ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
Displaying 1 - 9 of 9
Filtering by
- Creators: Pal, Anamitra
Description
The lifetime of a transformer is essentially determined by the life of its insulation
system which is a time function of the temperature defined by its thermal class. A large
quantity of studies and international standards have been published indicating the
possibility of increasing the thermal class of cellulose based materials when immersed
in natural esters which are superior to traditional mineral oils. Thus, a transformer
having thermally upgraded Kraft paper and natural ester dielectric fluid can be
classified as a high temperature insulation system. Such a transformer can also
operate at temperatures 20C higher than its mineral oil equivalent, holding additional
loading capability without losing life expectancy. This thesis focuses on evaluating
the use of this feature as an additional capability for enhancing the loadability and/or
extending the life of the distribution transformers for the Phoenix based utility - SRP
using FR3 brand natural ester dielectric fluid.
Initially, different transformer design options to use this additional loadability
are compared allowing utilities to select an optimal FR3 filled transformer design
for their application. Yearlong load profiles for SRP distribution transformers, sized
conventionally on peak load demands, are analyzed for their oil temperatures, winding
temperatures and loss of insulation life. It is observed that these load profiles can be
classified into two types: 1) Type-1 profiles with high peak and high average loads,
and 2) Type-2 profiles with comparatively low peak and low average load.
For the Type 1 load profiles, use of FR3 natural ester fluid with the same nominal
rating showed 7.4 times longer life expectation. For the Type 2 load profiles, a new
way of sizing ester filled transformers based on both average and peak load, instead of
only peak load, called “Sustainable Peak Loading” showed smaller size transformers
can handle the same yearly peak loads while maintaining superior insulation lifespan.
It is additionally possible to have reduction in the total energy dissipation over the
year. A net present value cost savings up to US$1200 per transformer quantifying
benefits of the life extension and the total ownership cost savings up to 30% for
sustainable peak loading showed SRP distribution transformers can gain substantial
economic savings when the distribution transformer fleet is replaced with FR3 ester
filled units.
system which is a time function of the temperature defined by its thermal class. A large
quantity of studies and international standards have been published indicating the
possibility of increasing the thermal class of cellulose based materials when immersed
in natural esters which are superior to traditional mineral oils. Thus, a transformer
having thermally upgraded Kraft paper and natural ester dielectric fluid can be
classified as a high temperature insulation system. Such a transformer can also
operate at temperatures 20C higher than its mineral oil equivalent, holding additional
loading capability without losing life expectancy. This thesis focuses on evaluating
the use of this feature as an additional capability for enhancing the loadability and/or
extending the life of the distribution transformers for the Phoenix based utility - SRP
using FR3 brand natural ester dielectric fluid.
Initially, different transformer design options to use this additional loadability
are compared allowing utilities to select an optimal FR3 filled transformer design
for their application. Yearlong load profiles for SRP distribution transformers, sized
conventionally on peak load demands, are analyzed for their oil temperatures, winding
temperatures and loss of insulation life. It is observed that these load profiles can be
classified into two types: 1) Type-1 profiles with high peak and high average loads,
and 2) Type-2 profiles with comparatively low peak and low average load.
For the Type 1 load profiles, use of FR3 natural ester fluid with the same nominal
rating showed 7.4 times longer life expectation. For the Type 2 load profiles, a new
way of sizing ester filled transformers based on both average and peak load, instead of
only peak load, called “Sustainable Peak Loading” showed smaller size transformers
can handle the same yearly peak loads while maintaining superior insulation lifespan.
It is additionally possible to have reduction in the total energy dissipation over the
year. A net present value cost savings up to US$1200 per transformer quantifying
benefits of the life extension and the total ownership cost savings up to 30% for
sustainable peak loading showed SRP distribution transformers can gain substantial
economic savings when the distribution transformer fleet is replaced with FR3 ester
filled units.
