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Hydrogenated-Nitrile-Butadiene-Rubber (HNBR) is known for its good physical strength. It is a widely used rubber in electrical insulation and other high performance applications. Performance of HNBR is affected in high temperature and an aggressive fluid environment. Adding silica to HNBR may overcome this problem. In order to investigate the effect of fluids and temperature on HNBR/silica composites we prepared multiple composites of HNBR with 8.3, 16.7, 33.4, 50 and 66.7 phr of silica (SiO2) by two roll compounding method. Swelling index and thermo gravimetric analyses were performed. Calculations of swelling indexes were performed at different time periods with ethanol, toluene and water. For thermo gravimetric analysis (TGA), thermo grams of samples were obtained and % char yields at 550 °C were analyzed for all samples. Improvements with the addition of silica were recorded up to a great extent in both analyses. Swelling index decreased with the addition of silica as compared to neat HNBR and reached an optimum position with 50 phr silica loading in ethanol, 8.3 phr in water and 66.7 phr in toluene. Moreover, the HNBR composite with 66.7 phr of silica was found to be the highest thermally stable sample and lost less than 60% of weight at 550 °C in comparison to neat HNBR in which 80% of weight loss occurred at 550 °C.

For improved performance of epoxy, its composites were studied for high-voltage insulation. Epoxy composites may offer several advantages over neat epoxy and ceramic materials. We fabricated nano- and microepoxy/silica composites with 5 wt% nanosilica and 20 wt% microsilica, respectively. The composites and neat epoxy were studied for thermal, mechanical, and electrical properties. A thermogravimetric analyzer was used for analyzing wt% loss with temperature. Tensile and hardness tests were performed according to DIN 53504/ASTM D412 and DIN 53505/ASTM D2240 standards, respectively. Electrical properties such as dielectric strength and resistivity were tested according to IEC-60243-1 and ASTM D257/IEC 60093 standards, respectively. Neat epoxy, microcomposite, and nanocomposite showed 50% weight loss at 392°C, 410°C, and 421°C, respectively. At 550°C, nanocomposite remained at 20% of its initial weight whereas neat epoxy and microcomposite remained at 10% of their initial weights. Microcomposite and nanocomposite showed tensile strengths of 65.7 Mpa and 69.3 Mpa, respectively. Enhancements of 8% and 19% in dielectric strength were recorded for microcomposites and nanocomposites, respectively. Nanosilica greatly improved surface and volume resistivity whereas microsilica showed negligible effect on resistivity.

Silicone rubber (SiR) is extensively used in outdoor insulation and other applications. However, like other polymers, SiR also degrades and lessens its performance in the exposition of environmental stresses. Nanofillers improve the aging behavior of polymeric insulating materials. To investigate the effect of nanosilica on the aging behavior of SiR, we fabricated nanocomposites of SiR with 2.5 wt.% nanosilica (SNC-2.5) and 5 wt.% nanosilica (SNC-5) by mechanical mixing and ultrasonication method. The prepared samples were subjected to uniform ultraviolet (UV) radiation, heat, humidity, salt fog, and acid rain along with neat SiR in cyclic manner at 2.5 kV for 5000 h in a specially fabricated weathering chamber. Neat SiR and both nanocomposites showed gradual decrease in transparency. Random loss and recovery in hydrophobicity and increase and decrease in leakage current (LC) were recorded for all samples, in which SNC-5 showed the best hydrophobic behavior and least LC. For SNC-5, Fourier transform infrared (FTIR) results showed negligible reduction in absorption peaks at important wave-numbers and increase and intactness were observed at absorption peaks related to the hydrophobic methyl group. Scanning electron microscopy (SEM) results also concurred with FTIR, LC measurements, and hydrophobicity analysis.

In this paper, an approach is presented for the demand-side management of residential loads in the urban areas of Pakistan using a battery storage system at the feeder level. The proposed storage system will be installed by a private distributor to supply affordable electricity during peak hours. The experimental data used to carry out this research work are the Pakistan Residential Energy Consumption (PRECON) data set. The households of the data set are categorized based on electric power usage through K-means clustering. The clusters are expanded for feeder synthesis to represent small-scale, medium-scale, and large-scale consumption. This expansion is performed through uniform distribution in a Monte Carlo simulation. The techno-economic analysis for the installation of a battery storage system is carried out for each feeder using SAM. The results of the research project elucidated that the load factors of the feeders representing small-scale, medium-scale, and large-scale consumption improved by 1%, 6%, and 7% by using the optimally sized batteries of 50 kW (670 kWh), 90 kW (1207 kWh), and 100 kW (1360 kWh), respectively. The distributor profit and the consumer utility bill savings ranged from US$12 k to US$25 k. The results proved the validity of the used approach to simultaneously reduce the consumer bill, maximize the distributor profit, and improve the feeder load factor. The novelty of this work lies in the location and in the way the system modeling has been performed with limited data. [ABSTRACT FROM AUTHOR]

Due to rapid rise in climate change, world is seeing a major technology shift from conventional vehicles to electric vehicles (EVs). Battery's state of charge (SOC) is a critical parameter in EVs whose accuracy affects not only the battery management system but can also influence the driving range estimation for an EV. In this paper, a simulation-based hybrid technique is presented, that combines both adaptive extended Kalman filter (AEKF) and artificial neural network (ANN) for SOC estimation of lithium-ion battery. The proposed hybrid technique is validated using five different EV driving cycles, that is, LA92, US06, urban dynamometer driving schedule (UDDS), highway fuel economy test (HWFET) and a mixed driving cycle at four different temperature values, that is, 0°C, 10°C, 25°C and 40°C. To facilitate the potential EV users, a real-time SOC estimator is also implemented. The real-time SOC estimated through open circuit voltage method and Coulomb counting method is further used for reservation of a charging slot in a nearby charging station. Moreover, to monitor the charging status of a potential EV from a remote location, a cloud-based Internet of things (IoT) platform, that is, ThingSpeak, is used. This results into the reduction of waiting time for an EV user at a charging station. In addition to this, a comparative analysis is carried out between proposed hybrid method and existing methods. It is demonstrated that our hybrid technique achieves lower root mean square error and higher accuracy as compared to existing methods at different operating conditions. [ABSTRACT FROM AUTHOR]

