Matching Items (35)
Description
Large-scale blackouts that have occurred across North America in the past few decades have paved the path for substantial amount of research in the field of security assessment of the grid. With the aid of advanced technology such as phasor measurement units (PMUs), considerable work has been done involving voltage stability analysis and power system dynamic behavior analysis to ensure security and reliability of the grid. Online dynamic security assessment (DSA) analysis has been developed and applied in several power system control centers. Existing applications of DSA are limited by the assumption of simplistic load profiles, which often considers a normative day to represent an entire year. To overcome these aforementioned challenges, this research developed a novel DSA scheme to provide security prediction in real-time for load profiles corresponding to different seasons. The major contributions of this research are to (1) develop a DSA scheme incorporated with PMU data, (2) consider a comprehensive seasonal load profile, (3) account for varying penetrations of renewable generation, and (4) compare the accuracy of different machine learning (ML) algorithms for DSA. The ML algorithms that will be the focus of this study include decision trees (DTs), support vector machines (SVMs), random forests (RFs), and multilayer neural networks (MLNNs).
This thesis describes the development of a novel DSA scheme using synchrophasor measurements that accounts for the load variability occurring across different seasons in a year. Different amounts of solar generation have also been incorporated in this study to account for increasing percentage of renewables in the modern grid. To account for the security of the operating conditions different ML algorithms have been trained and tested. A database of cases for different operating conditions has been developed offline that contains secure as well as insecure cases, and the ML models have been trained to classify the security or insecurity of a particular operating condition in real-time. Multiple scenarios are generated every 15 minutes for different seasons and stored in the database. The performance of this approach is tested on the IEEE-118 bus system.
This thesis describes the development of a novel DSA scheme using synchrophasor measurements that accounts for the load variability occurring across different seasons in a year. Different amounts of solar generation have also been incorporated in this study to account for increasing percentage of renewables in the modern grid. To account for the security of the operating conditions different ML algorithms have been trained and tested. A database of cases for different operating conditions has been developed offline that contains secure as well as insecure cases, and the ML models have been trained to classify the security or insecurity of a particular operating condition in real-time. Multiple scenarios are generated every 15 minutes for different seasons and stored in the database. The performance of this approach is tested on the IEEE-118 bus system.
ContributorsNATH, ANUBHAV (Author) / Pal, Anamitra (Thesis advisor) / Holbert, Keith (Committee member) / Wu, Meng (Committee member) / Arizona State University (Publisher)
Created2019
Description
With the increasing penetration of converter interfaced renewable generation into power systems, the structure and behavior of the power system is changing, catalyzing alterations and enhancements in modeling and simulation methods.
This work puts forth a Hybrid Electromagnetic Transient-Transient Stability simulation method implemented using MATLAB and Simulink, to study power electronic based power systems. Hybrid Simulation enables detailed, accurate modeling, along with fast, efficient simulation, on account of the Electromagnetic Transient (EMT) and Transient Stability (TS) simulations respectively. A critical component of hybrid simulation is the interaction between the EMT and TS simulators, established through a well-defined interface technique, which has been explored in detail.
This research focuses on the boundary conditions and interaction between the two simulation models for optimum accuracy and computational efficiency.
A case study has been carried out employing the proposed hybrid simulation method. The test case used is the IEEE 9-bus system, modified to integrate it with a solar PV plant. The validation of the hybrid model with the benchmark full EMT model, along with the analysis of the accuracy and efficiency, has been performed. The steady-state and transient analysis results demonstrate that the performance of the hybrid simulation method is competent. The hybrid simulation technique suitably captures accuracy of EMT simulation and efficiency of TS simulation, therefore adequately representing the behavior of power systems with high penetration of converter interfaced generation.
This work puts forth a Hybrid Electromagnetic Transient-Transient Stability simulation method implemented using MATLAB and Simulink, to study power electronic based power systems. Hybrid Simulation enables detailed, accurate modeling, along with fast, efficient simulation, on account of the Electromagnetic Transient (EMT) and Transient Stability (TS) simulations respectively. A critical component of hybrid simulation is the interaction between the EMT and TS simulators, established through a well-defined interface technique, which has been explored in detail.
This research focuses on the boundary conditions and interaction between the two simulation models for optimum accuracy and computational efficiency.