ContributorsVaidya, Chinmay Vishwas (Author) / Holbert, Keith E. (Thesis advisor) / Ayyanar, Raja (Committee member) / Pal, Anamitra (Committee member) / Arizona State University (Publisher)
Created2018
Description
After a major disturbance, the power system response is highly dependent on protection schemes and system dynamics. Improving power systems situational awareness requires proper and simultaneous modeling of both protection schemes and dynamic characteristics in power systems analysis tools. Historical information and ex-post analysis of blackouts reaffirm the critical role of protective devices in cascading events, thereby confirming the necessity to represent protective functions in transient stability studies. This dissertation is aimed at studying the importance of representing protective relays in power system dynamic studies. Although modeling all of the protective relays within transient stability studies may result in a better estimation of system behavior, representing, updating, and maintaining the protection system data becomes an insurmountable task. Inappropriate or outdated representation of the relays may result in incorrect assessment of the system behavior. This dissertation presents a systematic method to determine essential relays to be modeled in transient stability studies. The desired approach should identify protective relays that are critical for various operating conditions and contingencies. The results of the transient stability studies confirm that modeling only the identified critical protective relays is sufficient to capture system behavior for various operating conditions and precludes the need to model all of the protective relays. Moreover, this dissertation proposes a method that can be implemented to determine the appropriate location of out-of-step blocking relays. During unstable power swings, a generator or group of generators may accelerate or decelerate leading to voltage depression at the electrical center along with generator tripping. This voltage depression may cause protective relay mis-operation and unintentional separation of the system. In order to avoid unintentional islanding, the potentially mis-operating relays should be blocked from tripping with the use of out-of-step blocking schemes. Blocking these mis-operating relays, combined with an appropriate islanding scheme, help avoid a system wide collapse. The proposed method is tested on data from the Western Electricity Coordinating Council. A triple line outage of the California-Oregon Intertie is studied. The results show that the proposed method is able to successfully identify proper locations of out-of-step blocking scheme.
ContributorsHedman, Mojdeh Khorsand (Author) / Vittal, Vijay (Thesis advisor) / Ayyanar, Raja (Committee member) / Pal, Anamitra (Committee member) / Qin, Jiangchao (Committee member) / Arizona State University (Publisher)
Created2017
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
In order to meet the world’s growing energy need, it is necessary to create a reliable, robust, and resilient electric power grid. One way to ensure the creation of such a grid is through the extensive use of synchrophasor technology that is based on devices called phasor measurement units (PMUs), and their derivatives, such as μPMUs. Global positioning system (GPS) time-synchronized wide-area monitoring, protection, and control enabled by PMUs has opened up new ways in which the power grid can tackle the problems it faces today. However, with implementation of new technologies comes new challenges, and one of those challenges when it comes to PMUs is the misuse of GPS as a method to obtain a time reference.The use of GPS in PMUs is very intuitive as it is a convenient method to time stamp electrical signals, which in turn helps provide an accurate snapshot of the performance of the PMU-monitored section of the grid. However, GPS is susceptible to different types of signal interruptions due to natural (such as weather) or unnatural (jamming, spoofing) causes. The focus of this thesis is on demonstrating the practical feasibility of GPS spoofing attacks on PMUs, as well as developing novel countermeasures for them.
Prior research has demonstrated that GPS spoofing attacks on PMUs can cripple power system operation. The research conducted here first provides an experimental evidence of the feasibility of such an attack using commonly available digital radios known as software defined radio (SDR). Next, it introduces a new countermeasure against such attacks using GPS signal redundancy and low power long range (LoRa) spread spectrum modulation technique. The proposed approach checks the integrity of the GPS signal at remote locations and compares the data with the PMU’s current output. This countermeasure is a steppingstone towards developing a ready-to-deploy system that can provide an instant solution to the GPS spoofing detection problem for PMUs already placed in the power grid.
ContributorsSaadedeen, Fakhri G (Author) / Pal, Anamitra (Thesis advisor) / Sankar, Lalitha (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2021
Description
The past few years have witnessed a significant growth of distributed energy resources (DERs) in power systems at the customer level. Such growth challenges the traditional centralized model of conventional synchronous generation, making a transition to a decentralized network with a significant increase of DERs. This decentralized network requires a paradigm change in modeling distribution systems in more detail to maintain the reliability and efficiency while accommodating a high level of DERs. Accurate models of distribution feeders, including the secondary network, loads, and DER components must be developed and validated for system planning and operation and to examine the distribution system performance. In this work, a detailed model of an actual feeder with high penetration of DERs from an electrical utility in Arizona is developed. For the primary circuit, distribution transformers, and cables are modeled. For the secondary circuit, actual conductors to each house, as well as loads and photovoltaic (PV) units at each premise are represented. An automated tool for secondary network topology construction for load feeder topology assignation is developed. The automated tool provides a more accurate feeder topology for power flow calculation purposes. The input data for this tool consists of parcel geographic information system (GIS) delimitation data, and utility secondary feeder topology database. Additionally, a highly automated, novel method to enhance the accuracy of utility distribution feeder models to capture their performance by matching simulation results with corresponding field measurements is presented. The method proposed uses advanced metering infrastructure (AMI) voltage and derived active power measurements at the customer level, data acquisition systems (DAS) measurements at the feeder-head, in conjunction with an AC optimal power flow (ACOPF) to estimate customer active and reactive power consumption over a time horizon, while accounting for unmetered loads. The method proposed estimates both voltage magnitude and angle for each phase at the unbalanced distribution substation. The accuracy of the method developed by comparing the time-series power flow results obtained from the enhancement algorithm with OpenDSS results and with the field measurements available. The proposed approach seamlessly manages the data available from the optimization procedure through the final model verification.
