Matching Items (28)

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Privacy-guaranteed Data Collection: The Case for Efficient Resource Management of Nonprofit Organizations

Description

Through the personal experience of volunteering at ASU Project Humanities, an organization that provides resources such as clothing and toiletries to the homeless population in Downtown Phoenix, I noticed efficiently

Through the personal experience of volunteering at ASU Project Humanities, an organization that provides resources such as clothing and toiletries to the homeless population in Downtown Phoenix, I noticed efficiently serving the needs of the homeless population is an important endeavor, but the current processes for Phoenix nonprofits to collect data are manual, ad-hoc, and inefficient. This leads to the research question: is it possible to improve this process of collecting statistics on client needs, tracking donations, and managing resources using technology? Background research includes an interview with ASU Project Humanities, articles by analysts, and related work including case studies of current technologies in the nonprofit community. Major findings include i) a lack of centralized communication in nonprofits collecting needs, tracking surplus donations, and sharing resources, ii) privacy assurance is important to homeless individuals, and iii) pre-existing databases and technological solutions have demonstrated that technology has the ability to make an impact in the nonprofit community. To improve the process, standardization, efficiency, and automation need to increase. As a result of my analysis, the thesis proposes a prototype solution which includes two parts: an inventory database and a web application with forms for user input and tables for the user to view. This solution addresses standardization by showing a consistent way of collecting data on need requests and surplus donations while guaranteeing privacy of homeless individuals. This centralized solution also increases efficiency by connecting different agencies that cater to these clients. Lastly, the solution demonstrates the ability for resources to be made available to each organization which can increase automation. In conclusion, this database and web application has the potential to improve nonprofit organizations’ networking capabilities, resource management, and resource distribution. The percentile of homeless individuals connected to these resources is expected to increase substantially with future live testing and large-scale implementation.

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Created

Date Created
  • 2019-05

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Network reduction for system planning

Description

Due to great challenges from aggressive environmental regulations, increased demand due to new technologies and the integration of renewable energy sources, the energy industry may radically change the way the

Due to great challenges from aggressive environmental regulations, increased demand due to new technologies and the integration of renewable energy sources, the energy industry may radically change the way the power system is operated and designed. With the motivation of studying and planning the future power system under these new challenges, the development of the new tools is required. A network equivalent that can be used in such planning tools needs to be generated based on an accurate power flow model and an equivalencing procedure that preserves the key characteristics of the original system. Considering the pervasive use of the dc power flow models, their accuracy is of great concern. The industry seems to be sanguine about the performance of dc power flow models, but recent research has shown that the performance of different formulations is highly variable. In this thesis, several dc power-flow models are analyzed theoretically and evaluated numerically in IEEE 118-bus system and Eastern Interconnection 62,000-bus system. As shown in the numerical example, the alpha-matching dc power flow model performs best in matching the original ac power flow solution. Also, the possibility of applying these dc models in the various applications has been explored and demonstrated. Furthermore, a novel hot-start optimal dc power-flow model based on ac power transfer distribution factors (PTDFs) is proposed, implemented and tested. This optimal-reactance-only dc model not only matches the original ac PF solution well, but also preserves the congestion pattern obtain from the OPF results of the original ac model. Three improved strategies were proposed for applying the bus-aggregation technique to the large-scale systems, like EI and ERCOT, to improve the execution time, and memory requirements when building a reduced equivalent model. Speed improvements of up to a factor of 200 were observed.

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Created

Date Created
  • 2013

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Vulnerability Analysis of False Data Injection Attacks on Supervisory Control and Data Acquisition and Phasor Measurement Units

Description

The electric power system is monitored via an extensive network of sensors in tandem with data processing algorithms, i.e., an intelligent cyber layer, that enables continual observation and control of

The electric power system is monitored via an extensive network of sensors in tandem with data processing algorithms, i.e., an intelligent cyber layer, that enables continual observation and control of the physical system to ensure reliable operations. This data collection and processing system is vulnerable to cyber-attacks that impact the system operation status and lead to serious physical consequences, including systematic problems and failures.

This dissertation studies the physical consequences of unobservable false data injection (FDI) attacks wherein the attacker maliciously changes supervisory control and data acquisition (SCADA) or phasor measurement unit (PMU) measurements, on the electric power system. In this context, the dissertation is divided into three parts, in which the first two parts focus on FDI attacks on SCADA and the last part focuses on FDI attacks on PMUs.

