Low computational complexity model reduction of power systems with preservation of physical characteristics

A data-driven algorithm recently proposed to solve the problem of model reduction by moment matching is extended to multi-input, multi-output systems. The algorithm is exploited for the model reduction of large-scale interconnected power systems and it offers, simultaneously, a low computational complexity approximation of the moments and the possibility to easily enforce constraints on the reduced order model. This advantage is used to preserve selected slow and poorly damped modes. The preservation of these modes has been shown to be important from a physical point of view and in obtaining an overall good approximation. The problem of the choice of the socalled tangential directions is also analyzed. The algorithm and the resulting reduced order model are validated with the study of the dynamic response of the NETS-NYPS benchmark system (68-Bus, 16-Machine, 5-Area) to multiple fault scenarios

Frequency and voltage partitioning in presence of renewable energy resources for power system (example: North Chile power network)

This paper investigates techniques for frequency and voltage partitioning of power network based on the graph-theory. These methods divide the power system into distinguished regions to avoid the spread of disturbances and to minimize the interaction between these regions for frequency and voltage control of power system. In case of required active and reactive power for improving the performance of the power system, control can be performed regionally instead of a centralized controller. In this paper, renewable energy sources are connected to the power network to verify the effect of these sources on the power systems partitioning and performance. The number of regions is found based on the frequency sensitivity for frequency partitioning and bus voltage for voltage partitioning to disturbances being applied to loads in each region. The methodology is applied to the north part of Chile power network. The results show the performance and ability of graph frequency and voltage partitioning algorithm to divide large scale power systems to smaller regions for applying decentralized controllers.Peer ReviewedPostprint (published version

International comovement of stock market returns: a wavelet analysis

The assessment of the comovement among international stock markets is of key interest, for example, for the international portfolio diversification literature. In this paper, we re-examine such comovement by resorting to a novel approach, wavelet analysis. Wavelet analysis allows one to measure the comovement in the time-frequency space. In this way, one can characterize how international stock returns relate in the time and frequency domains simultaneously, which allows one to provide a richer analysis of the comovement. We focus on Germany, Japan, UK and US and the analysis is done at both the aggregate and sectoral levels.

Going Deeper into Action Recognition: A Survey

Understanding human actions in visual data is tied to advances in complementary research areas including object recognition, human dynamics, domain adaptation and semantic segmentation. Over the last decade, human action analysis evolved from earlier schemes that are often limited to controlled environments to nowadays advanced solutions that can learn from millions of videos and apply to almost all daily activities. Given the broad range of applications from video surveillance to human-computer interaction, scientific milestones in action recognition are achieved more rapidly, eventually leading to the demise of what used to be good in a short time. This motivated us to provide a comprehensive review of the notable steps taken towards recognizing human actions. To this end, we start our discussion with the pioneering methods that use handcrafted representations, and then, navigate into the realm of deep learning based approaches. We aim to remain objective throughout this survey, touching upon encouraging improvements as well as inevitable fallbacks, in the hope of raising fresh questions and motivating new research directions for the reader

Interplay of phase boundary anisotropy and electro-autocatalytic surface reactions on the lithium intercalation dynamics in LiX_XFePO4_4 platelet-like nanoparticles

Experiments on single crystal Li$_X$FePO$_4$ (LFP) nanoparticles indicate rich nonequilibrium phase behavior, such as suppression of phase separation at high lithiation rates, striped patterns of coherent phase boundaries, nucleation by binarysolid surface wetting and intercalation waves. These observations have been successfully predicted (prior to the experiments) by 1D depth-averaged phase-field models, which neglect any subsurface phase separation. In this paper, using an electro-chemo-mechanical phase-field model, we investigate the coherent non-equilibrium subsurface phase morphologies that develop in the $ab$- plane of platelet-like single-crystal platelet-like Li$_X$FePO$_4$ nanoparticles. Finite element simulations are performed for 2D plane-stress conditions in the $ab$- plane, and validated by 3D simulations, showing similar results. We show that the anisotropy of the interfacial tension tensor, coupled with electroautocatalytic surface intercalation reactions, plays a crucial role in determining the subsurface phase morphology. With isotropic interfacial tension, subsurface phase separation is observed, independent of the reaction kinetics, but for strong anisotropy, phase separation is controlled by surface reactions, as assumed in 1D models. Moreover, the driven intercalation reaction suppresses phase separation during lithiation, while enhancing it during delithiation, by electro-autocatalysis, in quantitative agreement with {\it in operando} imaging experiments in single-crystalline nanoparticles, given measured reaction rate constants