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- All Subjects: engineering
- Creators: Ayyanar, Raja
- Creators: Liu, Yongming
Description
The high penetration of photovoltaic (PV) both at the utility and at the distribu-tion levels, has raised concerns about the reliability of grid-tied inverters of PV power systems. Inverters are generally considered as the weak link in PV power systems. The lack of a dedicated qualification/reliability standard for PV inverters is a main barrier in realizing higher level of confidence in reliability. Development of a well-accepted design qualification standard specifically for PV inverters will help pave the way for significant improvement in reliability and performance of inverters across the entire industry. The existing standards for PV inverters such as UL 1741 and IEC 62109-1 primarily focus on safety. IEC 62093 discusses inverter qualification but it includes all the balance of sys-tem components and therefore not specific to PV inverters. There are other general stan-dards for distributed generators including the IEEE1547 series of standards which cover major concerns like utility integration but they are not dedicated to PV inverters and are not written from a design qualification point of view. In this thesis, some of the potential requirements for a design qualification standard for PV inverters are addressed. The IEC 62093 is considered as a guideline and the possible inclusions in the framework for a dedicated design qualification standard of PV inverter are discussed. The missing links in existing PV inverter related standards are identified by performing gap analysis. Dif-ferent requirements of small residential inverters compared to large utility-scale systems, and the emerging requirements on grid support features are also considered. Electric stress test is found to be the key missing link and one of the electric stress tests, the surge withstand test is studied in detail. The use of the existing standards for surge withstand test of residential scale PV inverters is investigated and a method to suitably adopt these standards is proposed. The proposed method is studied analytically and verified using simulation. A design criterion for choosing the switch ratings of the inverter that can per-form reliably under the surge environment is derived.
ContributorsAlampoondi Venkataramanan, Sai Balasubramanian (Author) / Ayyanar, Raja (Thesis advisor) / Vittal, Vijay (Committee member) / Heydt, Gerald (Committee member) / Arizona State University (Publisher)
Created2011
Description
In modern electric power systems, energy management systems (EMSs) are responsi-ble for monitoring and controlling the generation system and transmission networks. State estimation (SE) is a critical `must run successful' component within the EMS software. This is dictated by the high reliability requirements and need to represent the closest real time model for market operations and other critical analysis functions in the EMS. Tradi-tionally, SE is run with data obtained only from supervisory control and data acquisition (SCADA) devices and systems. However, more emphasis on improving the performance of SE drives the inclusion of phasor measurement units (PMUs) into SE input data. PMU measurements are claimed to be more accurate than conventional measurements and PMUs `time stamp' measurements accurately. These widely distributed devices meas-ure the voltage phasors directly. That is, phase information for measured voltages and currents are available. PMUs provide data time stamps to synchronize measurements. Con-sidering the relatively small number of PMUs installed in contemporary power systems in North America, performing SE with only phasor measurements is not feasible. Thus a hy-brid SE, including both SCADA and PMU measurements, is the reality for contemporary power system SE. The hybrid approach is the focus of a number of research papers. There are many practical challenges in incorporating PMUs into SE input data. The higher reporting rates of PMUs as compared with SCADA measurements is one of the salient problems. The disparity of reporting rates raises a question whether buffering the phasor measurements helps to give better estimates of the states. The research presented in this thesis addresses the design of data buffers for PMU data as used in SE applications in electric power systems. The system theoretic analysis is illustrated using an operating electric power system in the southwest part of the USA. Var-ious instances of state estimation data have been used for analysis purposes. The details of the research, results obtained and conclusions drawn are presented in this document.
ContributorsMurugesan, Veerakumar (Author) / Vittal, Vijay (Committee member) / Heydt, Gerald (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2013
Description
Solar power generation is the most promising technology to transfer energy consumption reliance from fossil fuel to renewable sources. Concentrated solar power generation is a method to concentrate the sunlight from a bigger area to a smaller area. The collected sunlight is converted more efficiently through two types of technologies: concentrated solar photovoltaics (CSPV) and concentrated solar thermal power (CSTP) generation. In this thesis, these two technologies were evaluated in terms of system construction, performance characteristics, design considerations, cost benefit analysis and their field experience. The two concentrated solar power generation systems were implemented with similar solar concentrators and solar tracking systems but with different energy collecting and conversion components: the CSPV system uses high efficiency multi-junction solar cell modules, while the CSTP system uses a boiler -turbine-generator setup. The performances are calibrated via the experiments and evaluation analysis.