The first part studies the physical consequences of FDI attacks on SCADA measurements designed with limited system information. The attacker is assumed to have perfect knowledge inside a sub-network of the entire system. Two classes of attacks with different assumptions on the attacker's knowledge outside of the sub-network are introduced. In particular, for the second class of attacks, the attacker is assumed to have no information outside of the attack sub-network, but can perform multiple linear regression to learn the relationship between the external network and the attack sub-network with historical data. To determine the worst possible consequences of both classes of attacks, a bi-level optimization problem wherein the first level models the attacker's goal and the second level models the system response is introduced.

The second part of the dissertation concentrates on analyzing the vulnerability of systems to FDI attacks from the perspective of the system. To this end, an off-line vulnerability analysis framework is proposed to identify the subsets of the test system that are more prone to FDI attacks.

The third part studies the vulnerability of PMUs to FDI attacks. Two classes of more sophisticated FDI attacks that capture the temporal correlation of PMU data are introduced. Such attacks are designed with a convex optimization problem and can always bypass both the bad data detector and the low-rank decomposition (LD) detector.

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Created

Date Created
  • 2017

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Quantifying Information Leakage via Adversarial Loss Functions: Theory and Practice

Description

Modern digital applications have significantly increased the leakage of private and sensitive personal data. While worst-case measures of leakage such as Differential Privacy (DP) provide the strongest guarantees, when utility

Modern digital applications have significantly increased the leakage of private and sensitive personal data. While worst-case measures of leakage such as Differential Privacy (DP) provide the strongest guarantees, when utility matters, average-case information-theoretic measures can be more relevant. However, most such information-theoretic measures do not have clear operational meanings. This dissertation addresses this challenge.

This work introduces a tunable leakage measure called maximal $\alpha$-leakage which quantifies the maximal gain of an adversary in inferring any function of a data set. The inferential capability of the adversary is modeled by a class of loss functions, namely, $\alpha$-loss. The choice of $\alpha$ determines specific adversarial actions ranging from refining a belief for $\alpha =1$ to guessing the best posterior for $\alpha = \infty$, and for the two specific values maximal $\alpha$-leakage simplifies to mutual information and maximal leakage, respectively. Maximal $\alpha$-leakage is proved to have a composition property and be robust to side information.

There is a fundamental disjoint between theoretical measures of information leakages and their applications in practice. This issue is addressed in the second part of this dissertation by proposing a data-driven framework for learning Censored and Fair Universal Representations (CFUR) of data. This framework is formulated as a constrained minimax optimization of the expected $\alpha$-loss where the constraint ensures a measure of the usefulness of the representation. The performance of the CFUR framework with $\alpha=1$ is evaluated on publicly accessible data sets; it is shown that multiple sensitive features can be effectively censored to achieve group fairness via demographic parity while ensuring accuracy for several \textit{a priori} unknown downstream tasks.

Finally, focusing on worst-case measures, novel information-theoretic tools are used to refine the existing relationship between two such measures, $(\epsilon,\delta)$-DP and R\'enyi-DP. Applying these tools to the moments accountant framework, one can track the privacy guarantee achieved by adding Gaussian noise to Stochastic Gradient Descent (SGD) algorithms. Relative to state-of-the-art, for the same privacy budget, this method allows about 100 more SGD rounds for training deep learning models.

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Created

Date Created
  • 2020

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Unobservable False Data Injection Attacks on Power Systems

Description

Reliable operation of modern power systems is ensured by an intelligent cyber layer that monitors and controls the physical system. The data collection and transmission is achieved by the supervisory

Reliable operation of modern power systems is ensured by an intelligent cyber layer that monitors and controls the physical system. The data collection and transmission is achieved by the supervisory control and data acquisition (SCADA) system, and data processing is performed by the energy management system (EMS). In the recent decades, the development of phasor measurement units (PMUs) enables wide area real-time monitoring and control. However, both SCADA-based and PMU-based cyber layers are prone to cyber attacks that can impact system operation and lead to severe physical consequences.

This dissertation studies false data injection (FDI) attacks that are unobservable to bad data detectors (BDD). Prior work has shown that an attacker-defender bi-level linear program (ADBLP) can be used to determine the worst-case consequences of FDI attacks aiming to maximize the physical power flow on a target line. However, the results were only demonstrated on small systems assuming that they are operated with DC optimal power flow (OPF). This dissertation is divided into four parts to thoroughly understand the consequences of these attacks as well as develop countermeasures.

The first part focuses on evaluating the vulnerability of large-scale power systems to FDI attacks. The solution technique introduced in prior work to solve the ADBLP is intractable on large-scale systems due to the large number of binary variables. Four new computationally efficient algorithms are presented to solve this problem.

