Early detection of power transformer fault is important because it can reduce the maintenance cost of the transformer and it can ensure continuous electricity supply in power systems. Dissolved Gas Analysis (DGA) technique is commonly used to identify oil-filled power transformer fault type but utilisation of artificial intelligence method with optimisation methods has shown convincing results. In this work, a hybrid support vector machine (SVM) with modified evolutionary particle swarm optimisation (EPSO) algorithm was proposed to determine the transformer fault type. The superiority of the modified PSO technique with SVM was evaluated by comparing the results with the actual fault diagnosis, unoptimised SVM and previous reported works. Data reduction was also applied using stepwise regression prior to the training process of SVM to reduce the training time. It was found that the proposed hybrid SVM-Modified EPSO (MEPSO)-Time Varying Acceleration Coefficient (TVAC) technique results in the highest correct identification percentage of faults in a power transformer compared to other PSO algorithms. Thus, the proposed technique can be one of the potential solutions to identify the transformer fault type based on DGA data on site.
It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA) has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN) and particle swarm optimisation (PSO) techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works.
This study provides a thorough investigation of partial discharge (PD) activities in nanofluid insulation material consisting of different types of nanoparticles, which are conductive and semiconductive when subjected to high voltage stress is presented. Nanofluids have become a topic of interest because they can be an alternative to liquid insulation in electrical apparatus due to their promising dielectric strength and cooling ability. However, during in-service operation, PDs can occur between conductors in the insulation system. Therefore, this study presents the behavior of PDs within nanofluid dielectric materials consisting of conductive and semiconductive nanoparticles. The results show that there is an improvement in the PD resistance and a reduction in the tan delta of nanofluids at power frequency after the incorporation of conductive or semiconductive nanoparticles in the nanofluid oil. However, the most suitable concentration of conductive and semiconductive nanoparticles in the base fluid was found to be, respectively, 0.01 g/L and 1.0 g/L at PD inception and PD steady-state conditions. The clustering of nanoparticles in a nanofluid suspension due to PD activities is also discussed in this study.
Cable joint insulation breakdown may cause a huge loss to power companies. Therefore, it is vital to diagnose the insulation quality to detect early signs of insulation failure. It is well known that there is a correlation between Partial discharge (PD) and the insulation quality. Although many works have been done on PD pattern recognition, it is usually performed in a noise free environment. Also, works on PD pattern recognition in actual cable joint are less likely to be found in literature. Therefore, in this work, classifications of actual cable joint defect types from partial discharge data contaminated by noise were performed. Five cross-linked polyethylene (XLPE) cable joints with artificially created defects were prepared based on the defects commonly encountered on site. Three different types of input feature were extracted from the PD pattern under artificially created noisy environment. These include statistical features, fractal features and principal component analysis (PCA) features. These input features were used to train the classifiers to classify each PD defect types. Classifications were performed using three different artificial intelligence classifiers, which include Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM). It was found that the classification accuracy decreases with higher noise level but PCA features used in SVM and ANN showed the strongest tolerance against noise contamination.
Cellulose with ample hydroxyl groups is considered as a promising supportive biopolymer for fabricating cellulose supported promising magnetic sorbents (CMS) for magnetic solid-phase extraction (MSPE). The easy recovery via external magnetic field, and recyclability of CMS, associated with different types and surface modifications of cellulose has made them a promising sorbent in the field of solid-phase extraction. CMS based sorbent can offer improved adsorption and absorption capabilities due to its high specific surface area, porous structure, and magnetic attraction feature. This review mainly focuses on the fabrication strategies of CMS using magnetic nanoparticles (MNPs) and various forms of cellulose as a heterogeneous and homogeneous solution either in alkaline mediated urea or Ionic liquids (ILs). Moreover, CMS will be elaborated based on their structures, synthesis, physical performance, and chemical attraction of MNPs and their MSPE in details. The advantages, challenges, and prospects of CMS in future applications are also presented.
New and emerging demand for polyurethane (PU) continues to rise over the years. The harmful isocyanate binding agents and their integrated PU products are at the height of environmental concerns, in particular PU (macro and micro) pollution and their degradation problems. Non-isocyanate poly(hydroxy urethane)s (NIPUs) are sustainable and green alternatives to conventional PUs. Since the introduction of NIPU in 1957, the market value of NIPU and its hybridized materials has increased exponentially in 2019 and is expected to continue to rise in the coming years. The secondary hydroxyl groups of these NIPU's urethane moiety have revolutionized them by allowing for adequate pre/post functionalization. This minireview highlights different strategies and advances in pre/post-functionalization used in biobased NIPU. We have performed a comprehensive evaluation of the development of new ideas in this field to achieve more efficient synthetic biobased hybridized NIPU processes through selective and kinetic understanding.
