Investigations of the performance of 3D printed micro wind turbine composed of PLA material.

Wind energy conversion systems (WECS) have gained increasing attention in recent years as promising renewable energy sources. Despite their potential, a clear research gap exists: the majority of WECS underperform in low wind speed conditions, limiting their applicability in many regions. To address this problem, this study proposes a novel approach by developing a 100 W micro wind turbine using Polylactic Acid (PLA) to generate efficient power in low wind speed conditions. The proposed wind turbine design employs Blade Element Momentum Theory (BEMT), which is commonly used for modeling wind turbine performance. Geometric design, mechanical analysis, and aerodynamic analysis are the fundamental considerations for designing any machine. In this work, the CREO 3.0 three-dimensional modeling software is used to create the geometric design of the proposed work. The airfoil SD7080 is selected due to its superior aerodynamic performance, and mechanical properties such as Young's modulus, density, and Poisson's ratio are attained to evaluate the wind blade's performance. Additionally, ANSYS 15.0 is used to conduct a detailed analysis of the proposed wind turbine, evaluating properties such as equivalent stress, deformation, and equivalent strain. Both simulation (ANSYS 15.0) and experimental setups are used to investigate the proposed wind turbine's performance, and the corresponding results are presented and discussed in this manuscript. The results indicate a significant performance improvement of the proposed wind blade when compared to conventional and ABS wind blades, demonstrating its potential as a more efficient solution for WECS. This proposed wind turbine design overcomes the problems like underprformance in low wind speed conditions and the wind turbine efficiency in all regions.

Optimizing the allocation of renewable DGs, DSTATCOM, and BESS to mitigate the impact of electric vehicle charging stations on radial distribution systems.

The increasing global adoption of Electric Vehicles (EVs) necessitates a greater supply of electricity for charging these cars. The popularity of EVs is also driven by their minimal maintenance requirements, enhanced performance, and eco-friendly nature. However, the expanding usage of EVs poses challenges to the distribution system's efficiency, thereby impacting its reliability. Consequently, ensuring the precise placement of electric vehicle charging stations (EVCS) becomes crucial for maintaining a dependable infrastructure. Solar and wind-based Renewable Distributed Generations (RDGs), Distribution STATic COMPensator (DSTATCOM), and Battery Energy Storage System (BESS) have become an important part of a Radial Distribution System (RDS) for mitigating the impact of EVCS as environmental sensitivity has grown and technology has advanced. Improper placement and sizing of components in can significantly impact the performance of a RDS. This research proposes a unique approach utilizing the Slime Mould Algorithm (SMA) and other optimization algorithms to identify the optimum positioning and sizing of RDG/DSTATCOM/EVCS/BESS within the RDS. The presented approach's efficacy is showcased by employing it on two commonly used IEEE RDSs: specifically, the 33-bus and 69-bus systems. The main objective of this research is to address actual power loss in these systems, subsequently enhancing voltage stability and bus voltage profiles. Findings from the test cases demonstrate that optimizing with the SMA algorithm produces more precise results in mitigating real power loss, enhancing bus voltage levels, and improving overall system stability when compared to existing algorithms.

Thermo-kinetic behaviour of green synthesized nanomaterial enhanced organic phase change material: Model fitting approach.

