Environmental Monitoring in Industry: Leveraging AI and IoT for Sustainable Solutions
1. Environmental gases monitoring in Industry
2. Levaragimg of AI and IoT Technologies
3. Deployed IoT solutions
Effect of nanofluids on performance of a flat plate solar collector
The increasing world population has increased the demand for electricity and energy. This is putting pressure on the already depleting fossil fuel resources to keep up with the demand and that is why identifying alternative ways of producing energy, especially renewable energies, is critical moving forward into the future to produce the energy demand as well as tackle some of United Nations’ Sustainable Development Goals. This study aims to investigate the performance enhancement of various nanofluids on a flat plate solar collector via an experimental study using a flat plate solar collector test rig. Nanofluids, namely water-copper oxide, water-aluminium oxide, radiator coolant-copper oxide, and radiator coolant-aluminium oxide were prepared at a 0.1% nanoparticle volume concentration through magnetic stirring with the addition of 15% concentration of the Triton-X surfactant. All four nanofluids along with water and radiator coolant were investigated at 0.5 and 0.75 LMP flow rates. The data obtained were used for numerical calculation using MS Excel to calculate the thermal efficiency of the flat plate solar collector. The findings are that the water-aluminium oxide had the maximum energy efficiency at 54.7% and 53.7% at 0.5 and 0.75 LMP flow rates respectively. Overall, the higher flow rate returned a higher efficiency.
Hydrogen Economy: A Review on the Current Applications, Policy and Production Outlook
Hydrogen, an energy carrier, is deemed as a prospective substitute for fossil fuels. Data from different articles, published documents show that the consumption of hydrogen is increasing globally, and it has increased 23% in 2020 than 2015. Different sectors such as transport and power are expected to switch to greener and cleaner energy, such as hydrogen, because it produces zero or near zero emission. To achieve net zero goal by 2050, around 530Mt of hydrogen is needed and it has been forecasted that sectors such as transport and power will dominate the consumption of hydrogen in future. Many countries are adopting or enacting policy to incorporate hydrogen into the energy sector as a substitute of fossil fuel to curb emission. This paper briefly reviewed global hydrogen production scenario, and its applications and policy in different countries and continents/sub-continents. Literatures suggest that the electrolysis method of hydrogen production is above other techniques in terms of technology and commercial readiness level. Many countries have significantly invested on research and infrastructure development to incorporate hydrogen in their energy sector. The paper will provide an in depth understanding of the global hydrogen production scenario, and its application and formulated policy in different countries.
Gesture-Based Language: Transforming Sign Language to Readable Text
For the deaf and hard-of-hearing, sign language
is an essential mode of communication. To
some, it might be a novel way to express oneself
without using words at all. But there’s a little
thing called the language barrier that gets in the way all too often. To help address this issue, this study creates a gesture-based system to convert sign language to readable text, which can help the users a little.
Advanced Energy Management for Homes: Optimized Control of PV, Battery, and EV Systems
The integration of renewable energy sources (RES) is essential for sustainable energy management systems (EMSs) in residential areas. However, the adoption of traditional EMSs remains constrained due to inefficiencies and limited adaptability to varying energy demands. This study presents a Solar PV/battery/EV-based EMS for home load that enhances energy efficiency, adaptability, and cost-effectiveness. The system converts solar energy into DC power using photovoltaic panels, which are then stored in a battery bank. An intelligent controller optimizes energy distribution by prioritizing essential loads and reducing reliance on grid power. The proposed advanced EMS model, developed using MATLAB Simulink optimization, demonstrates an energy efficiency ranging from 85% to 90%, resulting in expected energy savings of around 80% over traditional Home Energy Management System (HEMS) and improved user convenience by automating energy distribution. The developed model assumes that a standard residential load of around 10 kWh can be sustainably managed, ensuring uninterrupted power supply during peak hours and minimizing grid dependency.
Intelligent Fault Diagnosis in Smart Grids: Leveraging PMU Data with VGG-Based CNN Models
The evolution of smart grid technology necessitates sophisticated methods for fault detection to ensure system reliability and efficiency. Monitoring a complex power grid with phasor measurement units (PMUs) continuously transmitting data at high velocities. Rapidly and accurately analyzing this extensive data to detect faults presents a major challenge for grid operators. This study introduces a novel approach for fault classification in smart grids by utilizing Convolutional Neural Networks (CNNs) based on the architectures of VGG16 and VGG19. VGG is capable of rapidly classifying various grid events, such as faults, generation losses, and synchronous motor switching, with high efficiency. The system detects faults swiftly, allowing operators to minimize downtime and prevent significant damage by enabling prompt responses. The study meticulously examines the performance metrics of each model, including accuracy, precision, recall, and F1 score. Evaluations reveal that the VGG16 model outperforms VGG19, achieving an impressive accuracy of 98.75% and consistent precision, recall, and F1 scores of 0.99. In contrast, the VGG19 model attained a lower accuracy of 95.00%, with slightly diminished performance metrics. These findings highlight the efficacy of advanced deep learning techniques in improving fault detection accuracy within smart grid systems, suggesting that VGG16 offers a more reliable and accurate solution compared to VGG19.
Transient Stability Analysis of Islanded MV Microgrid under Variable Load and Fault Events
1. The authors proposed the design of an MV microgrid with traditional DG and a considerable PV plant control WECC model.
2. The proposed system has a better controller time constant, which can guarantee the effectiveness and robustness of the system.
3. The voltage profile of critical buses is improved, which results in a 1% steady-state error.
Limiting the Pollution of Batteries used in Ultra-Low Power Consumers. A Comprehensive Short Review
Detailed review of battery pollution in ultra-low power consumers
DormGuardNet: A Lightweight Deep Learning Model for Detecting Prohibited Items in Student Dormitory Environments.
This paper investigates the critical challenge of detecting prohibited items in student dormitories, and we proposed a new deep-learning model to detect prohibited items automatically. To address the lack of an existing dataset for this task, we developed a new dataset, PISD (Prohibited Items in Student Dormitories). Our model achieved competitive performance, with the lowest GFLOPS and inference time, the highest FPS, and strong results in terms of precision, and recall highlighting its efficiency and effectiveness. This demonstrates the model’s capability to reliably detect and classify prohibited items in student dormitory environments.
Analysis of Power Flow Control in Electrical Networks Considering Photovoltaic, Battery Energy Storage Systems and Electric Vehicles
Microgrids are localised power system that use local power generation to supply electricity to nearby loads. This idea has gained popularity with the development of battery energy storage system (BESS) and the emergence of renewable energy sources (RES). The integration of electric vehicles (EVs) into the grid has made it possible to integrate batteries and RES without significantly altering the system. The operation of microgrids is has discussed in this study with a special emphasis on power flow control during system disturbances and transitions. This article describes an algorithm designed to optimise the regulation of power, voltage, and frequency in a microgrid that includes EVs. The results of this study show that load control and energy distribution using simulation studies may be done effectively. This study used suggested algorithm to show stable frequency and controllable voltage dips. Additionally, this study contributes to a better knowledge of microgrid control.