A case study has been carried out employing the proposed hybrid simulation method. The test case used is the IEEE 9-bus system, modified to integrate it with a solar PV plant. The validation of the hybrid model with the benchmark full EMT model, along with the analysis of the accuracy and efficiency, has been performed. The steady-state and transient analysis results demonstrate that the performance of the hybrid simulation method is competent. The hybrid simulation technique suitably captures accuracy of EMT simulation and efficiency of TS simulation, therefore adequately representing the behavior of power systems with high penetration of converter interfaced generation.
ContributorsAthaide, Denise Maria Christine (Author) / Qin, Jiangchao (Thesis advisor) / Ayyanar, Raja (Committee member) / Wu, Meng (Committee member) / Arizona State University (Publisher)
Created2018
Description
High Voltage Direct Current (HVDC) Technology has several features that make it particularly attractive for specific transmission applications. Recent years have witnessed an unprecedented growth in the number of the HVDC projects, which demonstrates a heightened interest in the HVDC technology. In parallel, the use of renewable energy sources has dramatically increased. For instance, Kuwait has recently announced a renewable project to be completed in 2035; this project aims to produce 15% of the countrys energy consumption from renewable sources. However, facilities that use renewable sources, such as solar and wind, to provide clean energy, are mostly placed in remote areas, as their installation requires a massive space of free land. Consequently, considerable challenges arise in terms of transmitting power generated from renewable sources of energy in remote areas to urban areas for further consumption.
The present thesis investigates different transmission line systems for transmitting bulk energy from renewable sources. Specifically, two systems will be focused on: the high-voltage alternating current (HVAC) system and the high-voltage direct current (HVDC) system. In order to determine the most efficient way of transmitting bulk energy from renewable sources, different aspects of the aforementioned two types of systems are analyzed. Limitations inherent in both HVAC and HVDC systems have been discussed.
At present, artificial intelligence plays an important role in power system control and monitoring. Consequently, in this thesis, the fault issue has been analyzed in transmission systems, with a specific consideration of machine learning tools that can help monitor transmission systems by detecting fault locations. These tools, called models, are used to analyze the collected data. In the present thesis, a focus on such models as linear regression (LR), K-nearest neighbors (KNN), linear support vector machine (LSVM) , and adaptive boost (AdaBoost). Finally, the accuracy of each model is evaluated and discussed. The machine learning concept introduced in the present thesis lays down the foundation for future research in this area so that to enable further research on the efficient ways to improve the performance of transmission line components and power systems.
The present thesis investigates different transmission line systems for transmitting bulk energy from renewable sources. Specifically, two systems will be focused on: the high-voltage alternating current (HVAC) system and the high-voltage direct current (HVDC) system. In order to determine the most efficient way of transmitting bulk energy from renewable sources, different aspects of the aforementioned two types of systems are analyzed. Limitations inherent in both HVAC and HVDC systems have been discussed.
At present, artificial intelligence plays an important role in power system control and monitoring. Consequently, in this thesis, the fault issue has been analyzed in transmission systems, with a specific consideration of machine learning tools that can help monitor transmission systems by detecting fault locations. These tools, called models, are used to analyze the collected data. In the present thesis, a focus on such models as linear regression (LR), K-nearest neighbors (KNN), linear support vector machine (LSVM) , and adaptive boost (AdaBoost). Finally, the accuracy of each model is evaluated and discussed. The machine learning concept introduced in the present thesis lays down the foundation for future research in this area so that to enable further research on the efficient ways to improve the performance of transmission line components and power systems.
ContributorsAlbannai, Bassam Ahmad (Author) / Weng, Yang (Thesis advisor) / Wu, Meng (Committee member) / Dahal, Som (Committee member) / Arizona State University (Publisher)
Created2019
Description
Pacemakers in the early 1970s were powered by betavoltaic devices which provided long lasting battery life. The betavoltaic devices also emitted gamma radiation due to inadvertent radioisotope contamination, which could not be completely shielded. The betavoltaic devices were quickly replaced by lithium batteries after their invention, and betavoltaics were abandoned. Modern technological advancements made it possible to isolate beta emitting radioisotopes properly and achieve better energy conversion efficiencies which revived the topic of betavoltaics. This research project has studied state-of-the-art pacemakers and modern radioactive power sources in order to determine if modern pacemakers can be safely nuclear powered and if that is a reasonable combination.
