Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
Displaying 1 - 10 of 25
Filtering by
- Genre: Doctoral Dissertation
Description
Polymeric materials containing nanometer (nm) size particles are being introduced to provide compact shapes for low and medium voltage insulation equipment. The nanocomposites may provide superior electrical performance when compared with those available currently, such as lower dielectric losses and increased dielectric strength, tracking and erosion resistance, and surface hydrophobicity. All of the above mentioned benefits can be achieved at a lower filler concentration (< 10%) than conventional microfillers (40-60%). Also, the uniform shapes of nanofillers provide a better electrical stress distribution as compared to irregular shaped microcomposites which can have high internal electric stress, which could be a problem for devices with active electrical parts. Improvement in electrical performance due to addition of nanofillers in an epoxy matrix has been evaluated in this work. Scanning Electron Microscopy (SEM) was done on the epoxy samples to confirm uniform dispersion of nano-sized fillers as good filler dispersion is essential to realize the above stated benefits. Dielectric spectroscopy experiments were conducted over a wide range of frequencies as a function of temperature to understand the role of space charge and interfaces in these materials. The experiment results demonstrate significant reduction in dielectric losses in samples containing nanofillers. High voltage experiments such as corona resistance tests were conducted over 500 hours to monitor degradation in the samples due to corona. These tests revealed improvements in partial discharge endurance of nanocomposite samples. These improvements could not be adequately explained using a macroscopic quantity such as thermal conductivity. Thermo gravimetric analysis (TGA) showed higher weight loss initiation temperatures for nanofilled samples which is in agreement with the corona resistance experimental results. Theoretical models have also been developed in this work to complement the results of the corona resistance experiment and the TGA analysis. Degradation model was developed to map the erosion path using Dijkstra's shortest path algorithm. A thermal model was developed to calculate the localized temperature distribution in the micro and nano-filled samples using the PDE toolbox in MATLAB. Both the models highlight the fact that improvement in nanocomposites is not limited to the filler concentrations that were tested experimentally.
ContributorsIyer, Ganpathy (Author) / Gorur, Ravi S (Thesis advisor) / Vittal, Vijay (Committee member) / Richert, Ranko (Committee member) / Karady, George G. (Committee member) / Arizona State University (Publisher)
Created2012
Description
In the deregulated power system, locational marginal prices are used in transmission engineering predominantly as near real-time pricing signals. This work extends this concept to distribution engineering so that a distribution class locational marginal price might be used for real-time pricing and control of advanced control systems in distribution circuits. A formulation for the distribution locational marginal price signal is presented that is based on power flow sensitivities in a distribution system. A Jacobian-based sensitivity analysis has been developed for application in the distribution pricing method. Increasing deployment of distributed energy sources is being seen at the distribution level and this trend is expected to continue. To facilitate an optimal use of the distributed infrastructure, the control of the energy demand on a feeder node in the distribution system has been formulated as a multiobjective optimization problem and a solution algorithm has been developed. In multiobjective problems the Pareto optimality criterion is generally applied, and commonly used solution algorithms are decision-based and heuristic. In contrast, a mathematically-robust technique called normal boundary intersection has been modeled for use in this work, and the control variable is solved via separable programming. The Roy Billinton Test System (RBTS) has predominantly been used to demonstrate the application of the formulation in distribution system control. A parallel processing environment has been used to replicate the distributed nature of controls at many points in the distribution system. Interactions between the real-time prices in a distribution feeder and the nodal prices at the aggregated load bus have been investigated. The application of the formulations in an islanded operating condition has also been demonstrated. The DLMP formulation has been validated using the test bed systems and a practical framework for its application in distribution engineering has been presented. The multiobjective optimization yields excellent results and is found to be robust for finer time resolutions. The work shown in this report is applicable to, and has been researched under the aegis of the Future Renewable Electric Energy Delivery and Management (FREEDM) center, which is a generation III National Science Foundation engineering research center headquartered at North Carolina State University.
