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- All Subjects: engineering
- Creators: Ayyanar, Raja
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
Photovoltaics (PV) is an important and rapidly growing area of research. With the advent of power system monitoring and communication technology collectively known as the "smart grid," an opportunity exists to apply signal processing techniques to monitoring and control of PV arrays. In this paper a monitoring system which provides real-time measurements of each PV module's voltage and current is considered. A fault detection algorithm formulated as a clustering problem and addressed using the robust minimum covariance determinant (MCD) estimator is described; its performance on simulated instances of arc and ground faults is evaluated. The algorithm is found to perform well on many types of faults commonly occurring in PV arrays. Among several types of detection algorithms considered, only the MCD shows high performance on both types of faults.
ContributorsBraun, Henry (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Spanias, Andreas (Thesis advisor) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2012
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 thesis encompasses a comprehensive research effort dedicated to overcoming the critical bottlenecks that hinder the current generation of neural networks, thereby significantly advancing their reliability and performance. Deep neural networks, with their millions of parameters, suffer from over-parameterization and lack of constraints, leading to limited generalization capabilities. In other words, the complex architecture and millions of parameters present challenges in finding the right balance between capturing useful patterns and avoiding noise in the data. To address these issues, this thesis explores novel solutions based on knowledge distillation, enabling the learning of robust representations. Leveraging the capabilities of large-scale networks, effective learning strategies are developed. Moreover, the limitations of dependency on external networks in the distillation process, which often require large-scale models, are effectively overcome by proposing a self-distillation strategy. The proposed approach empowers the model to generate high-level knowledge within a single network, pushing the boundaries of knowledge distillation. The effectiveness of the proposed method is not only demonstrated across diverse applications, including image classification, object detection, and semantic segmentation but also explored in practical considerations such as handling data scarcity and assessing the transferability of the model to other learning tasks. Another major obstacle hindering the development of reliable and robust models lies in their black-box nature, impeding clear insights into the contributions toward the final predictions and yielding uninterpretable feature representations. To address this challenge, this thesis introduces techniques that incorporate simple yet powerful deep constraints rooted in Riemannian geometry. These constraints confer geometric qualities upon the latent representation, thereby fostering a more interpretable and insightful representation. In addition to its primary focus on general tasks like image classification and activity recognition, this strategy offers significant benefits in real-world applications where data scarcity is prevalent. Moreover, its robustness in feature removal showcases its potential for edge applications. By successfully tackling these challenges, this research contributes to advancing the field of machine learning and provides a foundation for building more reliable and robust systems across various application domains.
ContributorsChoi, Hongjun (Author) / Turaga, Pavan (Thesis advisor) / Jayasuriya, Suren (Committee member) / Li, Wenwen (Committee member) / Fazli, Pooyan (Committee member) / Arizona State University (Publisher)
Created2023
Description
Severe forms of mental illness, such as schizophrenia and bipolar disorder, are debilitating conditions that negatively impact an individual's quality of life. Additionally, they are often difficult and expensive to diagnose and manage, placing a large burden on society. Mental illness is typically diagnosed by the use of clinical interviews and a set of neuropsychiatric batteries; a key component of nearly all of these evaluations is some spoken language task. Clinicians have long used speech and language production as a proxy for neurological health, but most of these assessments are subjective in nature. Meanwhile, technological advancements in speech and natural language processing have grown exponentially over the past decade, increasing the capacity of computer models to assess particular aspects of speech and language. For this reason, many have seen an opportunity to leverage signal processing and machine learning applications to objectively assess clinical speech samples in order to automatically compute objective measures of neurological health. This document summarizes several contributions to expand upon this body of research. Mainly, there is still a large gap between the theoretical power of computational language models and their actual use in clinical applications. One of the largest concerns is the limited and inconsistent reliability of speech and language features used in models for assessing specific aspects of mental health; numerous methods may exist to measure the same or similar constructs and lead researchers to different conclusions in different studies. To address this, a novel measurement model based on a theoretical framework of speech production is used to motivate feature selection, while also performing a smoothing operation on features across several domains of interest. Then, these composite features are used to perform a much wider range of analyses than is typical of previous studies, looking at everything from diagnosis to functional competency assessments. Lastly, potential improvements to address practical implementation challenges associated with the use of speech and language technology in a real-world environment are investigated. The goal of this work is to demonstrate the ability of speech and language technology to aid clinical practitioners toward improvements in quality of life outcomes for their patients.
ContributorsVoleti, Rohit Nihar Uttam (Author) / Berisha, Visar (Thesis advisor) / Liss, Julie M (Thesis advisor) / Turaga, Pavan (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Created2022
Description
The most important metrics considered for electric vehicles are power density, efficiency, and reliability of the powertrain modules. The powertrain comprises of an Electric Machine (EM), power electronic converters, an Energy Management System (EMS), and an Energy Storage System (ESS). The power electronic converters are used to couple the motor with the battery stack. Including a DC/DC converter in the powertrain module is favored as it adds an additional degree of freedom to achieve flexibility in optimizing the battery module and inverter independently. However, it is essential that the converter is rated for high peak power and can maintain high efficiency while operating over a wide range of load conditions to not compromise on system efficiency. Additionally, the converter must strictly adhere to all automotive standards.