Metal, carbon and conducting polymer nanoparticles are blended with organic phase change materials (PCMs) to enhance the thermal conductivity, heat storage ability, thermal stability and optical property. However, the existing nanoparticle are expensive and need to be handle with high caution during operation as well during disposal owing to its toxicity. Subsequently handling of solid waste and the disposal of organic PCM after longevity usage are of utmost concern and are less exposed. Henceforth, the current research presents a new dimension of exploration by green synthesized nanoparticles from a thorny shrub of an invasive weed named Prosopis Juliflora (PJ) which is a agro based solid waste. Subsequently, the research is indented to decide the concentration of green synthesized nanoparticle for effective heat transfer rate of organic PCM (Tm = 35-40 °C & Hm = 145 J/g). Furthermore, an in-depth understanding on the kinetic and thermodynamic profile of degradation mechanism involved in disposal of PCM after usage via Coats and Redfern technique is exhibited. Engaging a two-step method, we fuse the green synthesized nanomaterial with PCM to obtain nanocomposite PCM. On experimental evaluation, thermal conductivity of the developed nanocomposite (PCM + PJ) increases by 63.8% (0.282 W/m⋅K to 0.462 W/m⋅K) with 0.8 wt% green synthesized nanomaterial owing to the uniform distribution of nanoparticle within PCM matrix thereby contributing to bridging thermal networks. Subsequently, PCM and PCM + PJ nanocomposites are tested using thermogravimetric analyzer at different heating rates (05 °C/min; 10 °C/min; 15 °C/min & 20 °C/min) to analyze the decomposition kinetic reaction. The kinetic and thermodynamic profile of degradation mechanism involved in disposal of PCM and its nanocomposite of PCM + PJ provides insight on thermal parameters to be considered on large scale operation and to understand the complex nature of the chemical reactions. Adopting thirteen different chemical mechanism model under Coats and Redfern method we determine the reaction mechanism; kinetic parameter like activation energy (Ea) & pre-exponential factor (A) and thermodynamic parameter like change in enthalpy (ΔH), change in Gibbs free energy (ΔG) and change in entropy (ΔS). Dispersion of PJ nanomaterial with PCM reduces Ea from 370.82 kJ/mol-1 to 342.54 kJ/mol-1 (7.7% reduction), as the developed nanomaterial is enriched in carbon element and exhibits a catalytic effect for breakdown reaction. Corresponding, value of ΔG for PCM and PCM + PJ sample within heating rates of 05-20 °C/min varies between 168.95 and 41.611 kJ/mol-1. The current research will unbolt new works with focus on exploring the pyrolysis behaviour of phase change materials and its nanocomposite used for energy storage applications. This work also provides insights on the disposal of PCM which is an organic solid waste. The thermo-kinetic profile will help to investigate and predict the optimum heating rate and temperature range for conversion of micro-scale pyrolysis to commercial scale process.

Solar photovoltaic converter controller using opposition-based reinforcement learning with butterfly optimization algorithm under partial shading conditions.

The major use of a power point tracking controller is to maximize or enhance the power generation in photovoltaic systems. These systems are steered to operate and maximize the power point. Under partial shading conditions, the power points may vary or fluctuate between global maxima and local maxima. This fluctuation leads to a decrease in energy or energy loss. Hence, to address the fluctuation issue and its variations, a new hybridized maximum power point tracking technique based on an opposition-based reinforcement learning approach with a butterfly optimization algorithm has been proposed. The proposed methodology has been tested on 6S, 3S2P and 2S3P photo-voltaic configurations under different shading conditions. Performance comparison and analysis have been presented with a butterfly optimization algorithm, grey wolf optimization algorithm, whale optimization algorithm, and particle swarm optimization-based maximum power point tracking techniques. Experimental results show that the proposed method performs better adaptation than the conventional approaches and mitigates the load variation convergence and frequent exploration and exploitation patterns.

Power enhanced solar PV array configuration based on calcudoku puzzle pattern for partial shaded PV system.

The power output of solar photovoltaic systems can be affected by environmental factors, such as partial shading. This can lead to a decrease in the power conversion rate of the system. Although existing solutions for this issue are cost-effective and efficient, new solutions could further improve the system's performance by increasing consistency, power generation, and reducing mismatch loss and costs. To address this, a new method for configuring PV arrays was proposed using the calcudoku puzzle pattern. The performance of this new array configuration was evaluated in MATLAB/Simulink® for a 9 × 9 PV array and compared to conventional methods like Series-parallel, Total Cross Tied (TCT), and Sudoku array configurations. The performance was evaluated under eight different shading patterns based on power conversion rate and mismatch losses between the PV rows. The proposed array configuration resulted in 3.9%-13.3% of mismatch losses across the different shading patterns, while other configurations had a minimum of 13.8% to a maximum of 51.9% of mismatch losses. This reduction in mismatch losses directly improved the power conversion rate of the PV array.

Performance Analysis of PLA Material Based Micro-Turbines for Low Wind Speed Applications.

