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- All Subjects: Electrical Engineering
- All Subjects: Electric network topology
- Creators: Vittal, Vijay
- Creators: Tepedelenlioğlu, Cihan
- Member of: Theses and Dissertations
Description
With the rapid growth of power systems and the concomitant technological advancements, the goal of achieving smart grids is no longer a vision but a foreseeable reality. Hence, the existing grids are undergoing infrastructural modifications to achieve the diverse characteristics of a smart grid. While there are many subjects associated with the operation of smart grids, this dissertation addresses two important aspects of smart grids: increased penetration of renewable resources, and increased reliance on sensor systems to improve reliability and performance of critical power system components. Present renewable portfolio standards are changing both structural and performance characteristics of power systems by replacing conventional generation with alternate energy resources such as photovoltaic (PV) systems. The present study investigates the impact of increased penetration of PV systems on steady state performance as well as transient stability of a large power system which is a portion of the Western U.S. interconnection. Utility scale and residential rooftop PVs are added to replace a portion of conventional generation resources. While steady state voltages are observed under various PV penetration levels, the impact of reduced inertia on transient stability performance is also examined. The simulation results obtained effectively identify both detrimental and beneficial impacts of increased PV penetration both for steady state stability and transient stability performance. With increased penetration of the renewable energy resources, and with the current loading scenario, more transmission system components such as transformers and circuit breakers are subject to increased stress and overloading. This research work explores the feasibility of increasing system reliability by applying condition monitoring systems to selected circuit breakers and transformers. A very important feature of smart grid technology is that this philosophy decreases maintenance costs by deploying condition monitoring systems that inform the operator of impending failures; or the approach can ameliorate problematic conditions. A method to identify the most critical transformers and circuit breakers with the aid of contingency ranking methods is presented in this study. The work reported in this dissertation parallels an industry sponsored study in which a considerable level of industry input and industry reported concerns are reflected.
ContributorsEftekharnejad, Sara (Author) / Heydt, Gerald (Thesis advisor) / Vittal, Vijay (Thesis advisor) / Si, Jennie (Committee member) / Tylavsky, Daniel (Committee member) / Arizona State University (Publisher)
Created2012
Description
Asymptotic comparisons of ergodic channel capacity at high and low signal-to-noise ratios (SNRs) are provided for several adaptive transmission schemes over fading channels with general distributions, including optimal power and rate adaptation, rate adaptation only, channel inversion and its variants. Analysis of the high-SNR pre-log constants of the ergodic capacity reveals the existence of constant capacity difference gaps among the schemes with a pre-log constant of 1. Closed-form expressions for these high-SNR capacity difference gaps are derived, which are proportional to the SNR loss between these schemes in dB scale. The largest one of these gaps is found to be between the optimal power and rate adaptation scheme and the channel inversion scheme. Based on these expressions it is shown that the presence of space diversity or multi-user diversity makes channel inversion arbitrarily close to achieving optimal capacity at high SNR with sufficiently large number of antennas or users. A low-SNR analysis also reveals that the presence of fading provably always improves capacity at sufficiently low SNR, compared to the additive white Gaussian noise (AWGN) case. Numerical results are shown to corroborate our analytical results. This dissertation derives high-SNR asymptotic average error rates over fading channels by relating them to the outage probability, under mild assumptions. The analysis is based on the Tauberian theorem for Laplace-Stieltjes transforms which is grounded on the notion of regular variation, and applies to a wider range of channel distributions than existing approaches. The theory of regular variation is argued to be the proper mathematical framework for finding sufficient and necessary conditions for outage events to dominate high-SNR error rate performance. It is proved that the diversity order being d and the cumulative distribution function (CDF) of the channel power gain having variation exponent d at 0 imply each other, provided that the instantaneous error rate is upper-bounded by an exponential function of the instantaneous SNR. High-SNR asymptotic average error rates are derived for specific instantaneous error rates. Compared to existing approaches in the literature, the asymptotic expressions are related to the channel distribution in a much simpler manner herein, and related with outage more intuitively. The high-SNR asymptotic error rate is also characterized under diversity combining schemes with the channel power gain of each branch having a regularly varying CDF. Numerical results are shown to corroborate our theoretical analysis. This dissertation studies several problems concerning channel inclusion, which is a partial ordering between discrete memoryless channels (DMCs) proposed by Shannon. Specifically, majorization-based conditions are derived for channel inclusion between certain DMCs. Furthermore, under general conditions, channel equivalence defined through Shannon ordering is shown to be the same as permutation of input and output symbols. The determination of channel inclusion is considered as a convex optimization problem, and the sparsity of the weights related to the representation of the worse DMC in terms of the better one is revealed when channel inclusion holds between two DMCs. For the exploitation of this sparsity, an effective iterative algorithm is established based on modifying the orthogonal matching pursuit algorithm. The extension of channel inclusion to continuous channels and its application in ordering phase noises are briefly addressed.
