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.

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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

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.
ContributorsVaidya, Chinmay Vishwas (Author) / Holbert, Keith E. (Thesis advisor) / Ayyanar, Raja (Committee member) / Pal, Anamitra (Committee member) / Arizona State University (Publisher)
Created2018
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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

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
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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

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
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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

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