The purpose of this paper is to investigate the wireless transmission of power in an indoor environment using multiple transmitters. The project involves a MATLAB and ADS simulation of an indoor room with 4 horn antennae transmitting power at a frequency of 2.45 GHz and a power of 13 dBm. This power distribution profile is then compared to the profile obtained from a lab experiment using the same setup. The results indicate that the profile is highly dependent on the location of the transmitters as well as the occurrence of constructive and destructive interference. These are useful in the development of large-scale indoor wireless power transfer systems as they facilitate the concentration of power in a particular location where a device can be located.
This paper presents a non-parametric tuning rule design using the modified-relay feedback test (MRFT) for the DC-AC converters. The auto-tuning guarantees a specified phase margin for the system. PID controller parameters are calculated using the tuning rules derived from a numerical optimization process. Performance of the auto-tuned controller is studied by simulation, and is compared to the response of an optimal but non-auto-tuned controller.
This paper introduces a new approach to identify generator coherency for the use of damping control of inter area modes of oscillations in power systems. The low frequency oscillations become present after any type of disturbances in the network which is due to the imbalance in electrical and mechanical torque in the machines. This consequently limits the capabilities of the network to transmit power as well as degrade the stability of the grid. This paper proposes to use the concept of enhanced generator coherency identification using machine learning to aid the damping control of such oscillations using the MPC algorithm for the wide-area damping control (WADC).
The switch to renewable energy (RE) resources is dramatically rising in a bid to reduce the emission of greenhouse gasses. However, excessive penetration from renewable energy resources like solar and wind farms leads to problems in the grid as they are intermittent and characterized by chaotic behavior. Connecting energy storage systems and smoothing filters with RE resources can help to smooth out the power fluctuations. This paper solves the problems of the conventional smoothing techniques like low pass filter (LPF) and moving average (MA) as they generate charging power to the battery system in clear days which leads to battery overworking. The problem is solved by introducing moving median (MM) and Hampel filtering (HF). The simulated results show a good smoothing performance and less battery charge comparing to the classical methods.
The need for comfortable seats rises as individuals spend more time sitting to perform various activities, including the attendance of long flights. This study proposes a system that evaluates seat comfort at early design stages without the need for physical prototyping by integrating pressure mapping, motion capture, and modeling using Computer Aided Design (CAD) and Jack software. The system shall also help in setting the base of the most optimum comfort in a seat that can also be contained in an aircraft while allowing for a greater passenger capacity.
Air Conditioning (AC) system is indeed the most demanded power especially in the Middle East. The research is concentrated on having the Ice Thermal Energy Storage System (ITESS) as an efficient cooling system to sustain the load which can be operated by Solar Photovoltaic (PV). ITESS can make ice to build and store cooling during daytime and consume the ice at night time when there is no source of solar energy. The research starts by the analysis of electricity consumption for a building in Abu Dhabi City. Moreover, an economical study comparison between air-cooled chillers, water-cooled chillers (district cooling), and air-cooled chillers retrofitted with ice thermal storage are done to evaluate the systems. The literature reviews along with the data show that AC-ITES is sustainable, environmental, and cost-effective with few drawbacks; hence, can be used by residential or commercial buildings.
Carbon Capture and storage from fossil fuel power plants has gained the attention of scientists. A post-combustion CO2 capture has been conducted using ASPEN Plus V10 software based on amine absorption /desorption from natural gas combined cycle power plant. This optimization aimed to investigate the required solvent flowrate, required thermal energy and the economic analysis for 90?pture rate of the CO2 in the first case and 99% in the second case using monoethanolamine (MEA) as a solvent. The obtained results showed that increasing the capture rate from 90% to 99?uses an increase in the solvent flow rate by 12%. Also, the increase in the capture rate causes an increase in the capital expenditures (CAPEX) by 7%. The obtained results showed an increase of less than 15% in most of the parameters which are feasible to capture 99% of the CO2 at a reasonable energy cost.
The emerging use of electric vehicles running on Lithium-ion batteries (LIB) is growing. This development is primarily a response to mitigate environmental challenges such as climate change, where there is a clear global agenda for achieving net-zero carbon emissions in the future. One of the challenges in achieving future net-zero carbon emissions relates to the supply chain of raw materials used in the production of clean energy technologies.The challenge is to maintain the demand and supply of the clean energy market for these materials to sustain the production of technologies such as light-duty vehicles and portable devices. LIB's primary materials used to improve its performance and cost are cobalt, lithium, nickel, graphite, manganese, and aluminum. The rapid growth in electric vehicles and energy storage sectors amplified the global demand for manufacturing LIB.Recycling could play an essential role in reducing primary raw materials demand until 2040.
E-commerce sales have risen across the world and further accelerated by the pandemic to currently achieve levels it was not expected to reach until 2022. This increase has prompted companies to look for cost-friendly alternatives to tackle the 'last-mile delivery challenge. This work aims to solve the last-mile problem by using location-based crowdsourcing where individuals would compete to deliver packages in exchange for monetary compensation as a detour on their original driving route. Existing solutions have heightened environmental cost as crowdsourced workers often take long detours as opposed to optimal routes taken by delivery trucks leading to more fuel consumption and pollution. Additionally, the solutions offer inadequate compensation for workers and are catered to small-scale data and thus perform poorly when the number of tasks increases. Some challenges faced would include identifying the necessary parameters, optimizing the matching algorithm, and selecting a routing algorithm pertaining to the pickup delivery problem.
Nowadays, in order to tackle climate change issue, governments seek to integrate as much renewable energy as possible. For large industrial energy users such as cement, they require large amount of energy. So it's important to think about ways to integrate renewable energy from these industries. This paper investigates cement industries to find ways to reduce CO2 emissions from cement production process. This work represents a methodology for mapping energy requirements and optimizing renewable electricity integration.