ContributorsZhang, Yuan (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Zhang, Junshan (Committee member) / Reisslein, Martin (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Created2013
Description
Image understanding has been playing an increasingly crucial role in vision applications. Sparse models form an important component in image understanding, since the statistics of natural images reveal the presence of sparse structure. Sparse methods lead to parsimonious models, in addition to being efficient for large scale learning. In sparse modeling, data is represented as a sparse linear combination of atoms from a "dictionary" matrix. This dissertation focuses on understanding different aspects of sparse learning, thereby enhancing the use of sparse methods by incorporating tools from machine learning. With the growing need to adapt models for large scale data, it is important to design dictionaries that can model the entire data space and not just the samples considered. By exploiting the relation of dictionary learning to 1-D subspace clustering, a multilevel dictionary learning algorithm is developed, and it is shown to outperform conventional sparse models in compressed recovery, and image denoising. Theoretical aspects of learning such as algorithmic stability and generalization are considered, and ensemble learning is incorporated for effective large scale learning. In addition to building strategies for efficiently implementing 1-D subspace clustering, a discriminative clustering approach is designed to estimate the unknown mixing process in blind source separation. By exploiting the non-linear relation between the image descriptors, and allowing the use of multiple features, sparse methods can be made more effective in recognition problems. The idea of multiple kernel sparse representations is developed, and algorithms for learning dictionaries in the feature space are presented. Using object recognition experiments on standard datasets it is shown that the proposed approaches outperform other sparse coding-based recognition frameworks. Furthermore, a segmentation technique based on multiple kernel sparse representations is developed, and successfully applied for automated brain tumor identification. Using sparse codes to define the relation between data samples can lead to a more robust graph embedding for unsupervised clustering. By performing discriminative embedding using sparse coding-based graphs, an algorithm for measuring the glomerular number in kidney MRI images is developed. Finally, approaches to build dictionaries for local sparse coding of image descriptors are presented, and applied to object recognition and image retrieval.
ContributorsJayaraman Thiagarajan, Jayaraman (Author) / Spanias, Andreas (Thesis advisor) / Frakes, David (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Today, the electric power system faces new challenges from rapid developing technology and the growing concern about environmental problems. The future of the power system under these new challenges needs to be planned and studied. However, due to the high degree of computational complexity of the optimization problem, conducting a system planning study which takes into account the market structure and environmental constraints on a large-scale power system is computationally taxing. To improve the execution time of large system simulations, such as the system planning study, two possible strategies are proposed in this thesis. The first one is to implement a relative new factorization method, known as the multifrontal method, to speed up the solution of the sparse linear matrix equations within the large system simulations. The performance of the multifrontal method implemented by UMFAPACK is compared with traditional LU factorization on a wide range of power-system matrices. The results show that the multifrontal method is superior to traditional LU factorization on relatively denser matrices found in other specialty areas, but has poor performance on the more sparse matrices that occur in power-system applications. This result suggests that multifrontal methods may not be an effective way to improve execution time for large system simulation and power system engineers should evaluate the performance of the multifrontal method before applying it to their applications. The second strategy is to develop a small dc equivalent of the large-scale network with satisfactory accuracy for the large-scale system simulations. In this thesis, a modified Ward equivalent is generated for a large-scale power system, such as the full Electric Reliability Council of Texas (ERCOT) system. In this equivalent, all the generators in the full model are retained integrally. The accuracy of the modified Ward equivalent is validated and the equivalent is used to conduct the optimal generation investment planning study. By using the dc equivalent, the execution time for optimal generation investment planning is greatly reduced. Different scenarios are modeled to study the impact of fuel prices, environmental constraints and incentives for renewable energy on future investment and retirement in generation.
ContributorsLi, Nan (Author) / Tylavsky, Daniel J (Thesis advisor) / Vittal, Vijay (Committee member) / Hedman, Kory W (Committee member) / Arizona State University (Publisher)
Created2012
Description
A distributed-parameter model is developed for a pressurized water reactor (PWR) in order to analyze the frequency behavior of the nuclear reactor. The model is built based upon the partial differential equations describing heat transfer and fluid flow in the reactor core. As a comparison, a multi-lump reactor core model with five fuel lumps and ten coolant lumps using Mann's model is employed. The derivations of the different transfer functions in both models are also presented with emphasis on the distributed parameter. In order to contrast the two models, Bode plots of the transfer functions are generated using data from the Palo Verde Nuclear Generating Station. Further, a detailed contradistinction between these two models is presented. From the comparison, the features of both models are presented. The distributed parameter model has the ability to offer an accurate transfer function at any location throughout the reactor core. In contrast, the multi-lump parameter model can only provide the average value in a given region (lump). Also, in the distributed parameter model only the feedback according to the specific location under study is incorporated into the transfer function; whereas the transfer functions derived from the multi-lump model contain the average feedback effects happening all over the reactor core.