Hybrid bionanocomposite coating systems (HBCSs) are green polymer materials consisting of an interface between a coating matrix and nanoparticles. The coating matrix was prepared by using a nonisocyanate poly(hydroxyl urethane) (NIPHU) prepolymer crosslinked via 1,3-diaminopropane and epoxidized Jatropha curcas oil. TEMPO-oxidized cellulose nanoparticles (TARC) were prepared from microcrystalline cellulose, and (3-aminopropyl)trimethoxysilane (APTMS)-coated ZnO nanoparticles (APTMS-ZnO) and their suspensions were synthesized separately. The suspensions at different weight ratios were incorporated into the coating matrix to prepare a series of HBCSs. FT-IR, 1H-NMR, 13C-NMR, XRD, SEM, and TEM were used to confirm the chemical structures, morphology, and elements of the coating matrix, nanomaterials, and HBCSs. The thermomechanical properties of the HBCSs were investigated by TGA-DTG and pencil hardness analyses. The UV and IR absorption spectra of the HBCSs were obtained using UV-vis spectroscopy and FTIR spectroscopy, respectively. The HBCSs exhibited good thermal stability at about 200 °C. The degradation temperature at 5% mass loss of all samples was over around 280 °C. The HBCSs exhibited excellent UV block and IR active properties with a stoichiometric ratio of the NIPHU prepolymer and EJCO of 1:1 (wt/wt) containing 5 wt % TARC and 15 wt % APTMS-ZnO nanoparticles. It was observed that the sample with 5 wt % TARC and 15 wt % APTMS-ZnO (HBCS-2) exhibited a uniform crosslinking and reinforcement network with a T onset of 282 °C. This sample has successfully achieved good coating hardness and excellent UV and IR absorption.
In power system networks, automatic fault diagnosis techniques of switchgears with high accuracy and less time consuming are important. In this work, classification of abnormal location in switchgears is proposed using hybrid gravitational search algorithm (GSA)-artificial intelligence (AI) techniques. The measurement data were obtained from ultrasound, transient earth voltage, temperature and sound sensors. The AI classifiers used include artificial neural network (ANN) and support vector machine (SVM). The performance of both classifiers was optimized by an optimization technique, GSA. The advantages of GSA classification on AI in classifying the abnormal location in switchgears are easy implementation, fast convergence and low computational cost. For performance comparison, several well-known metaheuristic techniques were also applied on the AI classifiers. From the comparison between ANN and SVM without optimization by GSA, SVM yields 2% higher accuracy than ANN. However, ANN yields slightly higher accuracy than SVM after combining with GSA, which is in the range of 97%-99% compared to 95%-97% for SVM. On the other hand, GSA-SVM converges faster than GSA-ANN. Overall, it was found that combination of both AI classifiers with GSA yields better results than several well-known metaheuristic techniques.
Nanocellulose reinforced chitosan hydrogel was synthesized using chemical crosslinking method for the delivery of curcumin which is a poorly water-soluble drug. Curcumin extracted from the dried rhizomes of Curcuma longa was incorporated to the hydrogel via in situ loading method. A nonionic surfactant (Tween 20) was incorporated into the hydrogel to improve the solubility of curcumin. After the gas foaming process, hydrogel showed large interconnected pore structures. The release studies in gastric medium showed that the cumulative release of curcumin increased from 0.21% ± 0.02% to 54.85% ± 0.77% with the increasing of Tween 20 concentration from 0% to 30% (w/v) after 7.5 h. However, the entrapment efficiency percentage decreased with the addition of Tween 20. The gas foamed hydrogel showed higher initial burst release within the first 120 min compared to hydrogel formed at atmospheric condition. The solubility of curcumin would increase to 3.014 ± 0.041 mg/mL when the Tween 20 concentration increased to 3.2% (w/v) in simulated gastric medium. UV-visible spectra revealed that the drug retained its chemical activity after in vitro release. From these findings, it is believed that the nonionic surfactant incorporated chitosan/nanocellulose hydrogel can provide a platform to overcome current problems associated with curcumin delivery.
Cellulose and its forms are widely used in biomedical applications due to their biocompatibility, biodegradability and lack of cytotoxicity. It provides ample opportunities for the functionalization of supported magnetic nanohybrids (CSMNs). Because of the abundance of surface hydroxyl groups, they are surface tunable in either homogeneous or heterogeneous solvents and thus act as a substrate or template for the CSMNs' development. The present review emphasizes on the synthesis of various CSMNs, their physicomagnetic properties, and potential applications such as stimuli-responsive drug delivery systems, MRI, enzyme encapsulation, nucleic acid extraction, wound healing and tissue engineering. The impact of CSMNs on cytotoxicity, magnetic hyperthermia, and folate-conjugates is highlighted in particular, based on their structures, cell viability, and stability. Finally, the review also discussed the challenges and prospects of CSMNs' development. This review is expected to provide CSMNs' development roadmap in the context of 21st-century demands for biomedical therapeutics.