Browsing by Author "Bayne, Stephen"
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Item Advanced Grid Operations: The Real-Time Simulation of Power System Faults to Innovate Protection Methodology(2023-08) Beard, Ashley; Bayne, Stephen; He, MiaoThis research answers the question, “With the current knowledge of power systems engineering, would protection problems be solved the same way?” Today’s protection methodologies follow a very narrow approach that relies on overcurrent detection, and these are traditionally used in power systems with radial power flow. In other words, when a protection scheme senses a higher than intended current, it limits or disables current flow; however, when this type of problem is approached with today’s innovative problem solving solutions, a lens is provided to see issues differently. In Advanced Grid Operations research, a Real-Time Digital Simulator is used to simulate, in real-time, several different types of faults on different types of power systems to create a repository of fault data used to train a Machine Learning algorithm. Machine Learning Protective Relaying aims to ultimately provide additional information that can be leveraged to solve protection problems regarding traditional protective relays, renewable energy, and other roadblocks.Item An assessment of deep cyber-physical situational awareness of power system using real-time testbed(2023-12) Bhatta, Rabindra; Bayne, Stephen; Chamana, Manohar; He, Mio; Li, Changzhi; Liyanage, SankaIn essence, smart grids are electrical networks that transmit and distribute electricity in a reliable, effective manner using information and communication technology (ICT). Trust and security are of the utmost importance. False data injection attacks (FDIA) are one of the newest security problems, and they can drastically impact the use of energy. By comprehending the correlation between the cyber layer and the physical layer, this research develops an effective and real-time technique to identify FDIA attacks in smart grids. We expose the existing vulnerabilities associated with existing detection techniques and analyze the performance of the proposed solution to detect and defend FDIA attacks against real-time measurements from the meters. We expose the implementation of the attack and the success of the detection algorithm using IEEE test systems and expand the real-world constraints associated with it. We show that the suggested method offers an accurate and dependable solution using realistic simulations based on the smart grid.Item Analysis of offshore wind energy in Colombia: Current status and future opportunities(2020) Arce, Laura; Bayne, StephenOffshore wind energy is a sustainable and innovative energy source. However, its performance is extremely dependent on the local meteorology and oceanographic conditions. There are numerous opportunities as well as challenges to generate energy on a commercial scale in Colombia. This work tries to set up a base for harnessing offshore wind energy, considering the integration into the Colombian grid to offshore wind energy and the cost compared with the current system. The roadmap of the future of offshore wind energy in Colombia must be to fulfill three primary objectives identify the best opportunities for harnessing the offshore wind resource, to improve the investment in resources, and to reduce carbon dioxide emissions. This study provides specific knowledge about opportunities and challenges of offshore wind energy in Barranquilla, Colombia, through both technical and economic aspects.Item Broadband highly efficient linear millimeter-wave medium-power power amplifier design(2022-05) Mayeda, Jill C.; Lie, Donald Y. C.; Bayne, Stephen; Chong, Jo Woon; Kong, Song-Charng5G (fifth-generation) mobile network’s FR2 band (i.e., 24.25 to 52.6 GHz) is currently used to support eMBB (enhanced Mobile Broadband) applications to achieve 10+ Gb/s download peak speed, and sub-1mS latency for the UR/LL, mMTC (ultra-reliable machine type communication) applications. The RF (radio-frequency) power amplifiers (PAs) are usually the most power-hungry components in the RF front-end modules (FEM) of a handset or in other portable wireless electronic products. Since the 5G FR2 band is considerably higher in frequency compared to the sub-6 GHz 5G FR1 band, the power-added efficiency (PAE), bandwidth (BW), and linearity of the mm-Wave 5G PAs will all be considerably lower than their 4G and sub-6GHz 5G counterparts, which makes the highly-efficient broadband and linear mm-Wave PAs design a critical area for the success of mm-Wave 5G. For example, path loss is higher at these mm-Wave frequencies so it would be necessary to use a larger number of more complicated mm-Wave phased array channels with MIMO (multiple-input and multiple-output) antennas to achieve 3-D beamsteering capabilities with the desired output power, which means inefficient PAs may create intolerable heat and reliability issues for mm-Wave systems. In mm-Wave 5G user equipment (UE) and femto/picocells, due to the increased number of channels, each PA’s output power (POUT) requirement will mostly only need to be in the sub-Watt range (i.e., <30 dBm), which is significantly reduced from their 4G/sub-6 GHz 5G counterparts. Also, if a PA can cover the entire ~20 GHz BW of the key mm-Wave 5G bands, it would be really attractive as it can greatly reduce the total number of FEMs needed for arrays, and is thus one key motivation for our broadband PAs design. Thus, in this proposed work we will investigate the design of mm-Wave PAs in the medium power range of ~ < 20 dBm, with very broad BW of ~ 20GHz, excellent broadband peak PAE > ~20%, and also good linearity. Thus this work looks at the design of broadband mm-Wave PAs in several state-of-the-art technologies for broadband commercial and DoD applications.Item Clustering and fast search of high dimensional big data(2022-08) Safari, Zohreh; Zhuang, Yu; Sheng, Victor; Zhang, Yuanlin; Serwadda, Abdule; Bayne, StephenSimilarity Search aims to extract the most similar objects to a given query which is very useful for many information retrieval applications. For big data, with different kinds of data from various resources, the search efficiency is as important as search quality. The importance of fast search operation motivates us to have a research for this main. To enable fast