ContributorsZhang, Taipeng (Author) / Holbert, Keith E. (Thesis advisor) / Vittal, Vijay (Committee member) / Tylavsky, Daniel (Committee member) / Arizona State University (Publisher)
Created2012
Description
A principal goal of this dissertation is to study stochastic optimization and real-time scheduling in cyber-physical systems (CPSs) ranging from real-time wireless systems to energy systems to distributed control systems. Under this common theme, this dissertation can be broadly organized into three parts based on the system environments. The first part investigates stochastic optimization in real-time wireless systems, with the focus on the deadline-aware scheduling for real-time traffic. The optimal solution to such scheduling problems requires to explicitly taking into account the coupling in the deadline-aware transmissions and stochastic characteristics of the traffic, which involves a dynamic program that is traditionally known to be intractable or computationally expensive to implement. First, real-time scheduling with adaptive network coding over memoryless channels is studied, and a polynomial-time complexity algorithm is developed to characterize the optimal real-time scheduling. Then, real-time scheduling over Markovian channels is investigated, where channel conditions are time-varying and online channel learning is necessary, and the optimal scheduling policies in different traffic regimes are studied. The second part focuses on the stochastic optimization and real-time scheduling involved in energy systems. First, risk-aware scheduling and dispatch for plug-in electric vehicles (EVs) are studied, aiming to jointly optimize the EV charging cost and the risk of the load mismatch between the forecasted and the actual EV loads, due to the random driving activities of EVs. Then, the integration of wind generation at high penetration levels into bulk power grids is considered. Joint optimization of economic dispatch and interruptible load management is investigated using short-term wind farm generation forecast. The third part studies stochastic optimization in distributed control systems under different network environments. First, distributed spectrum access in cognitive radio networks is investigated by using pricing approach, where primary users (PUs) sell the temporarily unused spectrum and secondary users compete via random access for such spectrum opportunities. The optimal pricing strategy for PUs and the corresponding distributed implementation of spectrum access control are developed to maximize the PU's revenue. Then, a systematic study of the nonconvex utility-based power control problem is presented under the physical interference model in ad-hoc networks. Distributed power control schemes are devised to maximize the system utility, by leveraging the extended duality theory and simulated annealing.
ContributorsYang, Lei (Author) / Zhang, Junshan (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Xue, Guoliang (Committee member) / Ying, Lei (Committee member) / Arizona State University (Publisher)
Created2012
Description
Wireless technologies for health monitoring systems have seen considerable interest in recent years owing to it's potential to achieve vision of pervasive healthcare, that is healthcare to anyone, anywhere and anytime. Development of wearable wireless medical devices which have the capability to sense, compute, and send physiological information to a mobile gateway, forming a Body Sensor Network (BSN) is considered as a step towards achieving the vision of pervasive health monitoring systems (PHMS). PHMS consisting of wearable body sensors encourages unsupervised long-term monitoring, reducing frequent visit to hospital and nursing cost. Therefore, it is of utmost importance that operation of PHMS must be reliable, safe and have longer lifetime. A model-based automatic code generation provides a state-of-art code generation of sensor and smart phone code from high-level specification of a PHMS. Code generator intakes meta-model of PHMS specification, uses codebase containing code templates and algorithms, and generates platform specific code. Health-Dev, a framework for model-based development of PHMS, uses code generation to implement PHMS in sensor and smart phone. As a part of this thesis, model-based automatic code generation was evaluated and experimentally validated. The generated code was found to be safe in terms of ensuring no race condition, array, or pointer related errors in the generated code and more optimized as compared to hand-written BSN benchmark code in terms of lesser unreachable code.