The second part studies vulnerability of N-1 reliable power systems operated by state-of-the-art EMSs commonly used in practice, specifically real-time contingency analysis (RTCA), and security-constrained economic dispatch (SCED). An ADBLP is formulated with detailed assumptions on attacker's knowledge and system operations.

The third part considers FDI attacks on PMU measurements that have strong temporal correlations due to high data rate. It is shown that predictive filters can detect suddenly injected attacks, but not gradually ramping attacks.

The last part proposes a machine learning-based attack detection framework consists of a support vector regression (SVR) load predictor that predicts loads by exploiting both spatial and temporal correlations, and a subsequent support vector machine (SVM) attack detector to determine the existence of attacks.

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Created

Date Created
  • 2020

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Bayesian Framework for Sparse Vector Recovery and Parameter Bounds with Application to Compressive Sensing

Description

Signal compressed using classical compression methods can be acquired using brute force (i.e. searching for non-zero entries in component-wise). However, sparse solutions require combinatorial searches of high computations. In this

Signal compressed using classical compression methods can be acquired using brute force (i.e. searching for non-zero entries in component-wise). However, sparse solutions require combinatorial searches of high computations. In this thesis, instead, two Bayesian approaches are considered to recover a sparse vector from underdetermined noisy measurements. The first is constructed using a Bernoulli-Gaussian (BG) prior distribution and is assumed to be the true generative model. The second is constructed using a Gamma-Normal (GN) prior distribution and is, therefore, a different (i.e. misspecified) model. To estimate the posterior distribution for the correctly specified scenario, an algorithm based on generalized approximated message passing (GAMP) is constructed, while an algorithm based on sparse Bayesian learning (SBL) is used for the misspecified scenario. Recovering sparse signal using Bayesian framework is one class of algorithms to solve the sparse problem. All classes of algorithms aim to get around the high computations associated with the combinatorial searches. Compressive sensing (CS) is a widely-used terminology attributed to optimize the sparse problem and its applications. Applications such as magnetic resonance imaging (MRI), image acquisition in radar imaging, and facial recognition. In CS literature, the target vector can be recovered either by optimizing an objective function using point estimation, or recovering a distribution of the sparse vector using Bayesian estimation. Although Bayesian framework provides an extra degree of freedom to assume a distribution that is directly applicable to the problem of interest, it is hard to find a theoretical guarantee of convergence. This limitation has shifted some of researches to use a non-Bayesian framework. This thesis tries to close this gab by proposing a Bayesian framework with a suggested theoretical bound for the assumed, not necessarily correct, distribution. In the simulation study, a general lower Bayesian Cram\'er-Rao bound (BCRB) bound is extracted along with misspecified Bayesian Cram\'er-Rao bound (MBCRB) for GN model. Both bounds are validated using mean square error (MSE) performances of the aforementioned algorithms. Also, a quantification of the performance in terms of gains versus losses is introduced as one main finding of this report.

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Created

Date Created
  • 2019

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Creating, Validating, and Using Synthetic Power Flow Cases: A Statistical Approach to Power System Analysis

Description

Synthetic power system test cases offer a wealth of new data for research and development purposes, as well as an avenue through which new kinds of analyses and questions can

Synthetic power system test cases offer a wealth of new data for research and development purposes, as well as an avenue through which new kinds of analyses and questions can be examined. This work provides both a methodology for creating and validating synthetic test cases, as well as a few use-cases for how access to synthetic data enables otherwise impossible analysis.

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.

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Created

Date Created
  • 2019

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HTLS upgrades for power transmission expansion planning and operation

Description

Renewable portfolio standards prescribe for penetration of high amounts of re-newable energy sources (RES) that may change the structure of existing power systems. The load growth and changes in power

