₂ was obtained in all sintered samples. The mean grain size increased with higher sintering temperatures, resulting in ceramics with a relative density exceeding 98%. A uniform dispersion of dopants and major elements was achieved. Remarkably, the dielectric constant increased significantly from 2 × 10³ to 3.7 × 10⁴ with a rising sintering temperature, primarily due to the enlarged grain size. Concurrently, the authors observed low loss tangents, ranging from tanδ≈0.016 to 0.024 at 1 kHz. Slight variations in the dielectric constant were observed with temperature from room temperature up to 210°C, while maintaining remarkably low tanδ. The GD properties were attributed to space charge polarisation at internal interfaces and defect dipoles. Further research explored the impact of environmental conditions on dielectric properties. Remarkably, the ceramics exhibited minimal capacitance variations of less than 10% within the relative humidity range of 30%–95% and temperatures from 15 to 85°C, indicating excellent dielectric stability.

The authors present an innovative photonic crystal fibre (PCF) detector that has the ability to detect sulphur dioxide (SO₂) gas. This deadly gas having no tone at all with a strong aroma. Our suggested sensor is designed with heptagonal cladding along with octagonal core. It displays exclusive performance in detecting SO₂ with an elevated relative sensitivity (RS). The max RS for this sensor is 87.39% with a tiny Confinement Loss of 6.8194 × 10⁻⁴ dB/m and having a total loss of 1.80609 × 10⁻² dB/m at optimum frequency of 1.8 THz. Suggested PCF sensor also has effective material loss of 0.017379 cm⁻¹ and effective area of 5.39710 × 10⁻⁸ m². Among the principal air pollutants that can irritate and make respiration tough is SO₂ and prolonged exposure could be a factor in permanent breathing problems. It also contributes to the development of acid rain, which damages aquatic environments. Therefore, it becomes necessary to detect this harmful gas and that can be effectively done with the proposed PCF sensor. The suggested PCF sensor can be crucial to minimise the air pollution rate. It will play a vital role for the improvement of human health safety from this deadly element.

Silicone rubber (SiR) is commonly used in reinforced insulation parts for high-voltage direct current (HVDC) cable accessories due to its excellent insulation, elasticity, and high-temperature resistance. HVDC cable accessories always suffer from the local electric field concentration due to the electrical conductivity mismatch between reinforced insulation and main insulation, which can ultimately lead to electric breakdown. The non-linear conductive composites based on SiR have the ability to adaptively adjust the distribution of the electric field in cable accessories. This is expected to solve the problem of localised electric field concentration. The zinc oxide (ZnO) and glycidyl methacrylate (GMA) are used as fillers and grafted modifier respectively to improve the non-linear electrical conductivity of ZnO/SiR-GMA composites. The results indicate that grafting GMA can increase electrical conductivity of SiR, while doping ZnO filler enables SiR to have non-linear conductivity characteristics. The combination of doping and grafting modification of the composites achieves excellent non-linear conductive properties at lower ZnO filler content. Additionally, the mechanical properties of the modified composites are enhanced. The simulation results indicate that ZnO/SiR-GMA is the most effective material for homogenising the electric field when used as reinforced insulation for cable intermediate joints.

A key factor in ensuring the efficient and safe operation of power transformers is the early and accurate diagnosis of incipient faults. Among the tools available to achieve this goal, dissolved gas analysis (DGA) is widely used by power transformers' maintenance professionals. It is a preventive maintenance tool, used for condition monitoring, fault diagnosis and unplanned outage prevention. With the development of artificial intelligence (AI), many intelligent-based methods using AI tools have been proposed in the literature for DGA data interpretation. Although these methods achieve high diagnostic accuracies and improve DGA efficiency, they are generally complicated and the research documented in these publications is difficult to replicate. Traditional DGA-based methods are simple, easy to understand and implement, and widely used by power transformers' maintenance professionals. Many methods proposed in recent years overcome the limitations of the pioneer methods and are increasingly effective. The authors present a detailed and comprehensive literature review of the traditional DGA-based methods for mineral oil-immersed power transformer faults diagnosis. This review also addresses ways to improve the efficiency of the available traditional methods. Some pitfalls that need to be taken into account to improve the efficiency of the DGA-based diagnostic methods are also presented.