ContributorsVerma, Sunit (Author) / Gupta, Sandeep (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Reisslein, Martin (Committee member) / Arizona State University (Publisher)
Created2013
Description
Under the framework of intelligent management of power grids by leveraging advanced information, communication and control technologies, a primary objective of this study is to develop novel data mining and data processing schemes for several critical applications that can enhance the reliability of power systems. Specifically, this study is broadly organized into the following two parts: I) spatio-temporal wind power analysis for wind generation forecast and integration, and II) data mining and information fusion of synchrophasor measurements toward secure power grids. Part I is centered around wind power generation forecast and integration. First, a spatio-temporal analysis approach for short-term wind farm generation forecasting is proposed. Specifically, using extensive measurement data from an actual wind farm, the probability distribution and the level crossing rate of wind farm generation are characterized using tools from graphical learning and time-series analysis. Built on these spatial and temporal characterizations, finite state Markov chain models are developed, and a point forecast of wind farm generation is derived using the Markov chains. Then, multi-timescale scheduling and dispatch with stochastic wind generation and opportunistic demand response is investigated. Part II focuses on incorporating the emerging synchrophasor technology into the security assessment and the post-disturbance fault diagnosis of power systems. First, a data-mining framework is developed for on-line dynamic security assessment by using adaptive ensemble decision tree learning of real-time synchrophasor measurements. Under this framework, novel on-line dynamic security assessment schemes are devised, aiming to handle various factors (including variations of operating conditions, forced system topology change, and loss of critical synchrophasor measurements) that can have significant impact on the performance of conventional data-mining based on-line DSA schemes. Then, in the context of post-disturbance analysis, fault detection and localization of line outage is investigated using a dependency graph approach. It is shown that a dependency graph for voltage phase angles can be built according to the interconnection structure of power system, and line outage events can be detected and localized through networked data fusion of the synchrophasor measurements collected from multiple locations of power grids. Along a more practical avenue, a decentralized networked data fusion scheme is proposed for efficient fault detection and localization.
ContributorsHe, Miao (Author) / Zhang, Junshan (Thesis advisor) / Vittal, Vijay (Thesis advisor) / Hedman, Kory (Committee member) / Si, Jennie (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
A signal with time-varying frequency content can often be expressed more clearly using a time-frequency representation (TFR), which maps the signal into a two-dimensional function of time and frequency, similar to musical notation. The thesis reviews one of the most commonly used TFRs, the Wigner distribution (WD), and discusses its application in Fourier optics: it is shown that the WD is analogous to the spectral dispersion that results from a diffraction grating, and time and frequency are similarly analogous to a one dimensional spatial coordinate and wavenumber. The grating is compared with a simple polychromator, which is a bank of optical filters. Another well-known TFR is the short time Fourier transform (STFT). Its discrete version can be shown to be equivalent to a filter bank, an array of bandpass filters that enable localized processing of the analysis signals in different sub-bands. This work proposes a signal-adaptive method of generating TFRs. In order to minimize distortion in analyzing a signal, the method modifies the filter bank to consist of non-overlapping rectangular bandpass filters generated using the Butterworth filter design process. The information contained in the resulting TFR can be used to reconstruct the signal, and perfect reconstruction techniques involving quadrature mirror filter banks are compared with a simple Fourier synthesis sum. The optimal filter parameters of the rectangular filters are selected adaptively by minimizing the mean-squared error (MSE) from a pseudo-reconstructed version of the analysis signal. The reconstruction MSE is proposed as an error metric for characterizing TFRs; a practical measure of the error requires normalization and cross correlation with the analysis signal. Simulations were performed to demonstrate the the effectiveness of the new adaptive TFR and its relation to swept-tuned spectrum analyzers.
ContributorsWeber, Peter C. (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Kovvali, Narayan (Committee member) / Arizona State University (Publisher)
Created2012
Description
At present, almost 70% of the electric energy in the United States is produced utilizing fossil fuels. Combustion of fossil fuels contributes CO2 to the atmosphere, potentially exacerbating the impact on global warming. To make the electric power system (EPS) more sustainable for the future, there has been an emphasis on scaling up generation of electric energy from wind and solar resources. These resources are renewable in nature and have pollution free operation. Various states in the US have set up different goals for achieving certain amount of electrical energy to be produced from renewable resources. The Southwestern region of the United States receives significant solar radiation throughout the year. High solar radiation makes concentrated solar power and solar PV the most suitable means of renewable energy production in this region. However, the majority of the projects that are presently being developed are either residential or utility owned solar PV plants. This research explores the impact of significant PV penetration on the steady state voltage profile of the electric power transmission system. This study also identifies the impact of PV penetration on the dynamic response of the transmission system such as rotor angle stability, frequency response and voltage response after a contingency. The light load case of spring 2010 and the peak load case of summer 2018 have been considered for analyzing the impact of PV. If the impact is found to be detrimental to the normal operation of the EPS, mitigation measures have been devised and presented in the thesis. Commercially available software tools/packages such as PSLF, PSS/E, DSA Tools have been used to analyze the power network and validate the results.
ContributorsPrakash, Nitin (Author) / Heydt, Gerald T. (Thesis advisor) / Vittal, Vijay (Thesis advisor) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2013