Matching Items (12)
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- All Subjects: Power system reliability
- All Subjects: Industrial Engineering
- Creators: Shunk, Dan
Description
In a healthcare setting, the Sterile Processing Department (SPD) provides ancillary services to the Operating Room (OR), Emergency Room, Labor & Delivery, and off-site clinics. SPD's function is to reprocess reusable surgical instruments and return them to their home departments. The management of surgical instruments and medical devices can impact patient safety and hospital revenue. Any time instrumentation or devices are not available or are not fit for use, patient safety and revenue can be negatively impacted. One step of the instrument reprocessing cycle is sterilization. Steam sterilization is the sterilization method used for the majority of surgical instruments and is preferred to immediate use steam sterilization (IUSS) because terminally sterilized items can be stored until needed. IUSS Items must be used promptly and cannot be stored for later use. IUSS is intended for emergency situations and not as regular course of action. Unfortunately, IUSS is used to compensate for inadequate inventory levels, scheduling conflicts, and miscommunications. If IUSS is viewed as an adverse event, then monitoring IUSS incidences can help healthcare organizations meet patient safety goals and financial goals along with aiding in process improvement efforts. This work recommends statistical process control methods to IUSS incidents and illustrates the use of control charts for IUSS occurrences through a case study and analysis of the control charts for data from a health care provider. Furthermore, this work considers the application of data mining methods to IUSS occurrences and presents a representative example of data mining to the IUSS occurrences. This extends the application of statistical process control and data mining in healthcare applications.
ContributorsWeart, Gail (Author) / Runger, George C. (Thesis advisor) / Li, Jing (Committee member) / Shunk, Dan (Committee member) / Arizona State University (Publisher)
Created2014
Description
This thesis presents research on innovative AC transmission design concepts and focused mathematics for electric power transmission design. The focus relates to compact designs, high temperature low sag conductors, and high phase order design. The motivation of the research is to increase transmission capacity with limited right of way.
Regarding compact phase spacing, insight into the possibility of increasing the security rating of transmission lines is the primary focus through increased mutual coupling and decreased positive sequence reactance. Compact design can reduce the required corridor width to as little as 31% of traditional designs, especially with the use of inter-phase spacers. Typically transmission lines are built with conservative clearances, with difficulty obtaining right of way, more compact phase spacing may be needed. With design consideration significant compaction can produce an increase by 5-25% in the transmission line security (steady state stability) rating. In addition, other advantages and disadvantages of compact phase design are analyzed. Also, the next two topics: high temperature low sag conductors and high phase order designs include the use of compact designs.
High temperature low sag (HTLS) conductors are used to increase the thermal capacity of a transmission line up to two times the capacity compared to traditional conductors. HTLS conductors can operate continuously at 150-210oC and in emergency at 180-250oC (depending on the HTLS conductor). ACSR conductors operate continuously at 50-110oC and in emergency conditions at 110-150oC depending on the utility, line, and location. HTLS conductors have decreased sag characteristics of up to 33% compared to traditional ACSR conductors at 100oC and up to 22% at 180oC. In addition to what HTLS has to offer in terms of the thermal rating improvement, the possibility of using HTLS conductors to indirectly reduce tower height and compact the phases to increase the security limit is investigated. In addition, utilizing HTLS conductors to increase span length and decrease the number of transmission towers is investigated. The phase compaction or increased span length is accomplished by utilization of the improved physical sag characteristics of HTLS conductors.
High phase order (HPO) focuses on the ability to increase the power capacity for a given right of way. For example, a six phase line would have a thermal rating of approximately 173%, a security rating of approximately 289%, and the SIL would be approximately 300% of a double circuit three phase line with equal right of way and equal voltage line to line. In addition, this research focuses on algorithm and model development of HPO systems. A study of the impedance of HPO lines is presented. The line impedance matrices for some high phase order configurations are circulant Toeplitz matrices. Properties of circulant matrices are developed for the generalized sequence impedances of HPO lines. A method to calculate the sequence impedances utilizing unique distance parameter algorithms is presented. A novel method to design the sequence impedances to specifications is presented. Utilizing impedance matrices in circulant form, a generalized form of the sequence components transformation matrix is presented. A generalized voltage unbalance factor in discussed for HPO transmission lines. Algorithms to calculate the number of fault types and number of significant fault types for an n-phase system are presented. A discussion is presented on transposition of HPO transmission lines and a generalized fault analysis of a high phase order circuit is presented along with an HPO analysis program.