ContributorsRanganathan Sathyanarayana, Bharadwaj (Author) / Heydt, Gerald T (Thesis advisor) / Vittal, Vijay (Committee member) / Ayyanar, Raja (Committee member) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2012
Description
The Smart Grid initiative describes the collaborative effort to modernize the U.S. electric power infrastructure. Modernization efforts incorporate digital data and information technology to effectuate control, enhance reliability, encourage small customer sited distributed generation (DG), and better utilize assets. The Smart Grid environment is envisioned to include distributed generation, flexible and controllable loads, bidirectional communications using smart meters and other technologies. Sensory technology may be utilized as a tool that enhances operation including operation of the distribution system. Addressing this point, a distribution system state estimation algorithm is developed in this thesis. The state estimation algorithm developed here utilizes distribution system modeling techniques to calculate a vector of state variables for a given set of measurements. Measurements include active and reactive power flows, voltage and current magnitudes, phasor voltages with magnitude and angle information. The state estimator is envisioned as a tool embedded in distribution substation computers as part of distribution management systems (DMS); the estimator acts as a supervisory layer for a number of applications including automation (DA), energy management, control and switching. The distribution system state estimator is developed in full three-phase detail, and the effect of mutual coupling and single-phase laterals and loads on the solution is calculated. The network model comprises a full three-phase admittance matrix and a subset of equations that relates measurements to system states. Network equations and variables are represented in rectangular form. Thus a linear calculation procedure may be employed. When initialized to the vector of measured quantities and approximated non-metered load values, the calculation procedure is non-iterative. This dissertation presents background information used to develop the state estimation algorithm, considerations for distribution system modeling, and the formulation of the state estimator. Estimator performance for various power system test beds is investigated. Sample applications of the estimator to Smart Grid systems are presented. Applications include monitoring, enabling demand response (DR), voltage unbalance mitigation, and enhancing voltage control. Illustrations of these applications are shown. Also, examples of enhanced reliability and restoration using a sensory based automation infrastructure are shown.
ContributorsHaughton, Daniel Andrew (Author) / Heydt, Gerald T (Thesis advisor) / Vittal, Vijay (Committee member) / Ayyanar, Raja (Committee member) / Hedman, Kory W (Committee member) / Arizona State University (Publisher)
Created2012
Description
Electric power systems are facing great challenges from environmental regulations, changes in demand due to new technologies like electric vehicle, as well as the integration of various renewable energy sources. These factors taken together require the development of new tools to help make policy and investment decisions for the future power grid. The requirements of a network equivalent to be used in such planning tools are very different from those assumed in the development of traditional equivalencing procedures. This dissertation is focused on the development, implementation and verification of two network equivalencing approaches on large power systems, such as the Eastern Interconnection. Traditional Ward-type equivalences are a class of equivalencing approaches but this class has some significant drawbacks. It is well known that Ward-type equivalents "smear" the injections of external generators over a large number of boundary buses. For newer long-term investment applications that take into account such things as greenhouse gas (GHG) regulations and generator availability, it is computationally impractical to model fractions of generators located at many buses. A modified-Ward equivalent is proposed to address this limitation such that the external generators are moved wholesale to some internal buses based on electrical distance. This proposed equivalencing procedure is designed so that the retained-line power flows in the equivalent match those in the unreduced (full) model exactly. During the reduction process, accommodations for special system elements are addressed, including static VAr compensators (SVCs), high voltage dc (HVDC) transmission lines, and phase angle regulators. Another network equivalencing approach based on the dc power flow assumptions and the power transfer distribution factors (PTDFs) is proposed. This method, rather than eliminate buses via Gauss-reduction, aggregates buses on a zonal basis. The bus aggregation approach proposed here is superior to the existing bus aggregation methods in that a) under the base case, the equivalent-system inter-zonal power flows exactly match those calculated using the full-network-model b) as the operating conditions change, errors in line flows are reduced using the proposed bus clustering algorithm c) this method is computationally more efficient than other bus aggregation methods proposed heretofore. A critical step in achieving accuracy with a bus aggregation approach is selecting which buses to cluster together and how many clusters are needed. Clustering in this context refers to the process of partitioning a network into subsets of buses. An efficient network clustering method is proposed based on the PTDFs and the data mining techniques. This method is applied to the EI topology using the "Saguaro" supercomputer at ASU, a resource with sufficient memory and computational capability for handling this 60,000-bus and 80,000-branch system. The network equivalents generated by the proposed approaches are verified and tested for different operating conditions and promising results have been observed.