ContributorsJin, Zhilei (Author) / Hui, Yu (Thesis advisor) / Ayyanar, Raja (Committee member) / Rodriguez, Armando (Committee member) / Arizona State University (Publisher)
Created2013
Description
Carrier lifetime is one of the few parameters which can give information about the low defect densities in today's semiconductors. In principle there is no lower limit to the defect density determined by lifetime measurements. No other technique can easily detect defect densities as low as 10-9 - 10-10 cm-3 in a simple, contactless room temperature measurement. However in practice, recombination lifetime τr measurements such as photoconductance decay (PCD) and surface photovoltage (SPV) that are widely used for characterization of bulk wafers face serious limitations when applied to thin epitaxial layers, where the layer thickness is smaller than the minority carrier diffusion length Ln. Other methods such as microwave photoconductance decay (µ-PCD), photoluminescence (PL), and frequency-dependent SPV, where the generated excess carriers are confined to the epitaxial layer width by using short excitation wavelengths, require complicated configuration and extensive surface passivation processes that make them time-consuming and not suitable for process screening purposes. Generation lifetime τg, typically measured with pulsed MOS capacitors (MOS-C) as test structures, has been shown to be an eminently suitable technique for characterization of thin epitaxial layers. It is for these reasons that the IC community, largely concerned with unipolar MOS devices, uses lifetime measurements as a "process cleanliness monitor." However when dealing with ultraclean epitaxial wafers, the classic MOS-C technique measures an effective generation lifetime τg eff which is dominated by the surface generation and hence cannot be used for screening impurity densities. I have developed a modified pulsed MOS technique for measuring generation lifetime in ultraclean thin p/p+ epitaxial layers which can be used to detect metallic impurities with densities as low as 10-10 cm-3. The widely used classic version has been shown to be unable to effectively detect such low impurity densities due to the domination of surface generation; whereas, the modified version can be used suitably as a metallic impurity density monitoring tool for such cases.
ContributorsElhami Khorasani, Arash (Author) / Alford, Terry (Thesis advisor) / Goryll, Michael (Committee member) / Bertoni, Mariana (Committee member) / Arizona State University (Publisher)
Created2013
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
The constant scaling of supply voltages in state-of-the-art CMOS processes has led to severe limitations for many analog circuit applications. Some CMOS processes have addressed this issue by adding high voltage MOSFETs to their process. Although it can be a completely viable solution, it usually requires a changing of the process flow or adding additional steps, which in turn, leads to an increase in fabrication costs. Si-MESFETs (silicon-metal-semiconductor-field-effect-transistors) from Arizona State University (ASU) on the other hand, have an inherent high voltage capability and can be added to any silicon-on-insulator (SOI) or silicon-on-sapphire (SOS) CMOS process free of cost. This has been proved at five different commercial foundries on technologies ranging from 0.5 to 0.15 μm. Another critical issue facing CMOS processes on insulated substrates is the scaling of the thin silicon channel. Consequently, the future direction of SOI/SOS CMOS transistors may trend away from partially depleted (PD) transistors and towards fully depleted (FD) devices. FD-CMOS are already being implemented in multiple applications due to their very low power capability. Since the FD-CMOS market only figures to grow, it is appropriate that MESFETs also be developed for these processes. The beginning of this thesis will focus on the device aspects of both PD and FD-MESFETs including their layout structure, DC and RF characteristics, and breakdown voltage. The second half will then shift the focus towards implementing both types of MESFETs in an analog circuit application. Aside from their high breakdown ability, MESFETs also feature depletion mode operation, easy to adjust but well controlled threshold voltages, and fT's up to 45 GHz. Those unique characteristics can allow certain designs that were previously difficult to implement or prohibitively expensive using conventional technologies to now be achieved. One such application which benefits is low dropout regulators (LDO). By utilizing an n-channel MESFET as the pass transistor, a LDO featuring very low dropout voltage, fast transient response, and stable operation can be achieved without an external capacitance. With the focus of this thesis being MESFET based LDOs, the device discussion will be mostly tailored towards optimally designing MESFETs for this particular application.