search, data needs to be organized/indexed, and clustering is one way to do it. For clustering, to be effective for search, firstly, we need to identify an appropriate number of clusters for the clustering, and secondly, develop a fast-easy-to use clustering procedure which allows evaluation of the software. My ph.D research consists of two parts to focus on two significant clustering aspects. The first part is a fast-easy-to-use clustering procedure with automatic determination of cluster numbers. We used clustering as a framework to organize data with high dimensions. Indeed, the hierarchical indexing struc- ture, similarity search-tree (SS-Tree)(White and Jain, 1996), and the clustering algorithm implemented. We also investigated a problem in clustering algorithms to determine the number of clusters; while in the second part, I mainly concen- trated on clustering in terms of facilitating search, as clustering is one of the most important method to reduce the cost of similarity search from the viewpoint of CPU and disk processing. We proposed a new structure to have more efficient search in big data with high dimensions and compared the new method with the existing tree-based structure in terms of computation time when finding the most similar data points in given ranges. The results indicates linear organization data set outperforms tree based structure when it comes to high dimensional big data.Item Continuous-Wave radars – applications, security, and target emulator(2022-08) Nallabolu, Prateek; Li, Changzhi; Bayne, Stephen; Saed, Mohammad; Ma, YaoRadars have been increasingly used for human-aware detection/activity recognition in the smart living sector, and to realize advanced driver-assistance systems (ADAS) and autonomous driving in the automotive industry. Prior to large-scale deployment of these sensors, it is essential to study the potential threats against these sensors that can interrupt their designed functionality, and comprehensively test them in scenarios that mimic real-world conditions. This dissertation presents three interlinked aspects related to continuous-wave (CW) radars: 1) their application as a sensing modality for smart homes, cities, and infrastructure, (2) a feasibility study on the possible spoofing attacks against CW radars, and (3) a low-cost CW radar target emulator for extensive testing under various background conditions. It is crucial for the radar sensors deployed indoors to isolate the target of interest from unwanted clutter sources. A novel approach to suppress both stationary and moving clutter sources based on exponential moving average (EMA) filtering is proposed for indoor sensing. Although EMA-based filtering techniques were used for stationary clutter suppression in existing works, the proposed work addresses moving clutter suppression as well. The proposed approach removes all motion artifacts outside the characteristic frequency range of the human cardiopulmonary motion. A filter-and-subtract method is employed, where the output from two filters with different cut-off frequencies is subtracted so that all moving clutter signatures are attenuated while retaining the human target signatures. For experimental validation of the proposed approach, a 60-GHz frequency-modulated continuous-wave (FMCW) radar with single-input multiple-output (SIMO) architecture is used to uniquely identify the 2-D location of a stationary human subject, with various moving and stationary clutter sources in the background. By leveraging the digital beamforming (DBF) capability of the 60-GHz radar, a sideways hand gesture recognition technique is proposed that determines the instantaneous 2-D position of the hand at the start and end of the gesture. To detect the start and end time of the gesture, EMA filtering was applied to attenuate the reflections from the human body so that the signature of the gesture is prominent. The results presented can differentiate left-to-right and right-to-left lateral hand gestures. A novel frequency-domain spoofing attack model is proposed to investigate the vulnerability of FMCW radars against malicious attacks. The proposed model avoids the need for precise nanosecond synchronization with the victim radar’s chirp transmission time. A single-sideband (SSB) mixer is the key component of the attack system, which introduces a frequency shift to the radio frequency (RF) chirp signal transmitted by the radar and retransmits the modulated chirp signal. Upon deramping operation on the radar's receiver chain, the introduced frequency shift by the SSB mixer translates to the beat frequency of the baseband signal, thereby creating an illusion of a real target. The frequency shift introduced can be varied to alter the range of the fake target. The theory of the spoofing model is developed, and a 5.8-GHz proof-of-concept spoofing system is designed to provide experimental validation. A hybrid-chirp FMCW waveform is proposed to distinguish a real target from a spoofing target to mitigate spoofing attacks. Finally, a low-cost radar target emulator (RTE) for Doppler and FMCW radars is proposed. The proposed RTE is designed to emulate the radar’s response generated by two human activities: the Doppler artifacts of a human elbow-down gesture as seen by a Doppler radar, and the inherent chest motion of a stationary human subject measured by an FMCW radar. For the Doppler mode RTE, an SSB mixer is utilized to electronically modulate the CW signal transmitted by the radar to resemble a human hand gesture. The vital sign motion of a stationary human target is emulated using an analog phase shifter that serves as an integral part of the FMCW mode RTE. An SSB mixer integrated with the FMCW RTE showcased the ability to vary the range of the human target as well. The Doppler- and FMCW- mode target emulators are realized using 5.8 GHz commercially available off-the-shelf RF components. Due to the high similarity between the system-level architecture of an RTE and a spoofing system, the RTEs discussed above are set up as spoofing systems to deceive two state-of-the-art human detection algorithms.Item Cyber Security of Grid-Scale Battery Energy Storage Systems using Battery Modeling and Statistical Methods(2023-12) O'Brien, Victoria A.; Rao, Vittal; Nutter, Brian; Bayne, Stephen; Trevizan, RodrigoBattery Energy Storage Systems (BESSs) are cyber-physical systems (CPSs) that can be implemented at the grid-scale to supplement intermittent energy generation sources by storing excess energy and supplying energy for grid balancing. To meet grid scale requirements, hundreds of battery cells may be connected as stacks and are controlled by a battery management system (BMS). The BMS ensures the safe operation of the batteries by taking sensor readings, estimating battery states, and protecting and balancing the cells. Most BMSs have voltage sensors for each cell and the battery stack, that may be vulnerable to cyber attacks, and sensor readings are used to estimate the state of charge (SoC) of each cell. The SoC is the available capacity of the cell relative to the total cell capacity and cannot be measured directly. Inaccurate SoC estimation has been linked to the overcharging or overdischarging of battery cells, which could result in rapid degradation, or thermal runaway events. CPSs have been the target of various cyber attacks and malware, one notable example being the Stuxnet worm attack on Iranian industrial plants. One type of man-in-the-middle attack, the false data injection attack (FDIA), aims to disturb state estimation by corrupting sensor measurements before they are used in state estimation. FDIAs use knowledge of the system’s parameters to evade traditional detection mechanisms. Although there have not been FDIAs discovered in CPSs yet, multiple publications contain brute-force and heuristic methods to design FDIA vectors, so proactively exploring defense against FDIA is crucial. It is critical to detect and isolate FDIAs to ensure the safe operation of CPSs, in this case the detection of FDIAs is applied to BESSs. In the scope of grid-scale BESSs a bad actor may use FDIAs to corrupt sensor readings, which could lead to inaccurate SoC estimation. In this dissertation, a three-pronged approach was used to detect FDIAs targeting the sensors of a BESS. Step one was to use a battery model to represent the dynamics of the battery cell or stack, step two was to use a suitable estimation method to estimate the system states, measurements, and generate measurement residuals, and step three was to postprocess the measurement residuals using a FDIA detection mechanism to determine the presence of FDIA. Multiple battery models were studied to represent the cells, including equivalent circuit models (ECMs), ambient temperature dependent ECMs (ATDECMs), and single particle models (SPMs). Various estimation methods were utilized including the Kalman filter (KF) for linear systems, the extended KF (EKF), input noise aware EKF (INAEKF), and unscented KF (UKF) for nonlinear systems. A statistical cumulative sum (CUSUM) algorithm was used to postprocess residuals and detect small-magnitude bias FDIAs injected to the sensors of BESSs. Simulations performed in MATLAB and Simulink were used to demonstrate the effectiveness of the CUSUM algorithm in detecting FDIAs applied to battery cells and stacks. FDIAs were applied to the scenarios described above, where the battery model and estimation method were varied from case to case. The CUSUM algorithm could be tuned to detect the FDIAs in all the systems studied, and in some cases was able to reveal pertinent information about the attack vector such as the targeted sensors and the sign of the FDIA. The false positive rate was able to be tuned to zero in each case, eliminating false alarms sent to the BMS.Item Deep Learning for Smart Grid Applications(2022-12) Hasan, Md Mahmudul; He, Miao; Giesselmann, Michael; Bayne, Stephen; Chaoui, HichamIn an effort to prevent climate change and make clean energy accessible to all, the global community is shifting away from electricity generation from fossil fuels and toward renewable energy sources. However, the electricity generated from renewable sources is stochastic in nature, exhibiting significant intermittency and variabilities, necessitating a smart power management system from the point of generation all the way through to the point of consumption. To make the power grid smarter, in this research, two individual projects are carried out with the aim of smart energy management from the generation end to the consumer end. With the help of deep learning integration, firstly, we modeled and analyzed a convolutional neural network (ConvNets)-based forecasting method for regional renewable energy (wind power) ramp. Secondly, we developed a reinforcement learning-based control scheme in droop-virtual inertia control for resilient community microgrids. Wind generation is highly intermittent with enumerable ramp events, making power management challenging. A smart forecasting method can improve system management and security to address this issue. However, spatial dynamics hinder the accurate forecasting of physical system data. To account for these spatial dynamics of physical systems, herein, we create a unique model for ramp forecasting in wind generation using ConvNets. First, we suggest a dependency and multi-resolution image processing technique that may enhance the geographical dependency of physical system data to circumvent the current constraints on the use of deep learning in this field. Because this multi-resolution enhancement maintains the high spatial dependency along spatial data, the kernel of ConvNets becomes very effective during feature extraction, and translationally variant characteristic has lifted. As a result of these two enhancements, the ConvNets may now utilize data from the physical system, allowing for more accurate forecasting compared to the state-of-the-art benchmark approaches. The community microgrid concept is becoming more attractive due to the large penetration of renewables and the adoption of battery systems of electric vehicles (EVs) as energy storage. However, the stochastic nature of these loads injects instability due to voltage and frequency fluctuation. Herein, we propose a novel microgrid control strategy incorporating reinforcement learning to address this issue in the microgrid system. A resilient community microgrid model, which is equipped with solar PV generation and electric vehicles (EVs) and an improved inverter control system, is considered. To fully exploit the capability of the community microgrid to operate in either grid-connected mode or islanded mode, as well as to achieve improved stability of the microgrid system, universal