A series of polyvinyl alcohol (PVA)based composites with well dispersed nano fillers were fabricated and compared in terms of dielectric, mechanical, and optical properties. Specifically, NiO and CuO nano-fillers were utilized in a range of 0.2–0.6 wt% for thin film fabrication by solution deposition method. The characterization of nanocomposites was confirmed through FTIR, FESEM, and XRPD, whereas dielectric and mechanical properties were analyzed with respect to the filler concentrations. The bandgap of PVA/nano-filler composites reduced with an increase in NiO and CuO concentration from 0.2 to 0.6 wt%. The increase in the permittivity of the material was observed for 6 wt% of nano-fillers. The toughness of PVA/nano-filler composites was improved by increasing CuO and NiO concentration and Young's modulus of 30.9 and 27.2 MPa for 0.6 wt% of NiO and CuO-based nanocomposite, respectively, was observed. The addition of nano-fillers showed improved optical, dielectric, and mechanical properties. [ABSTRACT FROM AUTHOR]

Polymers have gained attraction at the industrial level owing to their elastic and lightweight nature, as well as their astonishing mechanical and electrical applications. Their scope is limited due to their organic nature, which eventually leads to the degradation of their properties. The aim of this work was to produce polymer composites with finely dispersed metal oxide nanofillers and carbon nanotubes (CNTs) for the investigation of their charge-storage applications. This work reports the preparation of different polymeric composites with varying concentrations of metal oxide (MO) nanofillers and single-walled carbon nanotubes (SWCNTs). The successful synthesis of nanofillers (i.e., NiO and CuO) was carried out via the sonication and precipitation methods, respectively. After, the smooth and uniform polymeric composite thin films were prepared via the solution-casting methodology. Spectroscopy and diffraction techniques were used for the preliminary characterization. Scanning electron microscopy was used to check the dispersion of carbon nanotubes (CNTs) and MOs in the polymer matrix. The addition of nanofillers and carbon nanotubes (CNTs) tuned the bandgap, reduced the strain, and enhanced the elastic limit of the polymer. The addition of CNT enhanced the mechanical strength of the composite; however, it increased the conductivity, which was tuned by using metal oxides. By increasing the concentration of NiO and CuO from 2% to 6% bandgap of PVA, which is 5–6 eV reduced to 4.41 and 4.34 eV, Young's moduli of up to 59 and 57.7 MPa, respectively, were achieved. Moreover, improved dielectric properties were achieved, which shows that the addition of metal oxide enhances the dielectric behavior of the material. [ABSTRACT FROM AUTHOR]

Epoxy resins have demonstrated remarkable properties with potential for usage as high voltage insulators. However, a loss of these properties has been observed in high temperature and humid environments. In order to enhance the hydrothermal stability of epoxy resins, micro (15% SiO2) and nano (5% SiO2) silica-based composites of epoxy were fabricated and subjected to standard long term and short term accelerated hydrothermal conditions. To analyze the effect of these stresses, the samples were analyzed periodically through Fourier transform infrared spectroscopy (FTIR) for structure analysis; scanning electron microscopy (SEM) for surface analysis of long-term aged samples; and optical microscopy for the surface topography of short-term aged samples. The Swedish Transmission Research Institute (STRI) classification and contact angle measurement techniques were used for hydrophobicity analysis of long-term and short-term aged samples, respectively. After aging in both conditions, the nanocomposite showed better results as compared to the other samples. After 1000 h of aging, it showed HC-5 class of hydrophobicity, whereas EMC and NE degraded to the HC-6. In case of short-term aging, the contact angle decreased to the 64.15° and 75.05° from 104.15° and 114.9° for ENC and EMC, respectively. Also, in terms of structural degradation, ENC showed the highest structural stability after 1000 h of aging with the highest stable peak of aromatic ether at 1300–1500 cm−1. Microscopic observation through scanning electron and optical techniques also revealed superior performance of the nanocomposites. [ABSTRACT FROM AUTHOR]

The intermittency of solar energy resources possesses a serious challenge in balancing the power generation and load demand. To enhance the consistency of the system, it is crucial to forecast solar photovoltaic power. Among numerous techniques, Artificial Neural Network (ANN) is an efficient tool that may help simplify this problem. In this study, all 63 combinations of six input parameters, i.e., temperature, dew point, wind speed, cloud cover, relative humidity, and pressure, were applied one by one to ANN to forecast 24 hours ahead PV generation. The power forecast results were obtained based on weather forecast data of 21 days sampled from the recorded forecasted data of 180 days. To quantify the error between predicted and measured solar PV generation, Root Mean Squared Error (RMSE) was used, and the results of different input combinations were compared on basis of this statistical matrix. The analysis showed that the generation is best predicted on two combinations: the first is comprising of temperature, dew point, relative humidity, and cloud cover, while the second consists of all six parameters. And some of the combinations consisting of three parameters also resulted in RMSEs in close proximity of the least error value. [ABSTRACT FROM AUTHOR]