1,721,070 research outputs found
Data-driven Identification and Prediction of Power System Dynamics Using Linear Operators
Full text linkIn this paper, we propose linear operator theoretic framework involving Koopman operator for the data-driven identification of power system dynamics. We explicitly account for noise in the time series measurement data and propose robust approach for data-driven approximation of Koopman operator for the identification of nonlinear power system dynamics. The identified model is used for the prediction of state trajectories in the power system. The application of the framework is illustrated using an IEEE nine bus test system.This is a pre-print of the article Sharma, Pranav, Bowen Huang, Umesh Vaidya, and Venkatramana Ajjarapu. "Data-driven Identification and Prediction of Power System Dynamics Using Linear Operators." arXiv preprint arXiv:1903.06828 (2019). Posted with permission.</p
Data-Driven Nonlinear Stabilization Using Koopman Operator
Full text linkWe propose the application of Koopman operator theory for the design of stabilizing feedback controller for a nonlinear control system. The proposed approach is data-driven and relies on the use of time-series data generated from the control dynamical system for the lifting of a nonlinear system in the Koopman eigenfunction coordinates. In particular, a finite-dimensional bilinear representation of a control-affine nonlinear dynamical system is constructed in the Koopman eigenfunction coordinates using time-series data. Sample complexity results are used to determine the data required to achieve the desired level of accuracy for the approximate bilinear representation of the nonlinear system in Koopman eigenfunction coordinates. A control Lyapunov function-based approach is proposed for the design of stabilizing feedback controller, and the principle of inverse optimality is used to comment on the optimality of the designed stabilizing feedback controller for the bilinear system. A systematic convex optimization-based formulation is proposed for the search of control Lyapunov function. Several numerical examples are presented to demonstrate the application of the proposed data-driven stabilization approach.This is a pre-print of the article Huang, Bowen, Xu Ma, and Umesh Vaidya. "Data-Driven Nonlinear Stabilization Using Koopman Operator." arXiv preprint arXiv:1901.07678 (2019). Posted with permission.</p
Transfer Operator Theoretic Framework for Monitoring Building Indoor Environment in Uncertain Operating Conditions
Full text linkDynamical system-based linear transfer Perron- Frobenius (P-F) operator framework is developed to address analysis and design problems in the building system. In particular, the problems of fast contaminant propagation and optimal placement of sensors in uncertain operating conditions of indoor building environment are addressed. The linear nature of transfer P-F operator is exploited to develop a computationally efficient numerical scheme based on the finite dimensional approximation of P-F operator for fast propagation of contaminants. The proposed scheme is an order of magnitude faster than existing methods that rely on simulation of an advection-diffusion partial differential equation for contaminant transport. Furthermore, the system-theoretic notion of observability gramian is generalized to nonlinear flow fields using the transfer P-F operator. This developed notion of observability gramian for nonlinear flow field combined with the finite dimensional approximation of P-F operator is used to provide a systematic procedure for optimal placement of sensors under uncertain operating conditions. Simulation results are presented to demonstrate the applicability of the developed framework on the IEA-annex 2D benchmark problem.This is a preprint of the article Sharma, Himanshu, Anthony D. Fontanini, Umesh Vaidya, and Baskar Ganapathysubramanian. "Transfer Operator Theoretic Framework for Monitoring Building Indoor Environment in Uncertain Operating Conditions." arXiv preprint arXiv:1807.04781 (2018). Posted with permission.</p
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Theoretical foundations for finite-time transient stability and sensitivity analysis of power systems
Full text linkTransient stability and sensitivity analysis of power systems are problems of enormous academic and practical interest. These classical problems have received renewed interest, because of the advancement in sensor technology in the form of phasor measurement units (PMUs). The advancement in sensor technology has provided unique opportunity for the development of real-time stability monitoring and sensitivity analysis tools. Transient stability problem in power system is inherently a problem of stability analysis of the non-equilibrium dynamics, because for a short time period following a fault or disturbance the system trajectory moves away from the equilibrium point. The real-time stability decision has to be made over this short time period. However, the existing stability definitions and hence analysis tools for transient stability are asymptotic in nature. In this thesis, we discover theoretical foundations for the short-term transient stability analysis of power systems, based on the theory of normally hyperbolic invariant manifolds and finite time Lyapunov exponents, adopted from geometric theory of dynamical systems. The theory of normally hyperbolic surfaces allows us to characterize the rate of expansion and contraction of co-dimension one material surfaces in the phase