droop control, virtual inertia control, and a reinforcement learning-based control mechanism are combined in a cohesive manner, in which adaptive control parameters are determined online to tune the influence of the controllers. The microgrid model and control mechanisms are implemented in MATLAB/Simulink and set up in real-time simulation to test the feasibility and effectiveness of the proposed model. Experiment results reveal the effectiveness of regulating the controller's frequency and voltage for various operating conditions and scenarios of a microgrid.Item Development of Digitally Reconfigurable Radar for Short-Range Sensing(2023-12) Brown, Michael C.; Li, Changzhi; Saed, Mohammad; Nutter, Brian; Bayne, StephenMultimode continuous wave (CW) radar systems are being investigated for a wide range of applications. These systems are adept at measuring a target’s velocity by analyzing the frequency response of the return signal. Target distances can be determined by introducing modulation to the transmitted CW signal, such as frequency modulation (FMCW) or phase modulation (PMCW). Of the modulated CW radar system families, PMCW radar systems have been less explored compared to FMCW radar systems. The need for high-accuracy time-domain analysis, fast phase shifting, high data load, and system complexity has limited PMCW radar design compared to FMCW radar design. Despite these hurdles, simulations of PMCW radar systems show promise in addressing the increasingly cluttered frequency spectrum. Radar-to-radar interference, an issue faced by all modulated CW radar systems, can be reduced by implementing orthogonal PMCW modulation schemes. Joint radar-communication (JRC) systems capable of radar-to-radar communication can also be realized with PMCW waveforms. These informational advantages come at the cost of system and processing complexity. As such, the prevalent approaches to commercialized radar design focus on the integrated circuit (IC) design. These ICs can be manufactured for multimode operation but are not ideal solutions for rapid radar design due to their cost, complexity, and fabrication time. With the speed at which digital systems are improving, such as working with faster clock speeds, improved field programable gate arrays (FPGA), and more accurate signal acquisition, IC radar design must consider current and emerging technologies along with existing design costs. A more flexible approach to the prototyping and verification of digital signal integration into radio frequency (RF) systems is to use the advantages of existing ICs with PCB microwave structures. With access to faster semiconductor technologies, embedded chip solutions, and improved PCB techniques, PMCW radars can be realized on a portable board-level system that is more flexible for prototyping and continued technological growth. Integrating existing RFICs and digital systems opens the door to PMCW radar system exploration. This dissertation presents the theoretical analysis, component design, and experimental evaluation of a low-cost, portable, reconfigurable radar system implementing digital communication techniques for short-range sensing. As opposed to emerging PMCW radar IC designs with integrated FPGAs, the proposed K-band radar system can implement CW Doppler, PMCW, and FMCW operation modes using a digital modulation source that can be upgraded and reprogrammed without needing to refabricate the system. A voltage-controlled oscillator (VCO) controls the frequency of the carrier and implements the FMCW modes. High-frequency phase shifter ICs are commonly designed to support multi-bit phase shifting over a limited bandwidth, resulting in radar integration challenges and high costs. The PMCW mode for the proposed system only requires one-bit modulation, which is used for a high signal-to-noise ratio (SNR) and improved time-domain analysis of the radar response signal. As opposed to integrating a phase shifter IC, a wideband binary phase shifter (BPS) is implemented on a PCB utilizing fast-switching PIN diodes and conventional microwave structures. This BPS is vital for implementing PMCW operation, where its three-state system controls amplitude and phase modulation of the carrier signal. The bandwidth dependence of the BPS is explored as well, and an ultra-wideband BPS is evaluated theoretically and experimentally. The proposed radar system also uses microwave structure-based balanced mixers to downconvert the received high-frequency signal to a low-frequency baseband signal containing the target information and phase modulation. This approach further lowers the cost of the radar system while accounting for potential target response nulls by using in-phase and quadrature (I/Q) signals. To verify the system’s PMCW performance, short-range human responses are measured. A demodulation algorithm is used to recover the subject information. Experimental results from the radar demonstrate the system feasibility and differences between CW and PMCW modes when measuring the respiration rate of a human subject and in gesture detection.Item Dual diode-based current sense system: Uncertainty sources and possible accuracy improvements(2021-08) Soares Da Silva, Alexandre; Nutter, Brian; Bayne, StephenResistors are the classical current sense device; nevertheless, they are range-limited due to power constrains. A diode, given its exponential I(V) function, potentially offers wider range of current measurements than a resistor. To investigate the feasibility of a diode-based current sense device, the Texas Tech-Teradyne Diode Project phase 1 developed a proof-of-concept system based on a dual diode package. This thesis describes how that system was implemented, identifying sources of uncertainty from calculations and measurements, and suggesting posterior corrections to model outputs based on calibration.Item Experimental set up of PMU network and application of artificial neural network for PMU generated data analysis(2021-08) Roy, Vishwajit; Giesselmann, Michael; Bayne, Stephen; He, MiaoOne of the significant developments in Power systems is the implementation of new devices to ensure stability. The Power system is a dynamic system consists of Generation, Transmission, and Distribution. All these sectors of the Power System need to be monitored with accuracy, other than the system can be unstable within a blink of an