Various studies have been conducted in recent years to find solutions to the issues in wind energy conversion systems. A 100W horizontal axis micro wind turbine is built for low wind speed applications in this work. The Blade Element Momentum theory approach was used to design the 100W micro wind turbine blade. The wind turbine blade 3D model was created using the CREO CAD 3.0 software. Based on the aerodynamic studies, the airfoil S9000 is chosen among others for generating high power at low wind speed. The density, Young's modulus, and the Poisson ratio of the proposed wind turbine blade model with acrylonitrile butadiene styrene (ABS) and polylactic acid (PLA) materials were compared. ABS and PLA materials were investigated using a 0.33 mm layer of infill ranging from 10% to 100%. PLA and ABS output values were compared in terms of deformation, equivalent stress, and equivalent strain. PLA materials, on the other hand, have less deformation and greater structural properties than ABS materials. The wind blade structural analysis was performed in ANSYS 15 software, and the details of experimental and simulated results are presented in this paper.

A nanosecond pulsed laser-ablated MWCNT-Au heterostructure: an innovative ultra-sensitive electrochemical sensing prototype for the identification of glutathione.

Here, a scheme that aptly describes the reduction of gold nanoparticles' crystalline size on the surface of MWCNTs in an aqueous phase to generate a LAMWCNT-Au heterostructure, employing an Nd:YAG laser (energy = 505 mJ and λ = 1064 nm) is developed. Such a LAMWCNT-Au heterostructure results in the development of an easy electrochemical procedure based on voltammetry analysis for ultra-sensitive glutathione sensing. High-resolution transmission electron microscopy, UV-visible spectroscopy, Raman spectroscopy, and X-ray photoelectron spectroscopy were used to examine the composition and morphology of laser-ablated adhesion of AuNPs over the MWCNT heterostructure. With a wide dynamic range of 0.1-9 µmol L-1, the LAMWCNT-Au heterostructure can detect glutathione with a high sensitivity of 0.1186 µA (µmol L-1)-1 at the low limit of detection (LLOD; 0.93 µmol L-1). It improves electron transfer and promotes redox reactions by increasing the conductivity and surface area. The findings show that the fabricated LAMWCNT-Au/GCE is an effortless and potent biosensing prototype for the identification of glutathione (GSH) at a negative potential in a neutral medium. The substantial synergistic surface impact produced by the introduction of AuNPs over MWCNTs exhibits exceptional electrocatalytic activity in comparison with individual MWCNT and AuNP. Moreover, the LAMWCNT-Au heterostructure has excellent selectivity, long-term stability, and reproducibility, and it can easily separate target molecules that were identified using various voltammetric analyses.

False Data Injection Detection for Phasor Measurement Units.

Cyber-threats are becoming a big concern due to the potential severe consequences of such threats is false data injection (FDI) attacks where the measures data is manipulated such that the detection is unfeasible using traditional approaches. This work focuses on detecting FDIs for phasor measurement units where compromising one unit is sufficient for launching such attacks. In the proposed approach, moving averages and correlation are used along with machine learning algorithms to detect such attacks. The proposed approach is tested and validated using the IEEE 14-bus and the IEEE 30-bus test systems. The proposed performance was sufficient for detecting the location and attack instances under different scenarios and circumstances.

Ultrasound-aided synthesis of gold-loaded boron-doped graphene quantum dots interface towards simultaneous electrochemical determination of guanine and adenine biomolecules.

To acquire substantial electrochemical signals of guanine-GUA and adenine-ADE present in deoxyribonucleic acid-DNA, it is critical to investigate innovative electrode materials and their interfaces. In this study, gold-loaded boron-doped graphene quantum dots (Au@B-GQDs) interface was prepared via ultrasound-aided reduction method for monitoring GUA and ADE electrochemically. Transmission electron microscopy-TEM, Ultraviolet-Visible spectroscopy-UV-Vis, Raman spectroscopy, X-ray photoelectron spectroscopy-XPS, cyclic voltammetry-CV, and differential pulse voltammetry-DPV were used to examine the microstructure of the fabricated interfaceand demonstrate its electrochemical characteristics. The sensor was constructed by depositing the as-prepared Au@B-GQDs as a thin layer on a glassy carbon-GC electrode by the drop-casting method and carried out the electrochemical studies. The resulting sensor exhibited a good response with a wide linear range (GUA = 0.5-20 µM, ADE = 0.1-20 µM), a low detection limit-LOD (GUA = 1.71 µM, ADE = 1.84 µM), excellent sensitivity (GUA = 0.0820 µAµM-1, ADE = 0.1561 µAµM-1) and selectivity with common interferents results from biological matrixes. Furthermore, it seems to have prominentselectivity, reproducibility, repeatability, and long-lastingstability. The results demonstrate that the fabricated Au@B-GQDs/GC electrode is a simple and effective sensing platform for detecting GUA and ADE in neutral media at low potential as it exhibited prominent synergistic impact and outstanding electrocatalytic activity corresponding to individual AuNPs and B-GQDs modified electrodes.