space. The expansion and contraction rates of these material surfaces can be computed in finite time. We prove that the expansion and contraction rates can be used as finite time transient stability certificates. Furthermore, material surfaces with maximum expansion and contraction rate are identified with the stability boundaries. These stability boundaries are used for computation of stability margin. We have used the theoretical framework for the development of model-based and model-free real-time stability monitoring methods. Both the model-based and model-free approaches rely on the availability of high resolution time series data from the PMUs for stability prediction. The problem of sensitivity analysis of power system, subjected to changes or uncertainty in load parameters and network topology, is also studied using the theory of normally hyperbolic manifolds. The sensitivity analysis is used for the identification and rank ordering of the critical interactions and parameters in the power network. The sensitivity analysis is carried out both in finite time and in asymptotic. One of the distinguishing features of the asymptotic sensitivity analysis is that the asymptotic dynamics of the system is assumed to be a periodic orbit. For asymptotic sensitivity analysis we employ combination of tools from ergodic theory and geometric theory of dynamical systems.</p
Spectral diffusion map approach for structural health monitoring of wind turbine blades using distributed sensors
Full text linkDistributed Sensor Networks (DSN) has emerged as a sensing paradigms in the structural engineering field due to the application of Structural Health Monitoring (SHM) for large-scale structures which results in accurately diagnosing the health of structures and enhancing the reliability and robustness of monitoring systems. The multisensor network greatly enhances the feasibility of applying SHM and also provides awareness of structural damage. In this work, we develop data-driven method for the diagnosis of damage in mechanical structures using an array of distributed sensors. The proposed approach relies on comparing intrinsic geometry of data sets corresponding to the undamage and damage state of the system. This approach assumes no knowledge of underlying models of the different data sources. We use spectral diffusion map approach for identifying the intrinsic geometry of the data set. In particular, time series data from distributed sensors is used for the construction of diffusion map. The low dimensional embedding of the data set corresponding to different damage level is done using singular value decomposition of the diffusion map to identify the intrinsic geometry. We construct appropriate metric in diffusion space to compare the low-dimensional data set corresponding to different damage cases. The developed algorithm is applied for damage diagnosis of wind turbine blades. Towards this goal we developed a detailed nite element-based model of CX-100 blade in ANSYS using shell elements. The damage in the blade is modeled by degrading the material property which in turn results in change of stiffness. One of the main challenges in the development of health monitoring algorithms is the ability to use sensor data with relatively small signal to noise ratio. Our developed diffusion map-based algorithm is shown to be robust to the presence of sensor noise. The proposed diffusion map-based algorithm can not only account for data from different sensors but also different types of sensor in the form of sensor fusion hereby making it attractive to exploit the distributed nature of sensor array. The distributed nature of sensor array is further exploited to determine the location of damage on the wind turbine blade. Our extensive simulation results show that our proposed algorithms can not only determine the extend of damage but also the location of the damage.</p
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dynamic modeling and analysis of worm data via linear operator
Full text linkIn this thesis, we demonstrate the application of linear operator theory for data-driven dynamic modeling and analysis of worm data. In particular, Koopman and Perron-Frobenius operators are used for the dynamic modeling of two different worms namely Brugia Malayi and C elegans. Time-series data in the form of video is used to generate reduced order dynamics model to capture the moment of these two worms under different operating conditions. While the moment of the worm is in general modeled as a nonlinear dynamical system, our proposed linear operator theoretic framework provides for a linear representation of the nonlinear dynamics. The linear representation is made possible by shifting the focus from the state space to the space of functions. We exploit this linear representation for data-driven modeling of worm dynamics. For data-driven dynamic modeling, we construct a finite dimensional approximation of these linear operators. Two popular algorithms, Dynamic Mode Decomposition (DMD) and Extended Dynamic Mode Decomposition (EDMD) are used for the finite dimensional approximation of the linear Koopman operator from time series data. The data-driven model is used for prediction of worm dynamics and the comparison of worm movement under different operating conditions caused by the exposure of worm to different drug cocktails. The developed dynamic model will be used to understand the impact of different drug cocktails on worm moment thereby providing a systematic data-driven approach for drug discovery.</p
- …
