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.
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Moreover, wide band-gap devices (both SiC and GaN) are used for implementing their hardware prototypes. It enables the switching frequency to be high without compromising on the converter efficiency. Also it allows a reduced magnetic component size, further enabling a high power density solution, with power density far beyond the state-of-the art solutions.
Additionally, for the transformer-less microinverter application, another challenge is to achieve a very high gain DC-DC stage with a simultaneous high conversion efficiency. An extended duty ratio (EDR) boost converter which is a hybrid of switched capacitors and interleaved inductor technique, has been implemented for this purpose. It offers higher converter efficiency as most of the switches encounter lower voltage stress directly impacting switching loss; the input current being shared among all the interleaved converters (inherent sharing only in a limited duty ratio), the inductor conduction loss is reduced by a factor of the number of phases.
Further, the EDR boost converter has been studied for both discontinuous conduction mode (DCM) operations and operations with wide input/output voltage range in continuous conduction mode (CCM). A current sharing between its interleaved input phases is studied in detail to show that inherent sharing is possible for only in a limited duty ratio span, and modification of the duty ratio scheme is proposed to ensure equal current sharing over all the operating range for 3 phase EDR boost. All the analysis are validated with experimental results.
Constructing the hybrid AC-HVDC grid is a significant move in the development of the HVDC techniques; the form of dc system is evolving from the point-to-point stand-alone dc links to the embedded HVDC system and the multi-terminal HVDC (MTDC) system. The MTDC is a solution for the renewable energy interconnections, and the MTDC grids can improve the power system reliability, flexibility in economic dispatches, and converter/cable utilizing efficiencies.
The dissertation reviews the HVDC technologies, discusses the stability issues regarding the ac and HVDC connections, proposes a novel power oscillation control strategy to improve system stability, and develops a nonlinear voltage droop control strategy for the MTDC grid.
To verify the effectiveness the proposed power oscillation control strategy, a long distance paralleled AC-HVDC transmission test system is employed. Based on the PSCAD/EMTDC platform simulation results, the proposed power oscillation control strategy can improve the system dynamic performance and attenuate the power oscillations effectively.
To validate the nonlinear voltage droop control strategy, three droop controls schemes are designed according to the proposed nonlinear voltage droop control design procedures. These control schemes are tested in a hybrid AC-MTDC system. The hybrid AC-MTDC system, which is first proposed in this dissertation, consists of two ac grids, two wind farms and a five-terminal HVDC grid connecting them. Simulation studies are performed in the PSCAD/EMTDC platform. According to the simulation results, all the three design schemes have their unique salient features.
RTCA is first conducted to identify critical contingencies that may cause violations. Then, for each critical contingency, CTS is performed to determine the beneficial switching actions that can reduce post-contingency violations. To reduce computational burden, fast heuristic algorithms are proposed to generate candidate switching lists. Numerical simulations performed on three large-scale realistic power systems (TVA, ERCOT, and PJM) demonstrate that CTS can significantly reduce post-contingency violations. Parallel computing can further reduce the solution time.
RT SCED is to eliminate the actual overloads and potential post-contingency overloads identified by RTCA. Procedure-A, which is consistent with existing industry practices, is proposed to connect RTCA and RT SCED. As CTS can reduce post-contingency violations, higher branch limits, referred to as pseudo limits, may be available for some contingency-case network constraints. Thus, Procedure-B is proposed to take advantage of the reliability benefits provided by CTS. With the proposed Procedure-B, CTS can be modeled in RT SCED implicitly through the proposed pseudo limits for contingency-case network constraints, which requires no change to existing RT SCED tools. Numerical simulations demonstrate that the proposed Procedure-A can effectively eliminate the flow violations reported by RTCA and that the proposed Procedure-B can reduce most of the congestion cost with consideration of CTS.
The system status may be inaccurately estimated due to false data injection (FDI) cyber-attacks, which may mislead operators to adjust the system improperly and cause network violations. Thus, a two-stage FDI detection (FDID) approach, along with several metrics and an alert system, is proposed in this dissertation to detect FDI attacks. The first stage is to determine whether the system is under attack and the second stage would identify the target branch. Numerical simulations demonstrate the effectiveness of the proposed two-stage FDID approach.
Once the models were developed, it was observed that the earth surface temperature above the installation, solar radiation and other external factors such as underlying water lines, drain pipes, etc. play a key role in heating up or cooling down the power cables. It was also determined that the hotspot location in the power cable in the main duct was the same as the hotspot location in the spare duct inside the same installation.
It was also observed that the CYMCAP model had its limitations when the earth surface temperature variations were modeled in the software as the software only allows the earth’s ambient temperature to be modeled as a constant; further, results from the MATLAB model were more in line with the present theory of underground power cable temperature prediction. However, simulation results from both the MATLAB and CYMCAP model showed deviation from the measured data. It was also observed that the spare duct temperatures in this particular underground installation seemed to be affected by external factors such as solar radiation, underlying water lines, gas lines etc. which cannot be modeled in CYMCAP.