Renewable portfolio standards prescribe for penetration of high amounts of re-newable energy sources (RES) that may change the structure of existing power systems. The load growth and changes in power flow caused by RES integration may result in re-quirements of new available transmission capabilities and upgrades of existing transmis-sion paths. Construction difficulties of new transmission lines can become a problem in certain locations. The increase of transmission line thermal ratings by reconductoring using High Temperature Low Sag (HTLS) conductors is a comparatively new technology introduced to transmission expansion. A special design permits HTLS conductors to operate at high temperatures (e.g., 200oC), thereby allowing passage of higher current. The higher temperature capability increases the steady state and emergency thermal ratings of the transmission line. The main disadvantage of HTLS technology is high cost. The high cost may place special emphasis on a thorough analysis of cost to benefit of HTLS technology im-plementation. Increased transmission losses in HTLS conductors due to higher current may be a disadvantage that can reduce the attractiveness of this method. Studies described in this thesis evaluate the expenditures for transmission line re-conductoring using HTLS and the consequent benefits obtained from the potential decrease in operating cost for thermally limited transmission systems. Studies performed consider the load growth and penetration of distributed renewable energy sources according to the renewable portfolio standards for power systems. An evaluation of payback period is suggested to assess the cost to benefit ratio of HTLS upgrades. The thesis also considers the probabilistic nature of transmission upgrades. The well-known Chebyshev inequality is discussed with an application to transmission up-grades. The Chebyshev inequality is proposed to calculate minimum payback period ob-tained from the upgrades of certain transmission lines. The cost to benefit evaluation of HTLS upgrades is performed using a 225 bus equivalent of the 2012 summer peak Arizona portion of the Western Electricity Coordi-nating Council (WECC).

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Created

Date Created
  • 2014

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Cluster-and-connect: an algorithmic approach to generating synthetic electric power network graphs

Description

Understanding the graphical structure of the electric power system is important

in assessing reliability, robustness, and the risk of failure of operations of this criti-

cal infrastructure network. Statistical graph models of

Understanding the graphical structure of the electric power system is important

in assessing reliability, robustness, and the risk of failure of operations of this criti-

cal infrastructure network. Statistical graph models of complex networks yield much

insight into the underlying processes that are supported by the network. Such gen-

erative graph models are also capable of generating synthetic graphs representative

of the real network. This is particularly important since the smaller number of tradi-

tionally available test systems, such as the IEEE systems, have been largely deemed

to be insucient for supporting large-scale simulation studies and commercial-grade

algorithm development. Thus, there is a need for statistical generative models of

electric power network that capture both topological and electrical properties of the

network and are scalable.

Generating synthetic network graphs that capture key topological and electrical

characteristics of real-world electric power systems is important in aiding widespread

and accurate analysis of these systems. Classical statistical models of graphs, such as

small-world networks or Erd}os-Renyi graphs, are unable to generate synthetic graphs

that accurately represent the topology of real electric power networks { networks

characterized by highly dense local connectivity and clustering and sparse long-haul

links.

This thesis presents a parametrized model that captures the above-mentioned

unique topological properties of electric power networks. Specically, a new Cluster-

and-Connect model is introduced to generate synthetic graphs using these parameters.

Using a uniform set of metrics proposed in the literature, the accuracy of the proposed

model is evaluated by comparing the synthetic models generated for specic real

electric network graphs. In addition to topological properties, the electrical properties

are captured via line impedances that have been shown to be modeled reliably by well-studied heavy tailed distributions. The details of the research, results obtained and

conclusions drawn are presented in this document.

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Created

Date Created
  • 2015

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Consequences of false data injection on power system state estimation

Description

The electric power system is one of the largest, most complicated, and most important cyber-physical systems in the world. The link between the cyber and physical level is the Supervisory

The electric power system is one of the largest, most complicated, and most important cyber-physical systems in the world. The link between the cyber and physical level is the Supervisory Control and Data Acquisition (SCADA) systems and Energy Management Systems (EMS). Their functions include monitoring the real-time system operation through state estimation (SE), controlling the system to operate reliably, and optimizing the system operation efficiency. The SCADA acquires the noisy measurements, such as voltage angle and magnitude, line power flows, and line current magnitude, from the remote terminal units (RTUs). These raw data are firstly sent to the SE, which filters all the noisy data and derives the best estimate of the system state. Then the estimated states are used for other EMS functions, such as contingency analysis, optimal power flow, etc.

In the existing state estimation process, there is no defense mechanism for any malicious attacks. Once the communication channel between the SCADA and RTUs is hijacked by the attacker, the attacker can perform a man-in-middle attack and send data of its choice. The only step that can possibly detect the attack during the state estimation process is the bad data detector. Unfortunately, even the bad data detector is unable to detect a certain type of attack, known as the false data injection (FDI) attacks.

Diagnosing the physical consequences of such attacks, therefore, is very important to understand system stability. In this thesis, theoretical general attack models for AC and DC attacks are given and an optimization problem for the worst-case overload attack is formulated. Furthermore, physical consequences of FDI attacks, based on both DC and AC model, are addressed. Various scenarios with different attack targets and system configurations are simulated. The details of the research, results obtained and conclusions drawn are presented in this document.

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Created

Date Created
  • 2015