ContributorsAwad, Al-Homam Abdualrahman (Author) / Holbert, Keith (Thesis director) / Aberle, James (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2014-12
Description
The intention of this report is to use computer simulations to investigate the viability of two materials, water and polyethylene, as shielding against space radiation. First, this thesis discusses some of the challenges facing future and current manned space missions as a result of galactic cosmic radiation, or GCR. The project then uses MULASSIS, a Geant4 based radiation simulation tool, to analyze the effectiveness of water and polyethylene based radiation shields against proton radiation with an initial energy of 1 GeV. This specific spectrum of radiation is selected because it a component of GCR that has been shown by previous literature to pose a significant threat to humans on board spacecraft. The analysis of each material indicated that both would have to be several meters thick to adequately protect crew against the simulated radiation over a several year mission. Additionally, an analysis of the mass of a simple spacecraft model with different shield thicknesses showed that the mass would increase significantly with internal space. Thus, using either material as a shield would be expensive as a result of the cost of lifting a large amount of mass into orbit.
ContributorsBonfield, Maclain Peter (Author) / Holbert, Keith (Thesis director) / Young, Patrick (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
As more electric vehicles (EVs) are adopted, users need a solution to meet their expectations when it comes to Level 2 EV Charging (EVC). Currently, Adaptive Charging (AC) Techniques are used in multi-unit, public, settings. In the future, AC should be utilized to provide an optimized charging experience for the EV user in a single-unit residential application. In this experiment, an Electric Vehicle simulation tool was created using Python. A training dataset was generated from Alternative Fuels and Data Center (EVI-Pro) using charging data from Phoenix, Arizona. Similarly, the utility price plan chosen for this exercise was SRP Electric Vehicle Price plan. This will be the cost-basis for the thesis. There were four cases that were evaluated by the simulation tool. (1) Utility Guided Scheduling (2) Automatic Scheduling (3) Off-Site Enablement (4) Bidirectional enablement. These use-cases are some of the critical problems facing EV users when it comes to charging at home. Each of these scenarios and algorithms were proven to save the user money in their daily bill. Overall, the user will need some sort of weighted scenario that considers all four cases to provide the best solution to the user. All four scenarios support the use of Adaptive Charging techniques in residential level 2 electric vehicle chargers. By applying these techniques, the user can save up to 90% on their energy bill while offsetting the energy grid during peak hours. The adaptive charging techniques applied in this thesis are critical to the adoption of the next generation electric vehicles. Users need to be enabled to use the latest and greatest technology. In the future, individuals can use this report as a baseline to use an Artificial Intelligence model to make an educated case-by-case decision to deal with the variability of the data.
ContributorsSnyder, Jack (Author) / Wu, Meng (Thesis director) / Walsh, Stephanie (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2023-05
ContributorsSnyder, Jack (Author) / Wu, Meng (Thesis director) / Walsh, Stephanie (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2023-05
Description
The penetration of renewable energy in the power system has grown considerably in the past few years. While this use may come with an abundance of advantages, it also introduces new challenges in operating the 100+ years old electrical network. Fundamentally, the power system relies on a real-time balance of generation and demand. However, renewable resources such as solar and wind farms are not available throughout the day. Furthermore, they introduce temporal variability to the generation process due to metrological factors, making the balance of generation and demand precarious. Utilities use standby units with reserve power and high ramp-up, ramp-down capabilities to ensure balance. However, such solutions can be very costly. An accurate scenario generation and forecasting of the stochastic variables (load and renewable resources) can help reduce the cost of these solutions. The goal of this research is to solve the scenario generation and forecasting problems using state-of-the-art machine learning techniques and algorithms. The training database is created using publicly available data obtained from NREL and the Texas-2000 bus system. The IEEE-30 bus system is used as the test system for the analysis conducted here. The conventional generators of this system are replaced with solar farms and wind farms. The ability of four machine learning algorithms in addressing the scenario generation and forecasting problems are investigated using appropriate metrics.
The first machine learning algorithm is the convolutional neural network (CNN). It is found to be well-suited for the scenario generation problem. However, its inability to capture certain intricate details about the different variables was identified as a possible drawback. The second algorithm is the long-short term memory-variational auto-encoder (LSTM-VAE). It generated scenarios that are very similar to the actual scenarios indicating that it is suitable for solving the forecasting problem. The third algorithm is the conditional generative adversarial network (C-GAN). It was extremely effective in generating scenarios when the number of variables were small. However, its scalability was found to be a concern. The fourth algorithm is the spatio-temporal graph convolutional network (STGCN). It was found to generate representative correlated scenarios effectively.