Highly sensitive and selective detection of glutathione using ultrasonic aided synthesis of graphene quantum dots embedded over amine-functionalized silica nanoparticles.

Glutathione (GSH) is the most abundant antioxidant in the majority of cells and tissues; and its use as a biomarker has been known for decades. In this study, a facile electrochemical method was developed for glutathione sensing using voltammetry and amperometry analyses. In this study, a novel glassy carbon electrode composed of graphene quantum dots (GQDs) embedded on amine-functionalized silica nanoparticles (SiNPs) was synthesized. GQDs embedded on amine-functionalized SiNPs were physical-chemically characterized by different techniques that included high resolution-transmission electron microscopy (HR-TEM), X-ray diffraction spectroscopy (XRD), UV-visible spectroscopy, Fourier-transform infrared spectroscopy(FTIR), and Raman spectroscopy. The newly developed electrode exhibits a good response to glutathione with a wide linear range (0.5-7 µM) and a low detection limit (0.5 µM) with high sensitivity(2.64 µA µM-1). The fabricated GQDs-SiNPs/GC electrode shows highly attractive electrocatalytic activity towards glutathione detection in the neutral media at low potential due to a synergistic surface effect caused by the incorporation of GQDs over SiNPs. It leads to higher surface area and conductivity, improving electron transfer and promoting redox reactions. Besides, it provides outstanding selectivity, reproducibility, long-term stability, and can be used in the presence of interferences typically found in real sample analysis.

LaCoxFe1-XO3 (0≤x≤1) spherical nanostructures prepared via ultrasonic approach as photocatalysts.

To harvest the photon energy, a sequenceof perovskite-type oxides of LaCoxFe1-xO3 (0 ≤x≤1) nanostructures with distinct 'Cobalt' doping at the position of B-site are successfully prepared via a simple ultrasonic approach as photocatalyst. The crystallinity, phase identification, microstructure, and morphology of perovskite nanocomposites were analyzed to better understand their physicochemical properties. The catalytic efficiency was assessedusing Congo Red (CR) dye by visible light irradiation for 30 min. Applying terephthalic acid as a probe molecule, the formation of hydroxyl radicals during the processes was investigated. The photocatalytic efficacy was measured by varying different Co/Fe stoichiometric molar ratios and noticed the order of sequence is 0.2 > 0.6 > 0.4 > 0.8 > 0.5 > 0 > 1 after 30 min of reaction time. Finally using LaCo0.2Fe0.8O3 nanostructures, cycling studies (n = 3) were performed to determine its photostability and reusability. The photocatalytic methodology proposed in this study was discussed extensively.

A Novel Technique to Detect False Data Injection Attacks on Phasor Measurement Units.

The power industry is in the process of grid modernization with the introduction of phasor measurement units (PMUs), advanced metering infrastructure (AMI), and other technologies. Although these technologies enable more reliable and efficient operation, the risk of cyber threats has increased, as evidenced by the recent blackouts in Ukraine and New York. One of these threats is false data injection attacks (FDIAs). Most of the FDIA literature focuses on the vulnerability of DC estimators and AC estimators to such attacks. This paper investigates FDIAs for PMU-based state estimation, where the PMUs are comparable. Several states can be manipulated by compromising one PMU through the channels of that PMU. A Phase Locking Value (PLV) technique was developed to detect FDIAs. The proposed approach is tested on the IEEE 14-bus and the IEEE 30-bus test systems under different scenarios using a Monte Carlo simulation where the PLV demonstrated an efficient performance.