ContributorsMontano-Martinez, Karen Vanessa (Author) / Vittal, Vijay (Thesis advisor) / Ayyanar, Raja (Committee member) / Weng, Yang (Committee member) / Pal, Anamitra (Committee member) / Arizona State University (Publisher)
Created2022
Description
With the continued increase in the amount of renewable generation in the formof distributed energy resources, reliability planning has progressively become a more
challenging task for the modern power system. This is because with higher penetration
of renewable generation, the system has to bear a higher degree of variability
and uncertainty. One way to address this problem is by generating realistic scenarios
that complement and supplement actual system conditions. This thesis presents
a methodology to create such correlated synthetic scenarios for load and renewable
generation using machine learning.
Machine learning algorithms need to have ample amounts of data available to
them for training purposes. However, real-world datasets are often skewed in the
distribution of the different events in the sample space. Data augmentation and
scenario generation techniques are often utilized to complement the datasets with
additional samples or by filling in missing data points. Datasets pertaining to the
electric power system are especially prone to having very few samples for certain
events, such as abnormal operating conditions, as they are not very common in an
actual power system. A recurrent generative adversarial network (GAN) model is
presented in this thesis to generate solar and load scenarios in a correlated manner
using an actual dataset obtained from a power utility located in the U.S. Southwest.
The generated solar and load profiles are verified both statistically and by implementation
on a simulated test system, and the performance of correlated scenario
generation vs. uncorrelated scenario generation is investigated. Given the interconnected
relationships between the variables of the dataset, it is observed that correlated
scenario generation results in more realistic synthetic scenarios, particularly for abnormal
system conditions. When combined with actual but scarce abnormal conditions,
the augmented dataset of system conditions provides a better platform for performing contingency studies for a more thorough reliability planning.
The proposed scenario generation method is scalable and can be modified to work
with different time-series datasets. Moreover, when the model is trained in a conditional
manner, it can be used to synthesise any number of scenarios for the different
events present in a given dataset. In summary, this thesis explores scenario generation
using a recurrent conditional GAN and investigates the benefits of correlated
generation compared to uncorrelated synthesis of profiles for the reliability planning
problem of power systems.
ContributorsBilal, Muhammad (Author) / Pal, Anamitra (Thesis advisor) / Holbert, Keith (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2022
Description
In recent years, there has been an increasing need for effective voltage controls in power systems due to the growing complexity and dynamic nature of practical power grid operations. Deep reinforcement learning (DRL) techniques now have been widely explored and applied to various electric power operation analyses under different control structures. With massive data available from phasor measurement units (PMU), it is possible to explore the application of DRL to ensure that electricity is delivered reliably.For steady-state power system voltage regulation and control, this study proposed a novel deep reinforcement learning (DRL) based method to provide voltage control that can quickly remedy voltage violations under different operating conditions. Multiple types of devices, adjustable voltage ratio (AVR) and switched shunts, are considered as controlled devices. A modified deep deterministic policy gradient (DDPG) algorithm is applied to accommodate both the continuous and discrete control action spaces of different devices. A case study conducted on the WECC 240-Bus system validates the effectiveness of the proposed method.
System dynamic stability and performance after serious disturbances using DRL are further discussed in this study. A real-time voltage control method is proposed based on DRL, which continuously regulates the excitation system in response to system disturbances. Dynamic performance is considered by incorporating historical voltage data, voltage rate of change, voltage deviation, and regulation amount. A versatile transmission-level power system dynamic training and simulation platform is developed by integrating the simulation software PSS/E and a user-written DRL agent code developed in Python. The platform developed facilitates the training and testing of various power system algorithms and power grids in dynamic simulations with all the modeling capabilities available within PSS/E. The efficacy of the proposed method is evaluated based on the developed platform.