These targets for the converter are met through a number of different ways. The switches used are Silicon Carbide FETs. These are wide band gap (WBG) devices that can operate at high frequencies and temperatures. Since they allow for high frequency operation, a switching frequency of 250 khz is proposed and implemented. This helps with power density by reducing the size of passive components. High efficiencies are made possible by using a simple soft switching technique by augmenting the DC/DC converter with an auxiliary branch to enable zero voltage transition.
The efficacy of the approach is tested through simulation and hardware implementation of two different prototypes. The Gen-I prototype was a single soft switched synchronous boost converter rated at 2.5kw. Both the motoring mode and regenerative modes of operation (Boost and Buck) were hardware tested for over 2kw and efficiency results of over 98.15% were achieved. The Gen-II prototype and the main focus of this work is an interleaved soft switched synchronous boost converter. This converter has been implemented in hardware as well and has been tested at 6.7kw and an efficiency of over 98% has been achieved in the boost mode of operation.
First, this dissertation focuses on transmission thermal constraint relaxation practices. Most system operators employ constraint relaxation practices, which allow certain constraints to be relaxed for penalty prices, in their market models. A proper selection of penalty prices is imperative due to the influence that penalty prices have on generation scheduling and market settlements. However, penalty prices are primarily decided today based on stakeholder negotiations or system operator’s judgments. There is little to no methodology or engineered approach around the determination of these penalty prices. This work proposes new methods that determine the penalty prices for thermal constraint relaxations based on the impact overloading can have on the residual life of the line. This study evaluates the effectiveness of the proposed methods in the short-term operational planning and long-term transmission expansion planning studies.
The second part of this dissertation investigates an advanced methodology to handle uncertainties associated with high penetration of renewable resources, which poses new challenges to power system reliability and calls attention to include stochastic modeling within resource scheduling applications. However, the inclusion of stochastic modeling within mathematical programs has been a challenge due to computational complexities. Moreover, market design issues due to the stochastic market environment make it more challenging. Given the importance of reliable and affordable electric power, such a challenge to advance existing deterministic resource scheduling applications is critical. This ongoing and joint research attempts to overcome these hurdles by developing a stochastic look-ahead commitment tool, which is a stand-alone advisory tool. This dissertation contributes to the derivation of a mathematical formulation for the extensive form two-stage stochastic programming model, the utilization of Progressive Hedging decomposition algorithm, and the initial implementation of the Progressive Hedging subproblem along with various heuristic strategies to enhance the computational performance.
Currently, several hard-switching topologies have been employed such as conventional boost DC/DC, interleaved step-up DC/DC, and full-bridge DC/DC converter. These converters face respective limitations in achieving high step-up conversion ratio, size and weight issues, or high component count. In this work, a bi-directional synchronous boost DC/DC converter with easy interleaving capability is proposed with a novel ZVT mechanism. This converter steps up the EV battery voltage of 200V-300V to a wide range of variable output voltages ranging from 310V-800V. High power density and efficiency are achieved through high switching frequency of 250kHz for each phase with effective frequency doubling through interleaving. Also, use of wide bandgap high voltage SiC switches allows high efficiency operation even at high temperatures.
Comprehensive analysis, design details and extensive simulation results are presented. Incorporating ZVT branch with adaptive time delay results in converter efficiency close to 98%. Experimental results from a 2.5kW hardware prototype validate the performance of the proposed approach. A peak efficiency of 98.17% has been observed in hardware in the boost or motoring mode.
An important part of this elaborate project is the development of a new load forecasting algorithm and a better control strategy for the optimized utilization of the storage system. The built-in algorithm of this commercial unit uses simple forecasting and battery control strategies. With the recent improvement in Machine Learning (ML) techniques, development of a more sophisticated model of the problem in hand was possible. This research is aimed at achieving the goal by utilizing the appropriate ML techniques to better model the problem, which will essentially result in a better solution. In this research, a set of six unique features are used to model the load forecasting problem and different ML algorithms are simulated on the developed model. A similar approach is taken to solve the PV prediction problem. Finally, a very effective battery control strategy is built (utilizing the results of the load and PV forecasting), with the aim of ensuring a reduction in the amount of energy consumed from the grid during the “on-peak” hours. Apart from the reduction in the energy consumption, this battery control algorithm decelerates the “cycling aging” or the aging of the battery owing to the charge/dis-charges cycles endured by selectively charging/dis-charging the battery based on need.
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The results of this proposed strategy are verified using a hardware implementation (the PV system was coupled with a custom-built load bank and this setup was used to simulate a house). The results pertaining to the performances of the built-in algorithm and the ML algorithm are compared and the economic analysis is performed. The findings of this research have in the process of being published in a reputed journal.