The work presented has the objective of increasing the use of rights of way for bulk power transmission through the use of innovative transmission technologies. The purpose of this dissertation is to lay down some of the building blocks and to help make the three technologies discussed practical applications in the future.
Regarding compact phase spacing, insight into the possibility of increasing the security rating of transmission lines is the primary focus through increased mutual coupling and decreased positive sequence reactance. Compact design can reduce the required corridor width to as little as 31% of traditional designs, especially with the use of inter-phase spacers. Typically transmission lines are built with conservative clearances, with difficulty obtaining right of way, more compact phase spacing may be needed. With design consideration significant compaction can produce an increase by 5-25% in the transmission line security (steady state stability) rating. In addition, other advantages and disadvantages of compact phase design are analyzed. Also, the next two topics: high temperature low sag conductors and high phase order designs include the use of compact designs.
High temperature low sag (HTLS) conductors are used to increase the thermal capacity of a transmission line up to two times the capacity compared to traditional conductors. HTLS conductors can operate continuously at 150-210oC and in emergency at 180-250oC (depending on the HTLS conductor). ACSR conductors operate continuously at 50-110oC and in emergency conditions at 110-150oC depending on the utility, line, and location. HTLS conductors have decreased sag characteristics of up to 33% compared to traditional ACSR conductors at 100oC and up to 22% at 180oC. In addition to what HTLS has to offer in terms of the thermal rating improvement, the possibility of using HTLS conductors to indirectly reduce tower height and compact the phases to increase the security limit is investigated. In addition, utilizing HTLS conductors to increase span length and decrease the number of transmission towers is investigated. The phase compaction or increased span length is accomplished by utilization of the improved physical sag characteristics of HTLS conductors.
High phase order (HPO) focuses on the ability to increase the power capacity for a given right of way. For example, a six phase line would have a thermal rating of approximately 173%, a security rating of approximately 289%, and the SIL would be approximately 300% of a double circuit three phase line with equal right of way and equal voltage line to line. In addition, this research focuses on algorithm and model development of HPO systems. A study of the impedance of HPO lines is presented. The line impedance matrices for some high phase order configurations are circulant Toeplitz matrices. Properties of circulant matrices are developed for the generalized sequence impedances of HPO lines. A method to calculate the sequence impedances utilizing unique distance parameter algorithms is presented. A novel method to design the sequence impedances to specifications is presented. Utilizing impedance matrices in circulant form, a generalized form of the sequence components transformation matrix is presented. A generalized voltage unbalance factor in discussed for HPO transmission lines. Algorithms to calculate the number of fault types and number of significant fault types for an n-phase system are presented. A discussion is presented on transposition of HPO transmission lines and a generalized fault analysis of a high phase order circuit is presented along with an HPO analysis program.
The work presented has the objective of increasing the use of rights of way for bulk power transmission through the use of innovative transmission technologies. The purpose of this dissertation is to lay down some of the building blocks and to help make the three technologies discussed practical applications in the future.
ContributorsPierre, Brian J (Author) / Heydt, Gerald (Thesis advisor) / Karady, George G. (Committee member) / Shunk, Dan (Committee member) / Vittal, Vijay (Committee member) / Arizona State University (Publisher)
Created2015
Description
The ever-changing economic landscape has forced many companies to re-examine their supply chains. Global resourcing and outsourcing of processes has been a strategy many organizations have adopted to reduce cost and to increase their global footprint. This has, however, resulted in increased process complexity and reduced customer satisfaction. In order to meet and exceed customer expectations, many companies are forced to improve quality and on-time delivery, and have looked towards Lean Six Sigma as an approach to enable process improvement. The Lean Six Sigma literature is rich in deployment strategies; however, there is a general lack of a mathematical approach to deploy Lean Six Sigma in a global enterprise. This includes both project identification and prioritization. The research presented here is two-fold. Firstly, a process characterization framework is presented to evaluate processes based on eight characteristics. An unsupervised learning technique, using clustering algorithms, is then utilized to group processes that are Lean Six Sigma conducive. The approach helps Lean Six Sigma deployment champions to identify key areas within the business to focus a Lean Six Sigma deployment. A case study is presented and 33% of the processes were found to be Lean Six Sigma conducive. Secondly, having identified parts of the business that are lean Six Sigma conducive, the next steps are to formulate and prioritize a portfolio of projects. Very often the deployment champion is faced with the decision of selecting a portfolio of Lean Six Sigma projects that meet multiple objectives which could include: maximizing productivity, customer satisfaction or return on investment, while meeting certain budgetary constraints. A multi-period 0-1 knapsack problem is presented that maximizes the expected net savings of the Lean Six Sigma portfolio over the life cycle of the deployment. Finally, a case study is presented that demonstrates the application of the model in a large multinational company. Traditionally, Lean Six Sigma found its roots in manufacturing. The research presented in this dissertation also emphasizes the applicability of the methodology to the non-manufacturing space. Additionally, a comparison is conducted between manufacturing and non-manufacturing processes to highlight the challenges in deploying the methodology in both spaces.