ContributorsShi, Di (Author) / Tylavsky, Daniel J (Thesis advisor) / Vittal, Vijay (Committee member) / Hedman, Kory (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2012
Description
Ensuring reliable operation of large power systems subjected to multiple outages is a challenging task because of the combinatorial nature of the problem. Traditional methods of steady-state security assessment in power systems involve contingency analysis based on AC or DC power flows. However, power flow based contingency analysis is not fast enough to evaluate all contingencies for real-time operations. Therefore, real-time contingency analysis (RTCA) only evaluates a subset of the contingencies (called the contingency list), and hence might miss critical contingencies that lead to cascading failures.This dissertation proposes a new graph-theoretic approach, called the feasibility test (FT) algorithm, for analyzing whether a contingency will create a saturated or over-loaded cut-set in a meshed power network; a cut-set denotes a set of lines which if tripped separates the network into two disjoint islands. A novel feature of the proposed approach is that it lowers the solution time significantly making the approach viable for an exhaustive real-time evaluation of the system. Detecting saturated cut-sets in the power system is important because they represent the vulnerable bottlenecks in the network. The robustness of the FT algorithm is demonstrated on a 17,000+ bus model of the Western Interconnection (WI).
Following the detection of post-contingency cut-set saturation, a two-component methodology is proposed to enhance the reliability of large power systems during a series of outages. The first component combines the proposed FT algorithm with RTCA to create an integrated corrective action (iCA), whose goal is to secure the power system against post-contingency cut-set saturation as well as critical branch overloads. The second component only employs the results of the FT to create a relaxed corrective action (rCA) that quickly secures the system against saturated cut-sets.
The first component is more comprehensive than the second, but the latter is computationally more efficient. The effectiveness of the two components is evaluated based upon the number of cascade triggering contingencies alleviated, and the computation time. Analysis of different case-studies on the IEEE 118-bus and 2000-bus synthetic Texas systems indicate that the proposed two-component methodology enhances the scope and speed of power system security assessment during multiple outages.
ContributorsSen Biswas, Reetam (Author) / Pal, Anamitra (Thesis advisor) / Vittal, Vijay (Committee member) / Undrill, John (Committee member) / Wu, Meng (Committee member) / Zhang, Yingchen (Committee member) / Arizona State University (Publisher)
Created2021
Description
Due to the new and old challenges, modern-day market management systems continue to evolve, including market reformulations, introducing new market products, and proposing new frameworks for integrating distributed energy resources (DERs) into the wholesale markets. Overall, questions is regarding how to reflect these essential changes in the market models (design, reformulation, and coordination frameworks), design market-based incentive structures to adequately compensate participants for providing ancillary services, and assess these impacts on market settlements.First, this dissertation proposes the concept of securitized-LMP to solve the issue of how market participants should be compensated for providing N-1 reliability services. Then, pricing implications and settlements of three state-of-art market models are compared. The results show that with a more accurate representation of contingencies in the market models, N-1 grid security requirements are originally captured; thereby, the value of service provided by generators is reflected in the prices to achieve grid security.
Also, new flexible ramping product (FRP) designs are proposed for different market processes to (i) schedule day-ahead (DA) FRP awards that are more adaptive concerning the real-time (RT) 15-min net load changes, and (ii) address the FRP deployability issue in fifteen-minute market (FMM). The proposed market models performance with enhanced FRP designs is compared against the DA market and FMM models with the existing FRP design through a validation methodology based on California independent system operator (ISO) RT operation. The proposed FRP designs lead to less expected final RT operating cost, higher reliability, and fewer RT price spikes.