eye. The newly developed device, Synchrophasor (PMU-Phasor Measurement Unit) is introduced in Power System for better stability and Wide area measurement (WAM). The high resolution of time-stamped data containing 30 to 60 samples per second provides high accuracy for power grid monitoring. The introduction of PMUs in the power system has increased the large volume of data. These data like the voltage, current, frequency, and phase angle impose an important rule for power quality. Measurements from the above parameters can help for better understanding of disturbances and attacks. The overall aim of the dissertation is the design, development, and experimental setup of a small-scale PMU network and analyze data obtained from the PMU network. The analysis focuses on the traditional and application of Artificial Intelligence-based Techniques. Methods used in Artificial Intelligence help fast detection and extraction of proper information. For instance, traditional state estimation methods in SCADA (Supervisory Control and Data Acquisition) comprise a small volume of samples 2 to 4 samples per second. This kind of data has the probability to skip large events as the samples are low. But the data generated by PMU is large in volume and it is difficult to analyze the large volume of data with the traditional system. This kind of data is considered Big Data. Extraction of information from a large volume of data is tedious but the contents of the information are worthy. For experimental purposes, the large amount of PMU data from commercial grids are not easily available due to security reasons. At the same time, it is not easy to share total system data from any Opco as it contains network information which can make the system vulnerable, if it is a point of attack for an intruder. This dissertation proposes the experimental setup of a PMU network to find out different steps of implementing PMUs in a power system. On the other hand, data acquisition is not the final step. Receiving raw data in a PDC (Phasor Data Concentrator) needs to be transformed in a meaningful format for analysis. Another important contribution of this work is to implement a Cyber-Physical Platform to find the characteristic of PMU data when subject to external attack and find the scenario of how the attack propagates. The main contributions of the dissertation are as follows: A newly designed Synchrophasor network consisting of seven PMUs in different locations and provides connectivity to a server called PDC (Phasor data concentrator). The connectivity issues comply with IEEE C 37.118 protocol and different IP issues as well as firewall rules to transmit data over a large network. Several barriers need to overcome to make up a smooth data propagation network. As PMU generates a large volume of data, the data need to be archived and transform into a readable format. Different steps were followed inclusion of application of Artificial Intelligence models to analyze the data. Each dataset needs pre-cleaning and pre-preparation for analysis. All these kinds of analysis were completed with the application of different techniques and programming using R, Python and Matlab. At the end of the experiment, the PMU based network was also used to develop a Cyber-Physical Platform to find out the scenario after the attack. This is a systematic way to intrude data and find the nature of the trend which will help to discriminate the scenarios of attack and traditional fault. This dissertation is a combination of the application of different components of the power system as well as the theoretical contribution, application of Microgrid structure to enhance the power system Big data analysis. Last of all an initiative was taken for Cyber physical platform for intrusion detection from PMU data in case of any attack.Item Failure Mode Analysis and Design Optimization of 15 kV SiC SGTO Thyristors for Pulsed Power Applications(2017-05) Bayne, Stephen; Giesselmann, Michael; Nutter, Brian; Bilbao, Argenis; Ogunniyi, AderintoSiC SGTO thyristors are an advanced solution for increasing the power density of high voltage pulsed power or power electronics. However, for these devices to supersede the established technologies, their long-term reliability and failure modes must be further understood. This dissertation presents an in-depth analysis of the long-term reliability and failure modes of 1 cm2 15 kV SiC SGTO thyristors during 2 kA 100 μs pulsed operation. The electrical failure modes are determined experimentally with an automated high energy pulsed power system, and the physical failure modes are determined through advanced IR and SEM-FIB microscopy techniques. Next, this dissertation presents the electro-thermal TCAD simulations of the 15 kV SiC SGTO switching 2 kA from a PFN. These simulations were performed in an effort to gain a deeper understanding of the device’s operation and failure modes. Based off the TCAD simulations, a theory of stacking fault formation is put forth as the underling physical mechanism that resulted in the devices’ degradation. The TCAD simulations revealed the buffer layer currently used is insufficiently thick for pulsed power applications. In addition, the design of the 15 kV SiC SGTOs is optimized through TCAD simulations to circumvent the hypothesized failure modes in future devices and to achieve faster switching capability.Item High voltage doping-less semiconductor devices(2022-05) Hahmady, Sara; Bayne, Stephen; Nutter, Brian; Li, ChangzhiIn this research, a novel approach is used for the rst time to design a high- voltage siliocon and silicon carbide p-i-n diodes without any chemical doping process of cathode and anode region. This approach favors \p" and \n" plasma region forma- tion through various metal contacts with appropriate work-functions for anode and cathode respectively. In this study, the forward and reverse characteristics, as well as the switching performance (reverse recovery) of these novel devices, charge plasma (CP) p-i-n diode, were compared with the Schottky diodes and the conventional p-i-n diode using TCAD simulation. This research leads to the comparable characteristics between the novel devices and the conventional p-i-n diodes.Item Large-scale adoption of grid-forming inverter controls to enable ancillary services modeling and evaluation for microgrids(2023-05) Ward, Laura; Bayne, Stephen; Chamana, Manohar; He, Miao; Li, ChangzhiHigh