ContributorsDuarte, Brett Marc (Author) / Fowler, John W (Thesis advisor) / Montgomery, Douglas C. (Thesis advisor) / Shunk, Dan (Committee member) / Borror, Connie (Committee member) / Konopka, John (Committee member) / Arizona State University (Publisher)
Created2011
Description
For more than twenty years, clinical researchers have been publishing data regarding incidence and risk of adverse events (AEs) incurred during hospitalizations. Hospitals have standard operating policies and procedures (SOPP) to protect patients from AE. The AE specifics (rates, SOPP failures, timing and risk factors) during heart failure (HF) hospitalizations are unknown. There were 1,722 patients discharged with a primary diagnosis of HF from an academic hospital between January 2005 and December 2007. Three hundred eighty-one patients experienced 566 AEs, classified into four categories: medication (43.9%), infection (18.9%), patient care (26.3%), or procedural (10.9%). Three distinct analyses were performed: 1) patient's perspective of SOPP reliability including cumulative distribution and hazard functions of time to AEs; 2) Cox proportional hazards model to determine independent patient-specific risk factors for AEs; and 3) hospital administration's perspective of SOPP reliability through three years of the study including cumulative distribution and hazard functions of time between AEs and moving range statistical process control (SPC) charts for days between failures of each type. This is the first study, to our knowledge, to consider reliability of SOPP from both the patient's and hospital administration's perspective. AE rates in hospitalized patients are similar to other recently published reports and did not improve during the study period. Operations research methodologies will be necessary to improve reliability of care delivered to hospitalized patients.
ContributorsHuddleston, Jeanne (Author) / Fowler, John (Thesis advisor) / Montgomery, Douglas C. (Thesis advisor) / Gel, Esma (Committee member) / Shunk, Dan (Committee member) / Arizona State University (Publisher)
Created2012
Description
Buildings (approximately half commercial and half residential) consume over 70% of the electricity among all the consumption units in the United States. Buildings are also responsible for approximately 40% of CO2 emissions, which is more than any other industry sectors. As a result, the initiative smart building which aims to not only manage electrical consumption in an efficient way but also reduce the damaging effect of greenhouse gases on the environment has been launched. Another important technology being promoted by government agencies is the smart grid which manages energy usage across a wide range of buildings in an effort to reduce cost and increase reliability and transparency. As a great amount of efforts have been devoted to these two initiatives by either exploring the smart grid designs or developing technologies for smart buildings, the research studying how the smart buildings and smart grid coordinate thus more efficiently use the energy is currently lacking. In this dissertation, a "system-of-system" approach is employed to develop an integrated building model which consists a number of buildings (building cluster) interacting with smart grid. The buildings can function as both energy consumption unit as well as energy generation/storage unit. Memetic Algorithm (MA) and Particle Swarm Optimization (PSO) based decision framework are developed for building operation decisions. In addition, Particle Filter (PF) is explored as a mean for fusing online sensor and meter data so adaptive decision could be made in responding to dynamic environment. The dissertation is divided into three inter-connected research components. First, an integrated building energy model including building consumption, storage, generation sub-systems for the building cluster is developed. Then a bi-level Memetic Algorithm (MA) based decentralized decision framework is developed to identify the Pareto optimal operation strategies for the building cluster. The Pareto solutions not only enable multiple dimensional tradeoff analysis, but also provide valuable insight for determining pricing mechanisms and power grid capacity. Secondly, a multi-objective PSO based decision framework is developed to reduce the computational effort of the MA based decision framework without scarifying accuracy. With the improved performance, the decision time scale could be refined to make it capable for hourly operation decisions. Finally, by integrating the multi-objective PSO based decision framework with PF, an adaptive framework is developed for adaptive operation decisions for smart building cluster. The adaptive framework not only enables me to develop a high fidelity decision model but also enables the building cluster to respond to the dynamics and uncertainties inherent in the system.
