AbstractTransformer health analysis using Dissolved Gas Analysis is crucial for diagnosing power transformer faults. This paper proposes an innovative approach to diagnose power transformer faults by integrating machine learning algorithms with Ensemble techniques. The method involves fusing reduced dimensional input features through Principal Component Analysis with Ensemble techniques such as Bagging, Decorate, and Boosting. Various machine learning algorithms, including Decision Tree (DT), K‐Nearest Neighbours, Radial Basis Function Network, and Support Vector Machine, are employed in conjunction with Ensemble techniques. The long short‐term memory algorithm was used to create synthetic data to solve the issue of data imbalance. A dataset of 683 samples is used in the study for training, testing, validation, and comparison with current techniques. The results highlight the effectiveness of Ensemble techniques, particularly Boosting, which demonstrates superior performance across all classification algorithms. The Boosting with DT algorithm achieves an impressive accuracy of 98.32%, surpassing alternative methods. In validation, the proposed Boosting Ensemble technique outperforms various approaches, showcasing its diagnostic accuracy and superiority over alternative methods. The research emphasises the model's effectiveness in smoothing input vectors, enhancing harmony with ensemble techniques, and overcoming limitations in prior methods.

AbstractThe detection of human diseases is a major application for biosensors. During this work, a 2D photonic crystal fibre (PCF) biosensor design for dengue virus detection has been suggested. This work presents the decagonal hollow core PCF‐based dengue virus bio‐sensor and quantitatively investigates it over the terahertz regime. The suggested biosensor's performance is assessed using COMSOL Multiphysics, a professional tool that uses the Finite Element Method. The simulation's outcomes show that the suggested sensor performed better than earlier research, with a high sensitivity of 98.79%, 97.96%, 97.71%, 98.58%, 96.99% and 97.47% with more less confinement loss 1.2766 × 10−12 dB/m, 1.6385 × 10−12 dB/m, 2.8015 × 10−13 dB/m, 1.1798 × 10−13 dB/m, 7.0336 × 10−12 dB/m and 0.00 dB/m respectively for infected Haemoglobin (Hgb), Normal Haemoglobin (Hgb), Infected Platelets (Plt), Normal Platelets (Plt), Infected Plasma (Psm) and Normal Plasma (Psm) at 3.0 THz using the ideal geometric configuration. Very soon, its remarkable sensitivity and guiding capabilities will be crucial to dengue virus detection technology.

AbstractThis 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.

AbstractThis 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.

AbstractIn 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.

AbstractJurkat 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.

AbstractToughening 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 εF under tensile fields and a greater strain amplitude |εE| 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 εF 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 |εE| 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.

AbstractThis 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.