penetration of renewable energy sources (RES) operating as part of large interconnected systems can positively and negatively impact the security of the supply of electrical power systems. It is because there is no rule to determine the safe penetration level. Advanced power electronic technology protects the network from voltage instability and fluctuation, poor power factor, harmonics, DC bias, AC bus voltage magnitudes variation, and transient stability issues. Grid-forming (GFM) inverter control algorithms are applied in an interconnected system (IS) consisting of node regions with non-linear loads to cope with these issues. GFM inverter is a cost-effective device used to interface between the grid and RES, such as solar panels, wind turbines, and energy storage. It works autonomously to convert power in DC to AC form at the required frequency and voltage output by always-on universal droop control without external communication or phase-locked loops. This device needs a robust control strategy to face disturbances (e.g., grid voltage and frequency issues, blackouts). This research used a systematic methodology for characterizing the performance of the GFM power inverter control algorithms. This process was accomplished using an advanced real-time simulator tool called HYPERSIM developed by OPAL-RT for high penetration of renewable energy resources in the grid. These controllers are adopted, developed, and simulated step by step, scaling a test feeder with 120-inverters and 1200-node system. The newest and most mature controller consists of re-synchronization with the grid in 2 s and short circuits at specific times. The applicability of the developed control technology to other DERs is essential for commercialization. The GFM power inverter performance was validated in different feeders through extensive simulations in a real-time simulator tool. Simulation results show the scalable characteristic of the proposed adoption and keep voltage and frequency variation within specific ranges to protect the grid against voltage instability and fluctuation, an unsuitable frequency when operating in on-grid and off-grid and provide black-start capability in case of prolonged blackouts. The accuracy of the method was developed by comparing the voltages p.u. results obtained from the test utility feeders with the Open Distribution System Simulator (OpenDSS) results and with the GFM inverters was validated. The proposed approach seamlessly manages the data available from the GFM inverter control algorithm adoption through the final scalable control model verification available. The proposed approach seamlessly manages the data available from the optimization procedure through the final model evaluation.Item Large-scale adoption of self-synchronized universal droop controller-based inverters to enable ancillary services for different modes of distribution system operations(IEEE, 2020) Arce, Laura; Chamana, Manohar; Osman, Ilham; Ren, Beibei; Zhong, Qing-Chang; Bayne, StephenThis work proposes the large-scale adoption of self-synchronized universal droop controller (SUDC)-based inverters to enable ancillary services for different modes of distribution system operations. The IEEE 123 bus system was modeled on a real-time simulator to study the performance of large-scale adoption of SUDC inverters in a distribution system. The resulting data collected shows that the voltage and the frequency were regulated within ranges, such as less than 5% for voltage and less than 0.5% for frequency, under different load variations and grid operations. Also, the black start was achieved within 0.4 s without any voltage overshoot. Through the simulation and validation on a small microgrid and the IEEE 123 bus distribution system, it can be concluded that the SUDC was successfully adopted to regulate the voltage and the frequency within the given ranges, and black start achieved within 1 s without voltage overshoot for different modes of distribution system operations.Item Low-cost far-field and near-field radio frequency sensors for human sensing and liquid characterization(2022-05) Rodriguez, Daniel; Li, Changzhi; Saed, Mohammed; Bayne, StephenThis dissertation presents the theoretical analysis, system design, and experimental evaluation of low-cost far-field and near-field radio frequency sensors for human sensing and liquid characterization. The electromagnetic field characteristics of a radiating element are dependent of the measurement distance. The region furthest away from the radiating element is dominated by radiated electromagnetic fields and is called the far-field region. In this region, the radiation pattern is independent of the distance and the electric and magnetic fields are orthogonal to each other and to the direction of propagation, similar to plane waves. Portable far-field sensors are being widely studied and used due to their versatility in various applications such as vital signs detection, human tracking, gesture recognition, speech sensing, etc. Among far-field sensors, microwave radar systems are the most widely used. For instance, the deployment of these sensor has exponentially increased in recent years due to their high demand in the automotive sector. However, low-cost far-field sensors present special conditions and limitations. For instance, the most widely used architecture in microwave Doppler radar systems is the quadrature direct-conversion architecture (homodyne), since it provides lower hardware cost, less complexity, easier chip integration, and straightforward range correlation effect that mitigates the oscillator phase noise effect. Unfortunately, the quadrature direct-conversion receiver presents I/Q channels phase and amplitude imbalance that destroys the orthogonality of the received signal, causing a mirrored signal to appear superimposed to the desired signal. Additionally, the spectral components of slow motion and physiological signals are around DC, which makes them susceptible to 1/f noise in homodyne receivers. To address these issues, this dissertation analyzes the noise contribution and receiver link budget for the small-motion case based on circuits theory and signal processing methods used in small-motion detection. It is shown that the conventional link budget analysis used in communication systems is a pessimistic