Currently, several hard-switching topologies have been employed such as conventional boost DC/DC, interleaved step-up DC/DC, and full-bridge DC/DC converter. These converters face respective limitations in achieving high step-up conversion ratio, size and weight issues, or high component count. In this work, a bi-directional synchronous boost DC/DC converter with easy interleaving capability is proposed with a novel ZVT mechanism. This converter steps up the EV battery voltage of 200V-300V to a wide range of variable output voltages ranging from 310V-800V. High power density and efficiency are achieved through high switching frequency of 250kHz for each phase with effective frequency doubling through interleaving. Also, use of wide bandgap high voltage SiC switches allows high efficiency operation even at high temperatures.
Comprehensive analysis, design details and extensive simulation results are presented. Incorporating ZVT branch with adaptive time delay results in converter efficiency close to 98%. Experimental results from a 2.5kW hardware prototype validate the performance of the proposed approach. A peak efficiency of 98.17% has been observed in hardware in the boost or motoring mode.
Currently, several hard-switching topologies have been employed such as conventional boost DC/DC, interleaved step-up DC/DC, and full-bridge DC/DC converter. These converters face respective limitations in achieving high step-up conversion ratio, size and weight issues, or high component count. In this work, a bi-directional synchronous boost DC/DC converter with easy interleaving capability is proposed with a novel ZVT mechanism. This converter steps up the EV battery voltage of 200V-300V to a wide range of variable output voltages ranging from 310V-800V. High power density and efficiency are achieved through high switching frequency of 250kHz for each phase with effective frequency doubling through interleaving. Also, use of wide bandgap high voltage SiC switches allows high efficiency operation even at high temperatures.
Comprehensive analysis, design details and extensive simulation results are presented. Incorporating ZVT branch with adaptive time delay results in converter efficiency close to 98%. Experimental results from a 2.5kW hardware prototype validate the performance of the proposed approach. A peak efficiency of 98.17% has been observed in hardware in the boost or motoring mode.
ContributorsMullangi Chenchu, Hemanth (Author) / Ayyanar, Raja (Thesis advisor) / Qin, Jiangchao (Committee member) / Lei, Qin (Committee member) / Arizona State University (Publisher)
Created2018
Description
Despite the fact that machine learning supports the development of computer vision applications by shortening the development cycle, finding a general learning algorithm that solves a wide range of applications is still bounded by the ”no free lunch theorem”. The search for the right algorithm to solve a specific problem is driven by the problem itself, the data availability and many other requirements.
Automated visual inspection (AVI) systems represent a major part of these challenging computer vision applications. They are gaining growing interest in the manufacturing industry to detect defective products and keep these from reaching customers. The process of defect detection and classification in semiconductor units is challenging due to different acceptable variations that the manufacturing process introduces. Other variations are also typically introduced when using optical inspection systems due to changes in lighting conditions and misalignment of the imaged units, which makes the defect detection process more challenging.
In this thesis, a BagStack classification framework is proposed, which makes use of stacking and bagging concepts to handle both variance and bias errors. The classifier is designed to handle the data imbalance and overfitting problems by adaptively transforming the
multi-class classification problem into multiple binary classification problems, applying a bagging approach to train a set of base learners for each specific problem, adaptively specifying the number of base learners assigned to each problem, adaptively specifying the number of samples to use from each class, applying a novel data-imbalance aware cross-validation technique to generate the meta-data while taking into account the data imbalance problem at the meta-data level and, finally, using a multi-response random forest regression classifier as a meta-classifier. The BagStack classifier makes use of multiple features to solve the defect classification problem. In order to detect defects, a locally adaptive statistical background modeling is proposed. The proposed BagStack classifier outperforms state-of-the-art image classification techniques on our dataset in terms of overall classification accuracy and average per-class classification accuracy. The proposed detection method achieves high performance on the considered dataset in terms of recall and precision.
Automated visual inspection (AVI) systems represent a major part of these challenging computer vision applications. They are gaining growing interest in the manufacturing industry to detect defective products and keep these from reaching customers. The process of defect detection and classification in semiconductor units is challenging due to different acceptable variations that the manufacturing process introduces. Other variations are also typically introduced when using optical inspection systems due to changes in lighting conditions and misalignment of the imaged units, which makes the defect detection process more challenging.
In this thesis, a BagStack classification framework is proposed, which makes use of stacking and bagging concepts to handle both variance and bias errors. The classifier is designed to handle the data imbalance and overfitting problems by adaptively transforming the
multi-class classification problem into multiple binary classification problems, applying a bagging approach to train a set of base learners for each specific problem, adaptively specifying the number of base learners assigned to each problem, adaptively specifying the number of samples to use from each class, applying a novel data-imbalance aware cross-validation technique to generate the meta-data while taking into account the data imbalance problem at the meta-data level and, finally, using a multi-response random forest regression classifier as a meta-classifier. The BagStack classifier makes use of multiple features to solve the defect classification problem. In order to detect defects, a locally adaptive statistical background modeling is proposed. The proposed BagStack classifier outperforms state-of-the-art image classification techniques on our dataset in terms of overall classification accuracy and average per-class classification accuracy. The proposed detection method achieves high performance on the considered dataset in terms of recall and precision.
ContributorsHaddad, Bashar Muneer (Author) / Karam, Lina (Thesis advisor) / Li, Baoxin (Committee member) / He, Jingrui (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2019