Finally, this dissertation proposes a distribution utility and ISO coordination framework to enable ISO to manage the wholesale market while preemptively not allowing aggregators to cause distribution system (DS) violations. To this end, this coordination framework architecture utilizes the statistical information obtained using different DS conditions and data-mining algorithms to predict the aggregators qualified maximum capacity. A validation phase considering Volt-VAr support provided by distributed PV smart inverters is utilized for evaluate the proposed model performance. The proposed model produces wholesale market awards for aggregators that fall within the DS operational limits and, consequently, will not impose reliable and safety issues for the DS.
ContributorsGhaljehei, Mohammad (Author) / Khorsand, Mojdeh (Thesis advisor) / Vittal, Vijay (Committee member) / Wu, Meng (Committee member) / Weng, Yang (Committee member) / Arizona State University (Publisher)
Created2022
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
Voltage Source Converters (VSCs) have been widely used in grid-connected applications with Distributed Energy Resource (DER) and Electric Vehicle (EV) applications. Replacement of traditional thyristors with Silicon/Silicon-Carbide based active switches provides full control capability to the converters and allows bidirectional power flow between the source and active loads. In this study, advanced control strategies for DER inverters and EV traction inverters will be explored.Chapter 1 gives a brief introduction to State-of-the-Art of VSC control strategies and summarizes the existing challenges in different applications. Chapter 2 presents multiple advanced control strategies of grid-connected DER inverters. Various grid support functions have been implemented in simulations and hardware experiments under both normal and abnormal operating conditions. Chapter 3 proposes an automated design and optimization process of a robust H-infinity controller to address the stability issue of grid-connected inverters caused by grid impedance variation. The principle of the controller synthesis is to select appropriate weighting functions to shape the systems closed-loop transfer function and to achieve robust stability and robust performance. An optimal controller will be selected by using a 2-Dimensional Pareto Front. Chapter 4 proposes a high-performance 4-layer communication architecture to facilitate the control of a large distribution network with high Photovoltaic (PV) penetration. Multiple strategies have been implemented to address the challenges
of coordination between communication and system control and between different communication protocols, which leads to a boost in the communication efficiency and makes the architecture highly scalable, adaptive, and robust. Chapter 5 presents the control strategies of a traditional Modular Multilevel Converter (MMC) and a novel Modular Isolated Multilevel Converter (MIMC) in grid-connected and variable speed drive applications. The proposed MIMC is able to achieve great size reduction for the submodule capacitors since the fundamental and double-line frequency voltage ripple has been cancelled. Chapter 6 shows a detailed hardware and controller design for a 48 V Belt-driven Starter Generator (BSG) inverter using automotive gate driver ICs and microcontroller. The inverter prototype has reached a power density of 333
W/inch3, up to 200 A phase current and 600 Hz output frequency.