ContributorsHu, Mengqi (Author) / Wu, Teresa (Thesis advisor) / Weir, Jeffery (Thesis advisor) / Wen, Jin (Committee member) / Fowler, John (Committee member) / Shunk, Dan (Committee member) / Arizona State University (Publisher)
Created2012
Description
Photovoltaic (PV) power generation has the potential to cause a significant impact on power system reliability since its total installed capacity is projected to increase at a significant rate. PV generation can be described as an intermittent and variable resource because its production is influenced by ever-changing environmental conditions. The study in this dissertation focuses on the influence of PV generation on trans-mission system reliability. This is a concern because PV generation output is integrated into present power systems at various voltage levels and may significantly affect the power flow patterns. This dissertation applies a probabilistic power flow (PPF) algorithm to evaluate the influence of PV generation uncertainty on transmission system perfor-mance. A cumulant-based PPF algorithm suitable for large systems is used. Correlation among adjacent PV resources is considered. Three types of approximation expansions based on cumulants namely Gram-Charlier expansion, Edgeworth expansion and Cor-nish-Fisher expansion are compared, and their properties, advantages and deficiencies are discussed. Additionally, a novel probabilistic model of PV generation is developed to obtain the probability density function (PDF) of the PV generation production based on environmental conditions. Besides, this dissertation proposes a novel PPF algorithm considering the conven-tional generation dispatching operation to balance PV generation uncertainties. It is pru-dent to include generation dispatch in the PPF algorithm since the dispatching strategy compensates for PV generation injections and influences the uncertainty results. Fur-thermore, this dissertation also proposes a probabilistic optimal power dispatching strat-egy which considers uncertainty problems in the economic dispatch and optimizes the expected value of the total cost with the overload probability as a constraint. The proposed PPF algorithm with the three expansions is compared with Monte Carlo simulations (MCS) with results for a 2497-bus representation of the Arizona area of the Western Electricity Coordinating Council (WECC) system. The PDFs of the bus voltages, line flows and slack bus production are computed, and are used to identify the confidence interval, the over limit probability and the expected over limit time of the ob-jective variables. The proposed algorithm is of significant relevance to the operating and planning studies of the transmission systems with PV generation installed.
ContributorsFan, Miao (Author) / Vittal, Vijay (Thesis advisor) / Heydt, Gerald Thomas (Committee member) / Ayyanar, Raja (Committee member) / Si, Jennie (Committee member) / Arizona State University (Publisher)
Created2012
Description
This dissertation presents methods for the evaluation of ocular surface protection during natural blink function. The evaluation of ocular surface protection is especially important in the diagnosis of dry eye and the evaluation of dry eye severity in clinical trials. Dry eye is a highly prevalent disease affecting vast numbers (between 11% and 22%) of an aging population. There is only one approved therapy with limited efficacy, which results in a huge unmet need. The reason so few drugs have reached approval is a lack of a recognized therapeutic pathway with reproducible endpoints. While the interplay between blink function and ocular surface protection has long been recognized, all currently used evaluation techniques have addressed blink function in isolation from tear film stability, the gold standard of which is Tear Film Break-Up Time (TFBUT). In the first part of this research a manual technique of calculating ocular surface protection during natural blink function through the use of video analysis is developed and evaluated for it's ability to differentiate between dry eye and normal subjects, the results are compared with that of TFBUT. In the second part of this research the technique is improved in precision and automated through the use of video analysis algorithms. This software, called the OPI 2.0 System, is evaluated for accuracy and precision, and comparisons are made between the OPI 2.0 System and other currently recognized dry eye diagnostic techniques (e.g. TFBUT). In the third part of this research the OPI 2.0 System is deployed for use in the evaluation of subjects before, immediately after and 30 minutes after exposure to a controlled adverse environment (CAE), once again the results are compared and contrasted against commonly used dry eye endpoints. The results demonstrate that the evaluation of ocular surface protection using the OPI 2.0 System offers superior accuracy to the current standard, TFBUT.
ContributorsAbelson, Richard (Author) / Montgomery, Douglas C. (Thesis advisor) / Borror, Connie (Committee member) / Shunk, Dan (Committee member) / Pan, Rong (Committee member) / Arizona State University (Publisher)
Created2012
Description
The uncertainty and variability associated with stochastic resources, such as wind and solar, coupled with the stringent reliability requirements and constantly changing system operating conditions (e.g., generator and transmission outages) introduce new challenges to power systems. Contemporary approaches to model reserve requirements within the conventional security-constrained unit commitment (SCUC) models may not be satisfactory with increasing penetration levels of stochastic resources; such conventional models pro-cure reserves in accordance with deterministic criteria whose deliverability, in the event of an uncertain realization, is not guaranteed. Smart, well-designed reserve policies are needed to assist system operators in maintaining reliability at least cost.