ContributorsLepkowski, William (Author) / Thornton, Trevor (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Goryll, Michael (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2010
Description
This research examines the current challenges of using Lamb wave interrogation methods to localize fatigue crack damage in a complex metallic structural component subjected to unknown temperatures. The goal of this work is to improve damage localization results for a structural component interrogated at an unknown temperature, by developing a probabilistic and reference-free framework for estimating Lamb wave velocities and the damage location. The methodology for damage localization at unknown temperatures includes the following key elements: i) a model that can describe the change in Lamb wave velocities with temperature; ii) the extension of an advanced time-frequency based signal processing technique for enhanced time-of-flight feature extraction from a dispersive signal; iii) the development of a Bayesian damage localization framework incorporating data association and sensor fusion. The technique requires no additional transducers to be installed on a structure, and allows for the estimation of both the temperature and the wave velocity in the component. Additionally, the framework of the algorithm allows it to function completely in an unsupervised manner by probabilistically accounting for all measurement origin uncertainty. The novel algorithm was experimentally validated using an aluminum lug joint with a growing fatigue crack. The lug joint was interrogated using piezoelectric transducers at multiple fatigue crack lengths, and at temperatures between 20°C and 80°C. The results showed that the algorithm could accurately predict the temperature and wave speed of the lug joint. The localization results for the fatigue damage were found to correlate well with the true locations at long crack lengths, but loss of accuracy was observed in localizing small cracks due to time-of-flight measurement errors. To validate the algorithm across a wider range of temperatures the electromechanically coupled LISA/SIM model was used to simulate the effects of temperatures. The numerical results showed that this approach would be capable of experimentally estimating the temperature and velocity in the lug joint for temperatures from -60°C to 150°C. The velocity estimation algorithm was found to significantly increase the accuracy of localization at temperatures above 120°C when error due to incorrect velocity selection begins to outweigh the error due to time-of-flight measurements.
ContributorsHensberry, Kevin (Author) / Chattopadhyay, Aditi (Thesis advisor) / Liu, Yongming (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2013
Description
Structural/system health monitoring (SHM) and prognostic health management (PHM) are vital techniques to ensure engineering system reliability and safety during the service. As multi-functionality and enhanced performance are in demand, modern engineering systems including aerospace, mechanical, and civil applications have become more complex. The constituent and architectural complexity, and multisource sensing sources in modern engineering systems may limit the monitoring capabilities of conventional approaches and require more advanced SHM/PHM techniques. Therefore, a hybrid methodology that incorporates information fusion, nondestructive evaluation (NDE), machine learning (ML), and statistical analysis is needed for more effective damage diagnosis/prognosis and system safety management.This dissertation presents an automated aviation health management technique to enable proactive safety management for both aircraft and national airspace system (NAS). A real-time, data-driven aircraft safety monitoring technique using ML models and statistical models is developed to enable an early-stage upset detection capability, which can improve pilot’s situational awareness and provide a sufficient safety margin. The detection accuracy and computational efficiency of the developed monitoring techniques is validated using commercial unlabeled flight data recorder (FDR) and reported accident FDR dataset. A stochastic post-upset prediction framework is developed using a high-fidelity flight dynamics model to predict the post-impacts in both aircraft and air traffic system. Stall upset scenarios that are most likely occurred during loss of control in-flight (LOC-I) operation are investigated, and stochastic flight envelopes and risk region are predicted to quantify their severities.
In addition, a robust, automatic damage diagnosis technique using ultrasonic Lamb waves and ML models is developed to effectively detect and classify fatigue damage modes in composite structures. The dispersion and propagation characteristics of the Lamb waves in a composite plate are investigated. A deep autoencoder-based diagnosis technique is proposed to detect fatigue damage using anomaly detection approach and automatically extract damage sensitive features from the waves. The patterns in the features are then further analyzed using outlier detection approach to classify the fatigue damage modes. The developed diagnosis technique is validated through an in-situ fatigue tests with periodic active sensing.
The developed techniques in this research are expected to be integrated with the existing safety strategies to enhance decision making process for improving engineering system safety without affecting the system’s functions.