estimation for the small-motion interferometric case. In addition, it is shown that the radar range equation is not suitable for directly estimating the desired signal received power because of the generated harmonic tones in the detected signal. For the first time, the effect of the window functions on the spectrogram is discussed and introduced to the sensitivity calculation for the interferometric case. Additionally, a thorough distortion analysis is performed, showing the negligible effect of phase imbalance for small movements (i.e., compared with wavelength). A distortion correction method is proposed including a novel cross-correlation based phase imbalance correction technique. Furthermore, the advantages of the Welch’s estimator for small motion detection are analyzed experimentally and in simulation. Moreover, a sinusoidally modulated continuous wave (SMCW) far-field sensor is proposed. By periodically changing the sensor’s transmitted frequency, the target’s motion is successfully up-converted, effectively reducing the impact of the 1/f noise without affecting the range correlation effect (coherence). Finally, the feasibility of electronically spoofing far-field sensors with portable devices is studied. First, a simple binary phase-shift keying (BPSK) system is used to generate artificial spectral components in the reflected demodulated signal. Additionally, an analog phase shifter is driven by an arbitrary signal generator to mimic the human cardiorespiratory motion. The interest on low-power far- and near-field sensors with small form factor have increased in the last few years to fulfill the internet of things (IoT) requirements. Additionally, in order to make feasible the deployment of the high number of sensors demanded in the IoT era, it is imperative to pursue the design of ultra-low-cost on-board electronics. Therefore, it is important to reduce the number of radio frequency active devices in portable far- and near-field sensors. Therefore, a novel, low-cost, low-power 2-port monostatic far-field sensor suitable for massive fabrication and array implementation is proposed. Unlike conventional radar systems, this architecture does not use separate TX/RX signal chains, just counts on a single antenna for TX/RX and does not use a hybrid or circulator in the RF front-end. Besides the local oscillator, there is no other active device consuming any DC power, greatly reducing the power consumption and cost. Experimental results have verified the functionalities and demonstrated the potential for the proposed system to be utilized in human tracking and small motion detection. The region next to a radiating element is called the near-field region and it can be divided into two sub-regions called reactive and radiative near-field regions. The region closest to the radiating element forms the reactive near-field region. In this region, the radiation is negligible, and the electric and magnetic fields are 90 degrees out of phase, turning the field reactive. On the other hand, the radiative near-field region is where the transition of the electromagnetic field from reactive to radiative begins. To this end, this dissertation investigates a near-field sensor, which uses already existing hardware to realize new sensing functions with very few or no hardware added. The proposed system leverages the wireless power transfer (WPT) or near field communication (NFC) coil integrated in phones for liquid characterization. A circuit model for the beverage-sensor interaction is developed and the performance of features from different nature (e.g., magnitude, amplitude, phase) for machine learning based classification is analyzed and tested. The computational cost and radio frequency bandwidth needed for classification is reduced using box plot analysis, singular value decomposition, and histogram analysis without affecting the classification accuracy. Several experiments were carried out to verify the effectiveness of the proposed system. Experiments with fresh and spoiled milk showed that the proposed system can perform non-invasive beverage freshness detection, which is usually very difficult for the current existing methods (RFID tags, electrophysical and electrochemical measurements). Additionally, the non-invasive classification of liquid solutions with different concentrations is studied. Experiments with milk adulterated with different water volumes were carried out, milk concentrations of 100%, 80%, 60%, and 40% were used to train the machine learning classifiers, obtaining high classification accuracies.Item Novel control methods for intelligent power semiconductor modules(2021-05) Westmoreland, Braydon; Bayne, Stephen; Nutter, Brian; Bilbao, ArgenisIn high power applications, semiconductor power modules containing paralleled MOSFETs are often used to achieve high output currents. The current distribution between devices within a module is influenced by several factors such as component layout, minor variances due to manufacturing tolerances, and general device degradation that occurs over time. This thesis describes a method for balancing the current between paralleled MOSFETs by independently modulating each device’s gate-to-source voltage and measuring the corresponding drain-to-source current. To achieve this, a detailed simulation is created using MATLAB and Simulink. A reinforcement learning agent is implemented with the goal of adaptively balancing power module current as the components inside degrade over time. After extensively simulating different variants of the system along with various hyperparameter combinations, research transitioned to a physical system where similar successful results are achieved.Item Predictive analysis in power grid: A system-wide application of artificial intelligence(2020-08) Atique, Sharif Taufique; Bayne, Stephen; Giesselmann, Michael; He, MiaoA power grid comprises of generation, transmission and distribution components along various stakeholders. This dissertation is a study of artificial intelligence application throughout the power system. Forecasting of generation and demand play a key role in planning and operation of a power system and highly accurate prediction can result in large savings in large scale systems. In this dissertation, deep learning models have been explored in improving the accuracy on the two ends of the power system spectrum – generation and distribution. Renewable energy sources are gaining stronger foothold with every passing moment because of large supply and being environment friendly. Solar energy is the most popular among the renewable energy resources. However, solar energy, like most other renewable energy sources, are highly variable in nature as it is controlled by environment. So accurate solar energy forecasting is a challenging task as it can potentially change in an effervescent manner. Traditionally solar energy generation is forecasted using complex numeric weather prediction (NWP) models which depend on a lot of weather variables. In this work, solar generation has been forecasted using simple time series analysis and deep learning model RNN and LSTM outperform the other conventional methods and machine learning-based methods. Similarly, on the demand side, deep learning models trump traditional statistical time series model. These enhanced predictions are integrated to design a heuristic controller for a microgrid, and they successfully optimize the resource allocation in the microgrid. Today’s modern grid with improved communication infrastructure has opened the window of vulnerability to cyber-attacks. The reliability of a power grid now is susceptible to natural faults and new paradigm of cyber-attacks. Also, resiliency of the grid depends on successfully detecting and mitigating all types of anomalous events. This work establishes a benchmark for accomplishing this task using state-of-the-art machine learning techniques utilizing big data collected from PMU measurements. Sophisticated techniques like feature engineering, hyperparameter tuning are applied in improving performance, saving costs and tackling the curse of dimensionality. Overall, the studies conducted for this dissertation tackles the issues related to the power system from a modern perspective and can be used as a strong foundation to further improve the resiliency of the power grid.Item Reliability of 4H-SiC Devices Passivation Layer in High Humidity Environment for Power Electronic Applications(2023-08) Tsoi, Tsz (Jim); Bayne, Stephen; Giesselmann, Michael; Stephens, Jacob C.The 4H-SiC (Four Hexagon) devices create innovative technology for power electronic design and pulsed power applications to improve energy efficiency and reliability. The 4H-SiC material includes a higher bandgap and thermal conductivity than the current commercial Si material devices. The 4H-SiC material devices have become more suitable for high-power applications for long-term reliability. However, the 4H-SiC devices show a critical failure in the H3TRB (High Temperature, High Humidity, and High Field) test. This test places a semiconductor transistor in an extreme environmental condition to challenge a device’s long-term reliability. The passivation over the die inside the package and on top of the device becomes compromised by the chemical reaction of the water particles and the impurity’s containment from the package. The change of reaction creates a water tree line that would eventually become a short circuit across the surface of passivation. That could enhance serious long-term reliability issues for the wide-bandgap semiconductor device market. In this dissertation, the paper will cover the 4H-SiC TO-247 package devices’ failure mechanism from the H3TRB test and the physics simulation model for the failure mechanism. This paper divides into three parts of the research: background, devices’ test results, and simulation results. The first two chapters cover the introduction and background of the wide bandgap semiconductor materials, current 4H-SiC devices (Diode and MOSFET), static characterization on the devices, TO-247 package, passivation on the die, the H3TRB test industry standard and the COMSOL multiple level software backgrounds. The third chapter presents the 4H-SiC device results of the static characterization and internal layer of SEM images from the H3TRB test. The fourth chapter presents the semiconductor physics equations, 4H-SiC JBS diode design and simulation of the static characterization, and the field in the COMSOL multiple-level physic software. The fifth chapter covers the insulation physic equations, water tree description, and the simulation of the short circuit phenomenon in the COMSOL multiple-level software. Semiconductor covers the conclusion and the contribution in the semiconductor device reliability research in power electronic and pulsed power applications.Item Sizing PV and BESS for Grid-Connected Microgrid Resilience: A Data-Driven Hybrid Optimization Approach(2023) Murshed, Mahtab; Chamana, Manohar (TTU); Schmitt, Konrad Erich Kork; Pol, Suhas (TTU); Adeyanju, Olatunji (TTU); Bayne, StephenThis article presents a comprehensive data-driven approach on enhancing grid-connected microgrid grid resilience through advanced forecasting and optimization techniques in the context of power outages. Power outages pose significant challenges to modern societies, affecting various sectors such as industries, households, and critical infrastructures. The research combines statistical analysis, machine-learning algorithms, and optimization methods to address this issue to develop a holistic approach for predicting and mitigating power outage events. The proposed methodology involves the use of Monte Carlo simulations in MATLAB for future outage prediction, training a Long Short-Term Memory (LSTM) network for forecasting solar irradiance and load profiles with a dataset spanning from 2009 to 2018, and a hybrid LSTM-Particle Swarm Optimization (PSO) model to improve accuracy. Furthermore, the role of battery state of charge (SoC) in enhancing system resilience is explored. The study also assesses the techno-economic advantages of a grid-tied microgrid integrated with solar panels and batteries over conventional grid systems. The proposed methodology and optimization process demonstrate their versatility and applicability to a wide range of microgrid design scenarios comprising solar PV and battery energy storage systems (BESS), making them a valuable resource for enhancing grid resilience and economic efficiency across diverse settings. The results highlight the potential of the proposed approach in strengthening grid resilience by improving autonomy, reducing downtime by 25%, and fostering sustainable energy utilization by 82%.