Influence of Biosynthesized Nanoparticles Addition and Fibre Content on the Mechanical and Moisture Absorption Behaviour of Natural Fibre Composite

This study looks at how incorporating nanofiller into sisal/flax-fibre-reinforced epoxy-based hybrid composites affects their mechanical and water absorption properties. The green Al2O3 NPs are generated from neem leaves in a proportion of leaf extract to an acceptable aluminium nitrate combination. Both natural fibres were treated with different proportions of NaOH to eliminate moisture absorption. The following parameters were chosen as essential to achieving the objectives mentioned above: (i) 0, 5, 10, and 15% natural fibre concentrations; (ii) 0, 2, 4, and 6% aluminium powder concentrations; and (iii) 0, 1, 3, and 5% NaOH concentrations. Compression moulding was used to create the hybrid nanocomposites and ASTM standards were used for mechanical testing such as tension, bending, and impact. The findings reveal that combining sisal/flax fibre composites with nanofiller improved the mechanical features of the nanocomposite. The sisal and flax fibre hybridised successfully, with 10% fibres and 4% aluminium filler. The water absorption of the hybrids rose as the fibre weight % increased, and during the next 60 h, all of the specimens achieved equilibrium. The failed samples were examined using scanning electron Microscopic (SEM) images better to understand the composite’s failure in the mechanical experimentations. Al2O3 NPs were confirmed through XRD, UV spectroscope and HPLC analysis. According to the HPLC results, the leaf’s overall concentrations of flavonoids (gallocatechin, carnosic acid, and camellia) are determined to be 0.250 mg/g, 0.264 mg/g, and 0.552 mg/g, respectively. The catechin concentration is higher than the phenolic and caffeic acid levels, which could have resulted in a faster rate of reduction among many of the varying configurations, 4 wt.% nano Al2O3 particle, 10 wt.% flax and sisal fibres, as well as 4 h of NaOH with a 5 wt.% concentration, producing the maximum mechanical properties (59.94 MPa tension, 149.52 Mpa bending, and 37.9 KJ/m2 impact resistance). According to the results, it can be concluded that botanical nutrients may be used effectively in the manufacturing of nanomaterials, which might be used in various therapeutic and nanoscale applications

Unified Power Control of Permanent Magnet Synchronous Generator Based Wind Power System with Ancillary Support during Grid Faults

A unified active power control scheme is devised for the grid-integrated permanent magnet synchronous generator-based wind power system (WPS) to follow the Indian electricity grid code requirements. The objective of this paper is to propose control schemes to ensure the continuous integration of WPS into the grid even during a higher percentage of voltage dip. In this context, primarily a constructive reactive power reference is formulated to raise and equalize the point of common coupling (PCC) potential during symmetrical and asymmetrical faults, respectively. A simple active power reference is also proposed to inject a consistent percentage of generated power even during faults without violating system ratings. Eventually, the efficacy of the proposed scheme is demonstrated in terms of PCC voltage enhancement, DC-link potential, grid real, and reactive power oscillation minimization using the PSCAD/ EMTDC software

Transfer Learning Based Fault Detection for Suspension System Using Vibrational Analysis and Radar Plots

The suspension system is of paramount importance in any automobile. Thanks to the suspension system, every journey benefits from pleasant rides, stable driving and precise handling. However, the suspension system is prone to faults that can significantly impact the driving quality of the vehicle. This makes it essential to find and diagnose any faults in the suspension system and rectify them immediately. Numerous techniques have been used to identify and diagnose suspension faults, each with drawbacks. This paper’s proposed suspension fault detection system aims to detect these faults using deep transfer learning techniques instead of the time-consuming and expensive conventional methods. This paper used pre-trained networks such as Alex Net, ResNet-50, Google Net and VGG16 to identify the faults using radar plots of the vibration signals generated by the suspension system in eight cases. The vibration data were acquired using an accelerometer and data acquisition system placed on a test rig for eight different test conditions (seven faulty, one good). The deep learning model with the highest accuracy in identifying and detecting faults among the four models was chosen and adopted to find defects. The results state that VGG16 produced the highest classification accuracy of 96.70%

Deep Learning for Enhanced Fault Diagnosis of Monoblock Centrifugal Pumps: Spectrogram-Based Analysis

Abstract The reliable operation of monoblock centrifugal pumps (MCP) is crucial in various industrial applications. Achieving optimal performance and minimizing costly downtime requires effectively detecting and diagnosing faults in critical pump components. This study proposes an innovative approach that leverages deep transfer learning techniques. An accelerometer was adopted to capture vibration signals emitted by the pump. These signals are then converted into spectrogram images which serve as the input for a sophisticated classification system based on deep learning. This enables the accurate identification and diagnosis of pump faults. To evaluate the effectiveness of the proposed methodology, 15 pre-trained networks including ResNet-50, InceptionV3, GoogLeNet, DenseNet-201, ShuffleNet, VGG-19, MobileNet-v2, InceptionResNetV2, VGG-16, NasNetmobile, EfficientNetb0, AlexNet, ResNet-18, Xception, ResNet101 and ResNet-18 were employed. The experimental results demonstrate the efficacy of the proposed approach with AlexNet exhibiting the highest level of accuracy among the pre-trained networks. Additionally, a meticulous evaluation of the execution time of the classification process was performed. AlexNet achieved 100.00% accuracy with an impressive execution (training) time of 17 s. This research provides invaluable insights into applying deep transfer learning for fault detection and diagnosis in MCP. Using pre-trained networks offers an efficient and precise solution for this task. The findings of this study have the potential to significantly enhance the reliability and maintenance practices of MCP in various industrial settings

