Design and optimization of a small-scale horizontal axis wind turbine blade for energy harvesting at low wind profile areas

Wind turbine blades perform the most important function in the wind energy conversion process. It plays the most vital role of absorbing the kinetic energy of the wind, and converting it to mechanical energy before it is transformed into electrical energy by generators. In this work, National Advisory Committee for Aeronautics (NACA) 4412 and SG6043 airfoils were selected to design a small horizontal axis variable speed wind turbine blade for harvesting efficient energy from low wind speed areas. Due to the low wind profile of the targeted area, a blade of one-meter radius was considered in this study. To attain the set objectives of fast starting time and generate more torque and power at low wind speeds, optimization was carryout by varying Reynolds numbers (Re) on tip speed ratios (TSR) values of 4, 5, and 6. The blade element momentum (BEM) method was developed in MATLAB programming code to iteratively find the best twist and chord distributions along the one-meter blade length for each Re and tip speed ratio (TSR) value. To further enhance the blade performance, the twist and chord distributions were transferred to Q-blade software, where simulations of the power coefficients (Cp) were performed and further optimized by varying the angles of attack. The highest power coefficients values of 0.42, 0.43, and 0.44 were recorded with NACA 4412 rotor blades, and 0.43, 0.44, and 0.45 with SG6043 rotor blades. At the Re of 3.0 × 105, the blades were able to harvest maximum power of 144.73 watts (W), 159.69 W, and 201.04 W with the NACA 4412 and 213.15 W, 226.44 W, 245.09 W with the SG6043 at the TSR of 4, 5, and 6 respectively. The lowest cut-in speed of 1.80 m/s and 1.70 m/s were achieved with NACA 4412 and SG6043 airfoils at TSR 4. At a low wind speed of 4 m/s, the blades were able to harness an efficient power of 79.3. W and 80.10 W with both rotor blades at the TSR 4 and 6 accordingly

A brief review on ancillary services from advanced metering infrastructure (ASAMI) for distributed renewable energy network

Advanced metering infrastructure (AMI) is an integrated system of smart meters, communications networks, and data management systems that enable the secure, effective, and dependable distribution of power while also delivering enhanced capabilities to energy consumers. The system also can measure power usage, connect, and disconnect service, detect tampering, identify and isolate outages, and monitor voltage automatically and remotely, which were previously unavailable or required user intervention. This article focuses on AMI and effectively integrating renewable energy sources (RES). However, the study also recommends smart metering for renewables such as solar photovoltaic (PV), hydropower, anaerobic digestion (ad) metering, and renewable energy storage, in which AIM thoroughly supervises the energy utilized by users' appliances. With the prediction of new ancillary services connected with contestability, related regulation, the sufficiency of consumer protection, and safety issues, the magnitude of renewable energy sources in the AMI is an almost unprecedented problem for consumers. The present energy management problems include reducing the power supply-demand gap and boosting power supply dependability. Implementing AMI with distributed renewable energy resources might be a viable strategy for lowering power consumption, improving power supply management, and maximizing management resource use

Enhancement of hydrogen storage performance in cost effective novel g–C3N4–MoS2–Ni(OH)2 ternary nanocomposite fabricated via hydrothermal method

Energy from hydrogen has been looked upon with great favours to encounter the shortage of fossil fuels in energy generation. Safety issues and storage concerns of hydrogen has been a major drawback in this regard. Here, a novel material g–C3N4–MoS2–Ni(OH)2 is crafted to achieve promisingly sufficient storage capacity for hydrogen. Hydrothermal route is optimized in a best possible way to achieve flower like structure of MoS2. It is then blended with fine sheets of Ni(OH)2 and as synthesized g-C3N4 to develop the promising nanocomposite g–C3N4–MoS2–Ni(OH)2. Morphological investigation using TEM and SEM analyses revealed flower-like structure near fine sheets of Ni(OH)2 and g-C3N4. Fruitfully, the modified surface of the nanocomposite resulted in an enhanced hydrogen storage capability. The hydrogen sorption experiments were carried out at 150 °C for 15 and 30 min intervals under 10 bar hydrogen pressure, and the hydrogen desorption process was carried out from room temperature (RT) to 200 °C with a ramping rate of 15 °C min−1 in an argon medium with a flow rate of 100 mL min−1. During non-isothermal H2 desorption, S150 composite exhibits better hydrogen storage capacity of 2.79 and 3.21 wt% under hydrogenation intervals of 15 and 30 min respectively. Furthermore, S150 desorbed 3.7 wt% H2 in 20 min at isothermal desorption of 200 °C

Detection of corona faults in switchgear by ssing 1D-CNN, LSTM, and 1D-CNN-LSTM methods