ContributorsSi, Yunpeng (Author) / Lei, Qin (Thesis advisor) / Ayyanar, Raja (Committee member) / Vittal, Vijay (Committee member) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2022
Description
Power systems are transforming into more complex and stressed systems each day. These stressed conditions could lead to a slow decline in the power grid's voltage profile and sometimes lead to a partial or total blackout. This phenomenon can be identified by either solving a power flow problem or using measurement-based real-time monitoring algorithms. The first part of this thesis focuses on proposing a robust power flow algorithm for ill-conditioned systems. While preserving the stable nature of the fixed point (FP) method, a novel distributed FP equation is proposed to calculate the voltage at each bus. The proposed algorithm's performance is compared with existing methods, showing that the proposed method can correctly find the solutions when other methods cannot work due to high condition number matrices. It is also empirically shown that the FP algorithm is more robust to bad initialization points. The second part of this thesis focuses on identifying the voltage instability phenomenon using real-time monitoring algorithms. This work proposes a novel distributed measurement-based monitoring technique called voltage stability index (VSI). With the help of PMUs and communication of voltage phasors between neighboring buses, the processors embedded at each bus in the smart grid perform simultaneous online computations of VSI. VSI enables real-time identification of the system's critical bus with minimal communication infrastructure. Its benefits include interpretability, fast computation, and low sensitivity to noisy measurements. Furthermore, this work proposes the ``local static-voltage stability index" (LS-VSI) that removes the minimal communication requirement in VSI by requiring only one PMU at the bus of interest. LS-VSI also solves the issue of Thevenin equivalent parameter estimation in the presence of noisy measurements. Unlike VSI, LS-VSI incorporates the ZIP load models and load tap changers (LTCs) and successfully identifies the bifurcation point considering ZIP loads' impact on voltage stability. Both VSI and LS-VSI are useful to monitor the voltage stability margins in real-time using the PMU measurements from the field. However, they cannot indicate the onset of voltage emergency situations. The proposed LD-VSI uses the dynamic measurements of the power system to identify the onset of a voltage emergency situation with an alarm. Compared to existing methods, it is shown that it is more robust to PMU measurement noise and can also identify the voltage collapse point while the existing methods have issues with the same.
ContributorsGuddanti, Kishan Prudhvi (Author) / Weng, Yang (Thesis advisor) / Banerjee, Ayan (Committee member) / Zhang, Baosen (Committee member) / Vittal, Vijay (Committee member) / Arizona State University (Publisher)
Created2021
Description
In recent years, the adoption of Distributed Energy Resources (DERs) in power systems has been increasing, driven by technological advancements, development of monitoring and control techniques, policy guidance among various countries, and the benefits DERs bring to the power system. These benefits include low-cost energy production, environmental sustainability promotion, and enhanced operational efficiency of the power system. For instance, demand response (DR) can alleviate pressure during peak load periods, while solar PV units and wind turbines with smart inverters can improve grid reliability through grid regulation based on IEEE Standard 1547. Despite the opportunities DERs present, their adoption also poses challenges. The growing reliance on renewable sources introduces uncertainty, variability, and intermittency, directly impacting system stability and efficiency. Addressing these challenges necessitates comprehensive research to enhance stability, improve system operations, and maximize resource utilization. This dissertation concentrates on two primary research areas: analyzing prosumer (consumers and producers, as one) consumption behavior and developing AC optimal power flow (ACOPF) models. Firstly, understanding prosumer consumption behavior is important for reducing DERs' uncertainty, particularly DR programs. This study employs a proposed probabilistic algorithm to analyze the causal relationships between prosumer consumption behavior and other factors. Two causal-oriented approaches are utilized to establish accurate prediction models and assess demand flexibility. Causal artificial intelligence facilitates intervention and counterfactual analyses of prosumers’ DR participation and consumption behavior. Finally, a Conditional Hidden Semi-Markov Model (CHSMM) is applied to model and predict household appliance electricity consumption, further enhancing understanding of prosumer behavior. Secondly, the dissertation investigates optimization models for efficient, cost-effective power system operation and resource utilization maximization. A convex two-stage socially-aware and risk-aware Second-Order Cone Programming (SOCP)-based ACOPF model is introduced to mitigate DER uncertainty, enhance PV energy utilization, and reduce operational costs. Additionally, a convex SOCP-based ACOPF model is presented for three-phase unbalanced distribution systems, incorporating the Q-V characteristics of PV units with smart inverters based on IEEE Standard 1547. This model enables the participation of PV units with smart inverters in grid voltage regulation, enhancing power system stability and achieving efficient, cost-effective operation.
ContributorsHe, Mingyue (Author) / Khorsand, Mojdeh (Thesis advisor) / Vittal, Vijay (Committee member) / Weng, Yang (Committee member) / Wu, Meng (Committee member) / Arizona State University (Publisher)
Created2024