To enhance the controller's resilience in addressing communication failures, a dynamic voltage control method employing the Multi-agent DDPG algorithm is proposed. The algorithm follows the principle of centralized training and decentralized execution. Each agent has independent actor neural networks and critic neural networks. Simulation outcomes underscore the method’s efficacy, showcasing its capability in providing voltage support and handling communication failures among agents.
ContributorsWang, Yuling (Author) / Vittal, Vijay (Thesis advisor) / Ayyanar, Raja (Committee member) / Pal, Anamitra (Committee member) / Hedman, Mojdeh (Committee member) / Arizona State University (Publisher)
Created2024
Description
Energy is one of the wheels on which the modern world runs. Therefore, standards and limits have been devised to maintain the stability and reliability of the power grid. This research shows a simple methodology for increasing the amount of Inverter-based Renewable Generation (IRG), which is also known as Inverter-based Resources (IBR), for that considers the voltage and frequency limits specified by the Western Electricity Coordinating Council (WECC) Transmission Planning (TPL) criteria, and the tie line power flow limits between the area-under-study and its neighbors under contingency conditions. A WECC power flow and dynamic file is analyzed and modified in this research to demonstrate the performance of the methodology. GE's Positive Sequence Load Flow (PSLF) software is used to conduct this research and Python was used to analyze the output data.
The thesis explains in detail how the system with 11% of IRG operated before conducting any adjustments (addition of IRG) and what procedures were modified to make the system run correctly. The adjustments made to the dynamic models are also explained in depth to give a clearer picture of how each adjustment affects the system performance. A list of proposed IRG units along with their locations were provided by SRP, a power utility in Arizona, which were to be integrated into the power flow and dynamic files. In the process of finding the maximum IRG penetration threshold, three sensitivities were also considered, namely, momentary cessation due to low voltages, transmission vs. distribution connected solar generation, and stalling of induction motors. Finally, the thesis discusses how the system reacts to the aforementioned modifications, and how IRG penetration threshold gets adjusted with regards to the different sensitivities applied to the system.
The thesis explains in detail how the system with 11% of IRG operated before conducting any adjustments (addition of IRG) and what procedures were modified to make the system run correctly. The adjustments made to the dynamic models are also explained in depth to give a clearer picture of how each adjustment affects the system performance. A list of proposed IRG units along with their locations were provided by SRP, a power utility in Arizona, which were to be integrated into the power flow and dynamic files. In the process of finding the maximum IRG penetration threshold, three sensitivities were also considered, namely, momentary cessation due to low voltages, transmission vs. distribution connected solar generation, and stalling of induction motors. Finally, the thesis discusses how the system reacts to the aforementioned modifications, and how IRG penetration threshold gets adjusted with regards to the different sensitivities applied to the system.
ContributorsAlbhrani, Hashem A M H S (Author) / Pal, Anamitra (Thesis advisor) / Holbert, Keith E. (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2020
Description
The broad deployment of time-synchronized continuous point-on-wave (CPoW) modules will enable electric power utilities to gain unprecedented insight into the behavior of their power system assets, loads, and distributed renewable generation in real time. By increasing the available level of detail visible to operators, serious fault events such as wildfire-inducing arc flashes, safety-jeopardizing transformer failures, and equipment-damaging power quality decline can be mitigated in a data-driven, systematic manner. In this research project, a time-synchronized micro-scale CPoW module was designed, constructed, and characterized. This inductively powered CPoW module, which operates wirelessly by using the current flowing through a typical distribution conductor as its power source and a wireless data link for communication, has been configured to measure instantaneous line current at high frequency (nominally 3,000 samples per second) with 12-bit resolution. The design process for this module is detailed in this study, including background research, individual block design and testing, printed circuit board (PCB) design, and final characterization of the system.
To validate the performance of this module, tests of power requirements, measurement accuracy, battery life, susceptibility to electromagnetic interference, and fault detection performance were performed. The results indicate that the design under investigation will satisfy the technical and physical constraints required for bulk deployment in an actual distribution network after manufacturing optimizations. After the test results were summarized, the future research and development activities needed to finalize this design for commercial deployment were identified and discussed.
ContributorsPatterson, John (Author) / Pal, Anamitra (Thesis advisor) / Ogras, Umit (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2021