The damaging effects of corona faults have made them a major concern in metal-clad switchgear, requiring extreme caution during operation. Corona faults are also the primary cause of flashovers in medium-voltage metal-clad electrical equipment. The root cause of this issue is an electrical breakdown of the air due to electrical stress and poor air quality within the switchgear. Without proper preventative measures, a flashover can occur, resulting in serious harm to workers and equipment. As a result, detecting corona faults in switchgear and preventing electrical stress buildup in switches is critical. Recent years have seen the successful use of Deep Learning (DL) applications for corona and non-corona detection, owing to their autonomous feature learning capability. This paper systematically analyzes three deep learning techniques, namely 1D-CNN, LSTM, and 1D-CNN-LSTM hybrid models, to identify the most effective model for detecting corona faults. The hybrid 1D-CNN-LSTM model is deemed the best due to its high accuracy in both the time and frequency domains. This model analyzes the sound waves generated in switchgear to detect faults. The study examines model performance in both the time and frequency domains. In the time domain analysis (TDA), 1D-CNN achieved success rates of 98%, 98.4%, and 93.9%, while LSTM obtained success rates of 97.3%, 98.4%, and 92.4%. The most suitable model, the 1D-CNN-LSTM, achieved success rates of 99.3%, 98.4%, and 98.4% in differentiating corona and non-corona cases during training, validation, and testing. In the frequency domain analysis (FDA), 1D-CNN achieved success rates of 100%, 95.8%, and 95.8%, while LSTM obtained success rates of 100%, 100%, and 100%. The 1D-CNN-LSTM model achieved a 100%, 100%, and 100% success rate during training, validation, and testing. Hence, the developed algorithms achieved high performance in identifying corona faults in switchgear, particularly the 1D-CNN-LSTM model due to its accuracy in detecting corona faults in both the time and frequency domains

Construction novel highly active photocatalytic H2 evolution over noble-metal-free trifunctional Cu3P/CdS nanosphere decorated g-C3N4 nanosheet

Hydrogen energy possesses immense potential in developing a green renewable energy system. However, a significant problem still exists in improving the photocatalytic H2 production activity of metal-free graphitic carbon nitride (g-C3N4) based photocatalysts. Here is a novel Cu3P/CdS/g-C3N4 ternary nanocomposite for increasing photocatalytic H2 evolution activity. In this study, systematic characterizations have been carried out using techniques like X-ray diffraction (XRD), scanning electron microscopy (SEM), high resolution transmission electron microscopy (HR-TEM), Raman spectra, UV–Vis diffuse reflectance spectroscopy, X-ray photoelectron spectroscopy (XPS), surface area analysis (BET), electrochemical impedance (EIS), and transient photocurrent response measurements. Surprisingly, the improved 3CP/Cd-6.25CN photocatalyst displays a high H2 evolution rate of 125721 μmol h−1 g−1. The value obtained exceeds pristine g-C3N4 and Cu3P/CdS by 339.8 and 7.6 times, respectively. This could be the maximum rate of hydrogen generation for a g–C3N4–based ternary nanocomposite ever seen when exposed to whole solar spectrum and visible light (λ > 420 nm). This research provides fresh perspectives on the rational manufacture of metal-free g-C3N4 based photocatalysts that will increase the conversion of solar energy. By reusing the used 3CP/Cd/g-C3N4 photocatalyst in five consecutive runs, the stability of the catalyst was investigated, and their individual activity in the H2 production activity was assessed. To comprehend the reaction mechanisms and emphasise the value of synergy between the three components, several comparison systems are built

Developmental changes in voltage-gated calcium channel α(2)δ-subunit expression in the canine dorsal root ganglion

The voltage-gated calcium channel subunit (128 plays a fundamental role in propagation of excitatory signals associated with release of glutamate and neuropeptides substance P (SP) and calcitonin gene-related protein (CGRP). It can be selectively inhibited by gabapentinoids. Hence, investigation of the alpha(2)delta subunit may predict the efficacy of gabapentinoid therapy in neuropathic pain. Since sensory processing underlies significant age-related changes, this study was conducted in order to elucidate the role of the alpha(2)delta subunit in the sensory transmission during canine development. Dorsal root ganglia (DRG) were harvested from four spinal segments of 16 puppies and 10 adult dogs without a history of neurological signs, pain, spinal disease or orthopedic disorders. alpha(2)delta-Subunit expression and coexpression with SP and CGRP was evaluated immunohistochemically regarding the number of immunopositive ganglion cells, staining intensity and subcellular distribution. All tested ganglia were immunopositive for alpha(2)delta. Cell counts and expression levels were significantly lower in pups than in adult dogs (p < 0.05). In the cervical segments of both groups, the number and percentage of innmunopositive neurons was significantly higher than in lumbar DRG (p < 0.05). Multilabeling studies in all tested animals confirmed the coexpression of alpha(2)delta and pain peptides SP and CGRP. This anatomical study for the first time documents the involvement of alpha(2)delta subunits in sensory signal processing in dogs. The proportion of positive neurons and the intracellular expression levels show a net increase from early postnatal life to adulthood. A significant portion of alpha(2)delta-positive cells in the dogs exhibited C- and A delta-phenotypes compatible with nociceptive neurons. The coexpression of alpha(2)delta, SP and CGRP imply that these neurons are involved with peptidergic nociception. The cervicolumbar gradient of alpha(2)delta expression in adults reflects functional differences in between forelimbs and hind limbs. These data will facilitate translational studies on neuropathic pain states in this species such as common canine nerve entrapment syndromes. Copyright (C) 2012 S. Karger AG, Base

Failure to act or impossible task? The pursuit of climate justice and energy security through litigation

Frustrated with the lack of a meaningful global response to the inequities wrought by climate change, ordinary citizens and advocacy groups are increasingly turning to legal recourse. They are petitioning the courts to defend their rights, to hold governments and fossil fuel corporations accountable for failure to protect their health, safety and welfare interests from foreseeable natural disasters, and to provide energy security. This chapter discusses the international movement using litigation to redress injustices caused by governments and large companies putting economic benefits in conflict with social, environmental and moral obligations to humanity. As courts become the arena where people look to rectify breaches of climate justice, nations and businesses may ultimately bear a duty of care to reduce carbon emissions with compensation and renewable power sources