Influence of Biosynthesized Nanoparticles Addition and Fibre Content on the Mechanical and Moisture Absorption Behaviour of Natural Fibre Composite

This study looks at how incorporating nanofiller into sisal/flax-fibre-reinforced epoxy-based hybrid composites affects their mechanical and water absorption properties. The green Al2O3 NPs are generated from neem leaves in a proportion of leaf extract to an acceptable aluminium nitrate combination. Both natural fibres were treated with different proportions of NaOH to eliminate moisture absorption. The following parameters were chosen as essential to achieving the objectives mentioned above: (i) 0, 5, 10, and 15% natural fibre concentrations; (ii) 0, 2, 4, and 6% aluminium powder concentrations; and (iii) 0, 1, 3, and 5% NaOH concentrations. Compression moulding was used to create the hybrid nanocomposites and ASTM standards were used for mechanical testing such as tension, bending, and impact. The findings reveal that combining sisal/flax fibre composites with nanofiller improved the mechanical features of the nanocomposite. The sisal and flax fibre hybridised successfully, with 10% fibres and 4% aluminium filler. The water absorption of the hybrids rose as the fibre weight % increased, and during the next 60 h, all of the specimens achieved equilibrium. The failed samples were examined using scanning electron Microscopic (SEM) images better to understand the composite’s failure in the mechanical experimentations. Al2O3 NPs were confirmed through XRD, UV spectroscope and HPLC analysis. According to the HPLC results, the leaf’s overall concentrations of flavonoids (gallocatechin, carnosic acid, and camellia) are determined to be 0.250 mg/g, 0.264 mg/g, and 0.552 mg/g, respectively. The catechin concentration is higher than the phenolic and caffeic acid levels, which could have resulted in a faster rate of reduction among many of the varying configurations, 4 wt.% nano Al2O3 particle, 10 wt.% flax and sisal fibres, as well as 4 h of NaOH with a 5 wt.% concentration, producing the maximum mechanical properties (59.94 MPa tension, 149.52 Mpa bending, and 37.9 KJ/m2 impact resistance). According to the results, it can be concluded that botanical nutrients may be used effectively in the manufacturing of nanomaterials, which might be used in various therapeutic and nanoscale applications

Framework of Transactive Energy Market Strategies for Lucrative Peer-to-Peer Energy Transactions

Leading to the enhancement of smart grid implementation, the peer-to-peer (P2P) energy transaction concept has grown dramatically in recent years allowing the end-users to successfully exchange their excess generation and demand in a more profitable way. This paper presents local energy market (LEM) architecture with various market strategies for P2P energy trading among a set of end-users (consumers and prosumers) in a smart residential locality. In a P2P fashion, prosumers/consumers can export/import the available generation/demand in the LEM at a profit relative to utility prices. A common portal known as the transactive energy market operator (TEMO) is introduced to manage the trading in the LEM. The goal of the TEMO is to develop a transaction agreement among P2P players by establishing a price for each transaction based on the price and trading demand provided by the participants. A few case studies on a location with ten residential P2P participants validate the performance of the proposed TEMO

Transfer Learning Based Fault Detection for Suspension System Using Vibrational Analysis and Radar Plots

The suspension system is of paramount importance in any automobile. Thanks to the suspension system, every journey benefits from pleasant rides, stable driving and precise handling. However, the suspension system is prone to faults that can significantly impact the driving quality of the vehicle. This makes it essential to find and diagnose any faults in the suspension system and rectify them immediately. Numerous techniques have been used to identify and diagnose suspension faults, each with drawbacks. This paper’s proposed suspension fault detection system aims to detect these faults using deep transfer learning techniques instead of the time-consuming and expensive conventional methods. This paper used pre-trained networks such as Alex Net, ResNet-50, Google Net and VGG16 to identify the faults using radar plots of the vibration signals generated by the suspension system in eight cases. The vibration data were acquired using an accelerometer and data acquisition system placed on a test rig for eight different test conditions (seven faulty, one good). The deep learning model with the highest accuracy in identifying and detecting faults among the four models was chosen and adopted to find defects. The results state that VGG16 produced the highest classification accuracy of 96.70%

Multi-Objective Optimal Scheduling of a Microgrid Using Oppositional Gradient-Based Grey Wolf Optimizer

Optimal energy management has become a challenging task to accomplish in today’s advanced energy systems. If energy is managed in the most optimal manner, tremendous societal benefits can be achieved such as improved economy and less environmental pollution. It is possible to operate the microgrids under grid-connected, as well as isolated modes. The authors presented a new optimization algorithm, i.e., Oppositional Gradient-based Grey Wolf Optimizer (OGGWO) in the current study to elucidate the optimal operation in microgrids that is loaded with sustainable, as well as unsustainable energy sources. With the integration of non-Renewable Energy Sources (RES) with microgrids, environmental pollution is reduced. The current study proposes this hybrid algorithm to avoid stagnation and achieve premature convergence. Having been strategized as a bi-objective optimization problem, the ultimate aim of this model’s optimal operation is to cut the costs incurred upon operations and reduce the emission of pollutants in a 24-h scheduling period. In the current study, the authors considered a Micro Turbine (MT) followed by a Wind Turbine (WT), a battery unit and a Fuel Cell (FC) as storage devices. The microgrid was assumed under the grid-connected mode. The authors validated the proposed algorithm upon three different scenarios to establish the former’s efficiency and efficacy. In addition to these, the optimization results attained from the proposed technique were also compared with that of the results from techniques implemented earlier. According to the outcomes, it can be inferred that the presented OGGWO approach outperformed other methods in terms of cost mitigation and pollution reduction