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Item Enhanced DC Microgrid Protection: a Neural Network and Wavelet Transform Approach(2024-05)This thesis introduces an advanced protection scheme for DC microgrids, focusing on enhancing fault detection, classification, and localization while ensuring real-time operation. Leveraging the wavelet transform algorithm and neural networks' pattern recognition capabilities, the proposed system integrates modern techniques for achieving its objectives. The protection coordination scheme encompasses two settings: the primary coordination scheme, activated when the ANN accurately identifies the fault's location, and the backup coordination scheme, activated in the event of inaccuracies or errors in the neural-based algorithm. In this scenario, an optimization model is deployed to ensure that protective devices operate with predefined operation times and parameter settings, aiming to minimize the total operation time of all relays, including primary and backup. This ensures fault isolation regardless of the neural-based algorithm's status, with the optimization problem modeled as an NLP programming problem and solved using the optimization software GAMS. The optimization model acts as a duplicate protection, enhancing the protection system's reliability by providing an additional layer of defense. Furthermore, an innovative inductor injection mechanism is introduced to enhance the protection scheme's effectiveness. By injecting an inductor into the system after fault detection, the rate of fault current rise is significantly reduced, allowing for an expanded SFV (spatial feature vector) size without compromising fault detection accuracy. The inductor injection mechanism enables the SFV to encompass additional time slots, facilitating more comprehensive data input to the neural network for improved fault classification and localization. Additionally, the inductor injection mechanism is carefully selected to balance current damping with fault detection requirements, ensuring optimal system performance under various fault conditions. Simulations using MATLAB Simulink validate the proposed protection scheme's effectiveness, demonstrating high accuracy and reliability with real-time operation and robust error handling mechanisms. This research advances protection systems in DC microgrids, offering improved fault detection, classification, coordination, and localization capabilities.Item Content-Symmetrical Multidimensional Transpose of Image Sequences for the High Efficiency Video Coding (HEVC) All-Intra Configuration(MDPI, 2025-04)Enhancing the quality of video coding whilst maintaining compliance with the syntax of video coding standards is challenging. In the literature, many solutions have been proposed that apply mainly to two-pass encoding, bitrate control algorithms, and enhancements of locally decoded images in the motion-compensation loop. This work proposes a pre- and post-coding solution using the content-symmetrical multidimensional transpose of raw video sequences. The content-symmetrical multidimensional transpose results in images composed of slices of the temporal domain whilst preserving the video content. Such slices have higher spatial homogeneity at the expense of reducing the temporal resemblance. As such, an all-intra configuration is an excellent choice for compressing such images. Prior to displaying the decoded images, a content-symmetrical multidimensional transpose is applied again to restore the original form of the input images. Moreover, we propose a lightweight two-pass encoding solution in which we apply systematic temporal subsampling on the multidimensional transposed image sequences prior to the first-pass encoding. This noticeably reduces the complexity of the encoding process of the first pass and gives an indication as to whether or not the proposed solution is suitable for the video sequence at hand. Using the HEVC video codec, the experimental results revealed that the proposed solution results in a lower percentage of coding unit splits in comparison to regular HEVC coding without the multidimensional transpose of image sequences. This finding supports the claim of there being increasing spatial coherence as a result of the proposed solution. Additionally, using four quantization parameters, and in comparison to regular HEVC encoding, the resulting BD rate is −15.12%, which indicates a noticeable bitrate reduction. The BD-PSNR, on the other hand, was 1.62 dB, indicating an enhancement in the quality of the decoded images. Despite all of these benefits, the proposed solution has limitations, which are also discussed in the paper.Item A Real-Time Energy Management System Across Diverse Energy Sectors in Smart Cities(2024-05)The population growth and economic expansion have led to an unprecedented demand for energy, water, and transportation. This increased demand is contributing to the increase of greenhouse gas (GHG) emissions and intensifying the climate crisis. As a result, governments are strategically pursuing carbon neutrality by introducing the use of renewable energy resources (RESs) and electrifying various end-uses, such as electric vehicles, in power systems. Further, the adoption of advanced water treatment methods, with a particular emphasis on the energy-intensive desalination method, the seawater reverse osmosis (SWRO), becomes essential, especially in arid regions where conventional water sources are scarce. Integrating various energy sectors like SWRO systems and EV charging stations, and the existing electric power system presents significant challenges, primarily because the original design of the system did not accommodate these technologies. Consequently, the concept of an energy management system has emerged as an effective approach to bridging the gap between the different energy sectors. In this thesis, a new real-time framework is presented and tested for concurrently optimizing the three energy sectors as one interdependent model. The proposed model is a mixed-integer nonlinear programming model implemented within the GAMS optimization environment and is tested using the modified IEEE RTS 24-bus test system. The proposed approach takes into account the technical constraints of both the electric, water, and transportation systems, ensuring the optimization of their operation without compromising their technical limitations and the delivery of the services to customers. Several case studies are presented, each employing different optimization strategies. The results highlight the potential of real-time co-optimization in enhancing system flexibility and reducing costs. Specifically, it demonstrates a $73,000 or 12.7% reduction in operating costs for the co-optimized energy-water-transportation system.Item Test 20250413(2025)Item Nano-clay based flexible and implantable bioelectrodes for human body neurostimulation: fabrication and characterization(2024-05)This thesis explores the development and characterization of nano-clay based flexible and implantable bioelectrodes for human body neurostimulation, focusing on optimizing their biocompatibility, stability, and electrochemical attributes. Key materials such as silicone, nano-clay, glycerol, polyethylene glycol (PEG), and isoalcohol were employed to create a variety of composite samples. The primary objective is to ascertain how different material combinations influenced the electrodes' properties, aligning them with requirements for successful application in neurostimulation devices. Electrochemical Impedance Spectroscopy (EIS) and Cyclic Voltammetry (CV) provides comprehensive insights into the electrodes' performance. The EIS results indicated that varying the glycerol and PEG content affected the electrodes' bulk impedance, conductivity, and charge storage capacity. For instance, a sample with a 50% silicone, 20% glycerol, and 30% nano-clay composition showed a bulk impedance of 5.47 kΩ and conductivity of 2.33×10⁻⁵ S/cm, significantly outperforming a similar sample with PEG, which exhibited a higher bulk impedance of 38 kΩ and lower conductivity of 3.35×10⁻⁶ S/cm. These findings underscore the role of glycerol in enhancing electrochemical properties conducive to effective neural interface operations. Mechanical testing highlighted that the incorporation of nano-clay generally increased stiffness, whereas glycerol and PEG improved flexibility and conductivity. The optimal formulations displayed mechanical properties that were well-matched to the compliance required for integration with soft tissues, enhancing the potential for chronic implantation without adverse tissue reactions. Long-term immersion tests further demonstrated the electrodes' robustness, showing minimal degradation of electrochemical properties over extended periods, thus confirming their suitability for long-term neurostimulation and other clinical applications. The study successfully demonstrated that nano-clay based bioelectrodes could achieve excellent electrochemical performance and mechanical compliance, suggesting their potential for advanced biomedical applications. These findings pave the way for further research aimed at refining the bioelectrode technology for enhanced therapeutic outcomes in neurostimulation and other medical interventions.
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