ContributorsLee, Hyunseong (Author) / Chattopadhyay, Aditi (Thesis advisor) / Liu, Yongming (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Fard, Masoud Yekani (Committee member) / Tang, Pingbo (Committee member) / Campbell, Angela (Committee member) / Arizona State University (Publisher)
Created2021
Description
National Airspace Systems (NAS) are complex cyber-physical systems that require swift air traffic management (ATM) to ensure flight safety and efficiency. With the surging demand for air travel and the increasing intricacy of aviation systems, the need for advanced technologies to support air traffic management and air traffic control (ATC) service has become more crucial than ever. Data-driven models or artificial intelligence (AI) have been conceptually investigated by various parties and shown immense potential, especially when provided with a vast volume of real-world data. These data include traffic information, weather contours, operational reports, terrain information, flight procedures, and aviation regulations. Data-driven models learn from historical experiences and observations and provide expeditious recommendations and decision support for various operation tasks, directly contributing to the digital transformation in aviation. This dissertation reports several research studies covering different aspects of air traffic management and ATC service utilizing data-driven modeling, which are validated using real-world big data (flight tracks, flight events, convective weather, workload probes). These studies encompass a range of topics, including trajectory recommendations, weather studies, landing operations, and aviation human factors. Specifically, the topics explored are (i) trajectory recommendations under weather conditions, which examine the impact of convective weather on last on-file flight plans and provide calibrated trajectories based on convective weather; (ii) multi-aircraft trajectory predictions, which study the intention of multiple mid-air aircraft in the near-terminal airspace and provide trajectory predictions; (iii) flight scheduling operations, which involve probabilistic machine learning-enhanced optimization algorithms for robust and efficient aircraft landing sequencing; (iv) aviation human factors, which predict air traffic controller workload level from flight traffic data with conformalized graph neural network. The uncertainties associated with these studies are given special attention and addressed through Bayesian/probabilistic machine learning. Finally, discussions on high-level AI-enabled ATM research directions are provided, hoping to extend the proposed studies in the future. This dissertation demonstrates that data-driven modeling has great potential for aviation digital twins, revolutionizing the aviation decision-making process and enhancing the safety and efficiency of ATM. Moreover, these research directions are not merely add-ons to existing aviation practices but also contribute to the future of transportation, particularly in the development of autonomous systems.
ContributorsPang, Yutian (Author) / Liu, Yongming (Thesis advisor) / Yan, Hao (Committee member) / Zhuang, Houlong (Committee member) / Marvi, Hamid (Committee member) / Ren, Yi (Committee member) / Arizona State University (Publisher)
Created2023
Description
The design of energy absorbing structures is driven by application specific requirements like the amount of energy to be absorbed, maximum transmitted stress that is permissible, stroke length, and available enclosing space. Cellular structures like foams are commonly leveraged in nature for energy absorption and have also found use in engineering applications. With the possibility of manufacturing complex cellular shapes using additive manufacturing technologies, there is an opportunity to explore new topologies that improve energy absorption performance. This thesis aims to systematically understand the relationships between four key elements: (i) unit cell topology, (ii) material composition, (iii) relative density, and (iv) fields; and energy absorption behavior, and then leverage this understanding to develop, implement and validate a methodology to design the ideal cellular structure energy absorber. After a review of the literature in the domain of additively manufactured cellular materials for energy absorption, results from quasi-static compression of six cellular structures (hexagonal honeycomb, auxetic and Voronoi lattice, and diamond, Gyroid, and Schwarz-P) manufactured out of AlSi10Mg and Nylon-12. These cellular structures were compared to each other in the context of four design-relevant metrics to understand the influence of cell design on the deformation and failure behavior. Three new and revised metrics for energy absorption were proposed to enable more meaningful comparisons and subsequent design selection. Triply Periodic Minimal Surface (TPMS) structures were found to have the most promising overall performance and formed the basis for the numerical investigation of the effect of fields on the energy absorption performance of TPMS structures. A continuum shell-based methodology was developed to analyze the large deformation behavior of field-driven variable thickness TPMS structures and validated against experimental data. A range of analytical and stochastic fields were then evaluated that modified the TPMS structure, some of which were found to be effective in enhancing energy absorption behavior in the structures while retaining the same relative density. Combining findings from studies on the role of cell geometry, composition, relative density, and fields, this thesis concludes with the development of a design framework that can enable the formulation of cellular material energy absorbers with idealized behavior.
ContributorsShinde, Mandar (Author) / Bhate, Dhruv (Thesis advisor) / Peralta, Pedro (Committee member) / Liu, Yongming (Committee member) / Jiao, Yang (Committee member) / Kwon, Beomjin (Committee member) / Arizona State University (Publisher)
Created2023