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.
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.
ContributorsSinghal, Nikita Ghanshyam (Author) / Hedman, Kory W (Thesis advisor) / Vittal, Vijay (Committee member) / Sankar, Lalitha (Committee member) / Pal, Anamitra (Committee member) / Arizona State University (Publisher)
Created2018
Description
The current Enterprise Requirements and Acquisition Model (ERAM), a discrete event simulation of the major tasks and decisions within the DoD acquisition system, identifies several what-if intervention strategies to improve program completion time. However, processes that contribute to the program acquisition completion time were not explicitly identified in the simulation study. This research seeks to determine the acquisition processes that contribute significantly to total simulated program time in the acquisition system for all programs reaching Milestone C. Specifically, this research examines the effect of increased scope management, technology maturity, and decreased variation and mean process times in post-Design Readiness Review contractor activities by performing additional simulation analyses. Potential policies are formulated from the results to further improve program acquisition completion time.
ContributorsWorger, Danielle Marie (Author) / Wu, Teresa (Thesis director) / Shunk, Dan (Committee member) / Wirthlin, J. Robert (Committee member) / Industrial, Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2013-05
Description
Corrective transmission topology control schemes are an essential part of grid operations and are used to improve the reliability of the grid as well as the operational efficiency. However, topology control schemes are frequently established based on the operator's past knowledge of the system as well as other ad-hoc methods. This research presents robust corrective topology control, which is a transmission switching methodology used for system reliability as well as to facilitate renewable integration.
This research presents three topology control (corrective transmission switching) methodologies along with the detailed formulation of robust corrective switching. The robust model can be solved off-line to suggest switching actions that can be used in a dynamic security assessment tool in real-time. The proposed robust topology control algorithm can also generate multiple corrective switching actions for a particular contingency. The solution obtained from the robust topology control algorithm is guaranteed to be feasible for the entire uncertainty set, i.e., a range of system operating states.
Furthermore, this research extends the benefits of robust corrective topology control to renewable resource integration. In recent years, the penetration of renewable resources in electrical power systems has increased. These renewable resources add more complexities to power system operations, due to their intermittent nature. This research presents robust corrective topology control as a congestion management tool to manage power flows and the associated renewable uncertainty. The proposed day-ahead method determines the maximum uncertainty in renewable resources in terms of do-not-exceed limits combined with corrective topology control. The results obtained from the topology control algorithm are tested for system stability and AC feasibility.
The scalability of do-not-exceed limits problem, from a smaller test case to a realistic test case, is also addressed in this research. The do-not-exceed limit problem is simplified by proposing a zonal do-not-exceed limit formulation over a detailed nodal do-not-exceed limit formulation. The simulation results show that the zonal approach is capable of addressing scalability of the do-not-exceed limit problem for a realistic test case.
This research presents three topology control (corrective transmission switching) methodologies along with the detailed formulation of robust corrective switching. The robust model can be solved off-line to suggest switching actions that can be used in a dynamic security assessment tool in real-time. The proposed robust topology control algorithm can also generate multiple corrective switching actions for a particular contingency. The solution obtained from the robust topology control algorithm is guaranteed to be feasible for the entire uncertainty set, i.e., a range of system operating states.
Furthermore, this research extends the benefits of robust corrective topology control to renewable resource integration. In recent years, the penetration of renewable resources in electrical power systems has increased. These renewable resources add more complexities to power system operations, due to their intermittent nature. This research presents robust corrective topology control as a congestion management tool to manage power flows and the associated renewable uncertainty. The proposed day-ahead method determines the maximum uncertainty in renewable resources in terms of do-not-exceed limits combined with corrective topology control. The results obtained from the topology control algorithm are tested for system stability and AC feasibility.
The scalability of do-not-exceed limits problem, from a smaller test case to a realistic test case, is also addressed in this research. The do-not-exceed limit problem is simplified by proposing a zonal do-not-exceed limit formulation over a detailed nodal do-not-exceed limit formulation. The simulation results show that the zonal approach is capable of addressing scalability of the do-not-exceed limit problem for a realistic test case.
ContributorsKorad, Akshay Shashikumar (Author) / Hedman, Kory W (Thesis advisor) / Ayyanar, Raja (Committee member) / Vittal, Vijay (Committee member) / Zhang, Muhong (Committee member) / Arizona State University (Publisher)
Created2015