ContributorsAlhazmi, Mohammed Ahmed (Author) / Pal, Anamitra (Thesis advisor) / Ayyanar, Raja (Committee member) / Holbert, Keith (Committee member) / Arizona State University (Publisher)
Created2021
Description
The fast growth of the power system industry and the increase in the usage of computerized management systems introduces more complexities to power systems operations. Although these computerized management systems help system operators manage power systems reliably and efficiently, they introduce the threat of cyber-attacks. In this regard, this dissertation focuses on the load-redistribution (LR) attacks, which cause overflows in power systems. Previous researchers have shown the possibility of launching undetectable LR attacks against power systems, even when protection schemes exist. This fact pushes researchers to develop detection mechanisms. In this thesis, real-time detection mechanisms are developed based on the fundamental knowledge of power systems, operation research, and machine learning. First, power systems domain insight is used to identify an underlying exploitable structure for the core problem of LR attacks. Secondly, a greedy algorithm’s ability to solve the identified structure to optimality is proved, which helps operators quickly find the best attack vector and the most sensitive buses for each target transmission asset. Then, two quantitative security indices are proposed and leveraged to develop a measurement threat analysis (MTA) tool. Finally, a machine learning-based classifier is used to enhance the MTA tool’s functionality in flagging tiny LR attacks and distinguishing them from measurement/forecasting errors. On the other hand, after acknowledging that an adversarial LR attack interferes with the system, establishing a corrective action is imperative to mitigate or remove the potential consequences of the attack. This dissertation proposes two corrective actions; the first one is developed based on the worst-case attack scenario, considering the information provided by the MTA tool. After The MTA tool flags an LR attack in the system, it should determine the primary target and other affected transmission assets, using which the operator can estimate the actual loads in the post-attack stage. This estimation is essential since the corresponding security constraints in the first corrective action model are modeled based on these loads. The second one is a robust optimization that considers various load scenarios. The functionality of this robust model does not depend on the information provided by the MTA tool and is more reliable.
ContributorsKaviani, Ramin (Author) / Hedman, Kory (Thesis advisor) / Vittal, Vijay (Committee member) / Hedman, Mojdeh (Committee member) / Wu, Meng (Committee member) / Arizona State University (Publisher)
Created2022
Description
Information about the elemental composition of a planetary surface can be determined using nuclear instrumentation such as gamma-ray and neutron spectrometers (GRNS). High-energy Galactic Cosmic Rays (GCRs) resulting from cosmic super novae isotropically bombard the surfaces of planetary bodies in space. When GCRs interact with a body’s surface, they can liberate neutrons in a process called spallation, resulting in neutrons and gamma rays being emitted from the planet’s surface; how GCRs and source particles (i.e. active neutron generators) interact with nearby nuclei defines the nuclear environment. In this work I describe the development of nuclear detection systems and techniques for future orbital and landed missions, as well as the implications of nuclear environments on a non-silicate (icy) planetary body. This work aids in the development of future NASA and international missions by presenting many of the capabilities and limitations of nuclear detection systems for a variety of planetary bodies (Earth, the Moon, metallic asteroids, icy moons). From bench top experiments to theoretical simulations, from geochemical hypotheses to instrument calibrations—nuclear planetary science is a challenging and rapidly expanding multidisciplinary field. In this work (1) I describe ground-truth verification of the neutron die-away method using a new type of elpasolite (Cs2YLiCl6:Ce) scintillator, (2) I explore the potential use of temporal neutron measurements on the surface of Titan through Monte-Carlo simulation models, and (3) I report on the experimental spatial efficiency and calibration details of the miniature neutron spectrometer (Mini-NS) on board the NASA LunaH-Map mission. This work presents a subset of planetary nuclear science and its many challenges in humanity's ongoing effort to explore strange new worlds.
ContributorsHeffern, Lena Elizabeth (Author) / Hardgrove, Craig (Thesis advisor) / Elkins-Tanton, Linda (Committee member) / Parsons, Ann (Committee member) / Garvie, Laurence (Committee member) / Holbert, Keith (Committee member) / Lyons, James (Committee member) / Arizona State University (Publisher)
Created2022