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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Signals from the system are used to obtain the frequency response of the component transfer functions. The magnitude and phase angle of the transfer functions are obtained using the fast Fourier transform. The transfer function phase angles of base cases (no attack) are stored and are compared with the phase angles calculated at regular time intervals. If the difference in the phase characteristics is greater than a set threshold, an alarm is issued indicating the detection of a cyber attack.
The developed algorithm is designed for use in the envisioned Future Renewable Electric Energy Delivery and Management (FREEDM) system. Examples are shown for the noise free and noisy cases.
Contemporary market models do not satisfy the minimum stipulated N-1 mandate for generator contingencies adequately. This research enhances the traditional market practices to handle generator contingencies more appropriately. In addition, this research employs stochastic optimization that leverages statistical information of an ensemble of uncertain scenarios and data analytics-based algorithms to design and develop cohesive reserve policies. The proposed approaches modify the classical SCUC problem to include reserve policies that aim to preemptively anticipate post-contingency congestion patterns and account for resource uncertainty, simultaneously. The hypothesis is to integrate data-mining, reserve requirement determination, and stochastic optimization in a holistic manner without compromising on efficiency, performance, and scalability. The enhanced reserve procurement policies use contingency-based response sets and post-contingency transmission constraints to appropriately predict the influence of recourse actions, i.e., nodal reserve deployment, on critical transmission elements.
This research improves the conventional deterministic models, including reserve scheduling decisions, and facilitates the transition to stochastic models by addressing the reserve allocation issue. The performance of the enhanced SCUC model is compared against con-temporary deterministic models and a stochastic unit commitment model. Numerical results are based on the IEEE 118-bus and the 2383-bus Polish test systems. Test results illustrate that the proposed reserve models consistently outperform the benchmark reserve policies by improving the market efficiency and enhancing the reliability of the market solution at reduced costs while maintaining scalability and market transparency. The proposed approaches require fewer ISO discretionary adjustments and can be employed by present-day solvers with minimal disruption to existing market procedures.
Universal F-V coding problem for the class of first-order stationary, irreducible and aperiodic Markov sources is first considered. Third-order coding rate of the TS code for the Markov class is derived. A converse on the third-order coding rate for the general class of F-V codes is presented which shows the optimality of the TS code for such Markov sources.
This type class approach is then generalized for compression of the parametric sources. A natural scheme is to define two sequences to be in the same type class if and only if they are equiprobable under any model in the parametric class. This natural approach, however, is shown to be suboptimal. A variation of the Type Size code is introduced, where type classes are defined based on neighborhoods of minimal sufficient statistics. Asymptotics of the overflow rate of this variation is derived and a converse result establishes its optimality up to the third-order term. These results are derived for parametric families of i.i.d. sources as well as Markov sources.
Finally, universal V-F length coding of the class of parametric sources is considered in the short blocklengths regime. The proposed dictionary which is used to parse the source output stream, consists of sequences in the boundaries of transition from low to high quantized type complexity, hence the name Type Complexity (TC) code. For large enough dictionary, the $\epsilon$-coding rate of the TC code is derived and a converse result is derived showing its optimality up to the third-order term.
First, the question of how synthetic cases may be generated in an automatic manner, and how synthetic samples should be validated to assess whether they are sufficiently ``real'' is considered. Transmission and distribution levels are treated separately, due to the different nature of the two systems. Distribution systems are constructed by sampling distributions observed in a dataset from the Netherlands. For transmission systems, only first-order statistics, such as generator limits or line ratings are sampled statistically. The task of constructing an optimal power flow case from the sample sets is left to an optimization problem built on top of the optimal power flow formulation.
Secondly, attention is turned to some examples where synthetic models are used to inform analysis and modeling tasks. Co-simulation of transmission and multiple distribution systems is considered, where distribution feeders are allowed to couple transmission substations. Next, a distribution power flow method is parametrized to better account for losses. Numerical values for the parametrization can be statistically supported thanks to the ability to generate thousands of feeders on command.
is known as the high-voltage (HV) solution or operable solution. The rest of the solutions
are the low-voltage (LV), or large-angle, solutions.
In this report, a recently developed non-iterative algorithm for solving the power-
flow (PF) problem using the holomorphic embedding (HE) method is shown as
being capable of finding the HV solution, while avoiding converging to LV solutions
nearby which is a drawback to all other iterative solutions. The HE method provides a
novel non-iterative procedure to solve the PF problems by eliminating the
non-convergence and initial-estimate dependency issues appeared in the traditional
iterative methods. The detailed implementation of the HE method is discussed in the
report.
While published work focuses mainly on finding the HV PF solution, modified
holomorphically embedded formulations are proposed in this report to find the
LV/large-angle solutions of the PF problem. It is theoretically proven that the proposed
method is guaranteed to find a total number of 2N solutions to the PF problem
and if no solution exists, the algorithm is guaranteed to indicate such by the oscillations
in the maximal analytic continuation of the coefficients of the voltage power series
obtained.
After presenting the derivation of the LV/large-angle formulations for both PQ
and PV buses, numerical tests on the five-, seven- and 14-bus systems are conducted
to find all the solutions of the system of nonlinear PF equations for those systems using
the proposed HE method.
After completing the derivation to find all the PF solutions using the HE method, it
is shown that the proposed HE method can be used to find only the of interest PF solutions
(i.e. type-1 PF solutions with one positive real-part eigenvalue in the Jacobian
matrix), with a proper algorithm developed. The closet unstable equilibrium point
(UEP), one of the type-1 UEP’s, can be obtained by the proposed HE method with
limited dynamic models included.
The numerical performance as well as the robustness of the proposed HE method is
investigated and presented by implementing the algorithm on the problematic cases and
large-scale power system.
A DC based heuristic, i.e., a greedy algorithm, is developed and applied to improve the computational time for the TC problem while still maintaining the ability to find quality solutions. In the greedy algorithm, an expression is derived, which indicates the impact on the objective for a marginal change in the state of a transmission line. This expression is used to generate a priority list with potential candidate lines for switching, which may provide huge improvements to the system. The advantage of this method is that it is a fast heuristic as compared to using mixed integer programming (MIP) approach.
Alternatively, AC based heuristics are developed for TC problem and tested on actual data from PJM, ERCOT and TVA. AC based N-1 contingency analysis is performed to identify the contingencies that cause network violations. Simple proximity based heuristics are developed and the fast decoupled power flow is solved iteratively to identify the top five TC actions, which provide reduction in violations. Time domain simulations are performed to ensure that the TC actions do not cause system instability. Simulation results show significant reductions in violations in the system by the application of the TC heuristics.