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METHOD:PUBLISH
X-WR-CALNAME;VALUE=TEXT:[MCRN] Group Calendar: Joint Data Assimilation Seminar with ICTS-TIFR
X-PUBLISHED-TTL:PT15M
X-ORIGINAL-URL:https://mcrn.hubzero.org/groups/jdas/calendar/subscribe/0,11,12.ics
CALSCALE:GREGORIAN
BEGIN:VEVENT
UID:2@mcrn.hubzero.org
SEQUENCE:0
DTSTAMP:20191212T144314Z
DTSTART:20140225T130000Z
DTEND:20140225T150000Z
CREATED:20150217T005425Z
LAST-MODIFIED:20150217T005425Z
SUMMARY:Naratip Santitissadeekorn: Joint parameter-state estimation using a two-stage filter
DESCRIPTION:
END:VEVENT
BEGIN:VEVENT
UID:3@mcrn.hubzero.org
SEQUENCE:0
DTSTAMP:20191212T144314Z
DTSTART:20140304T130000Z
DTEND:20140304T150000Z
CREATED:20150217T005541Z
LAST-MODIFIED:20150217T005541Z
SUMMARY:Naratip Santitissadeekorn: Joint parameter-state estimation using a two-stage filter Part 2
DESCRIPTION:
END:VEVENT
BEGIN:VEVENT
UID:4@mcrn.hubzero.org
SEQUENCE:0
DTSTAMP:20191212T144314Z
DTSTART:20140318T140000Z
DTEND:20140318T160000Z
CREATED:20150217T005737Z
LAST-MODIFIED:20150217T005737Z
SUMMARY:Karthik Gurumoorthy: The Mathematical Background of AUS
DESCRIPTION:Karthik Gurumoorthy describes the mathematical background behind the Assimilation in Unstable Subspaces (AUS) approach in data assimilation which relies on efficiently computing the Liapunov vectors. He will cover what is meant by Liapunov vectors and the standard procedure behind computing them.
END:VEVENT
BEGIN:VEVENT
UID:7@mcrn.hubzero.org
SEQUENCE:0
DTSTAMP:20191212T144314Z
DTSTART:20140429T140000Z
DTEND:20140429T160000Z
CREATED:20150217T010559Z
LAST-MODIFIED:20150217T010803Z
SUMMARY:Xin Tong: Filtering with Noisy Lagrangian Tracers
DESCRIPTION:An important practical problem is the recovery of a turbulent velocity field from Lagrangian tracers that move with the fluid flow. Despite the inherent nonlinearity in measuring noisy Lagrangian tracers, it is shown that there are exact closed analytic formulas for the optimal filter. When the underlying velocity field is incompressible, the tracers’ distribution converge to the uniform distribution geometrically fast; concrete asymptotic features, such as information barriers, are obtained for the optimal filter when the number of tracers goes to infinity. On the hand, the filtering of a compressible flow that consists of both geostrophically balanced (GB) modes and gravity waves is also considered. Its performance can be closely approximated by the filter performance of an idealized GB truncation of this model when the Rossby number is small, i.e. the rotation is fast. Such phenomenon is caused by fast-wave averaging and inspires a simplified filtering scheme.
END:VEVENT
BEGIN:VEVENT
UID:8@mcrn.hubzero.org
SEQUENCE:0
DTSTAMP:20191212T144314Z
DTSTART:20140415T140000Z
DTEND:20140415T160000Z
CREATED:20150217T010732Z
LAST-MODIFIED:20150217T010732Z
SUMMARY:Laura Slivinski: A hybrid particle-ensemble Kalman filter for Lagrangian data assimilation
DESCRIPTION:Lagrangian data assimilation involves using observations of the positions of passive drifters in a flow in order to obtain a probability distribution on the underlying Eulerian flow field. Several data assimilation schemes have been studied in the context of geophysical fluid flows (such as the particle filter and the ensemble Kalman filter), but these methods often have disadvantages. I will give an overview of Lagrangian data assimilation and present results from a new hybrid filter scheme applied to the shallow water equations.
END:VEVENT
BEGIN:VEVENT
UID:9@mcrn.hubzero.org
SEQUENCE:0
DTSTAMP:20191212T144314Z
DTSTART:20140506T140000Z
DTEND:20140506T160000Z
CREATED:20150217T010934Z
LAST-MODIFIED:20150217T010934Z
SUMMARY: Erik Van Vleck: Computational Techniques for Lyapunov Exponents and Vectors
DESCRIPTION:In this talk we present computational techniques for Lyapunov exponents and vectors based upon continuous matrix factorizations (QR and SVD). We outline the techniques, their well-posedness, error analysis/perturbation theory, and describe codes we have developed. We then discuss application of these techniques to decoupling/dimension reduction of dissipative differential equations and to assimilation in the unstable subspace.
END:VEVENT
BEGIN:VEVENT
UID:10@mcrn.hubzero.org
SEQUENCE:0
DTSTAMP:20191212T144314Z
DTSTART:20140513T140000Z
DTEND:20140513T160000Z
CREATED:20150217T011205Z
LAST-MODIFIED:20150217T011205Z
SUMMARY:General discussion on connections between DA and Lyapunov stability and short summaries of volunteered readings
DESCRIPTION:
END:VEVENT
BEGIN:VEVENT
UID:11@mcrn.hubzero.org
SEQUENCE:0
DTSTAMP:20191212T144314Z
DTSTART:20140716T140000Z
DTEND:20140716T160000Z
CREATED:20150217T011328Z
LAST-MODIFIED:20150217T011328Z
SUMMARY:Karthik Gurumoorthy: A Review of Particle Filter Methods
DESCRIPTION:
END:VEVENT
BEGIN:VEVENT
UID:12@mcrn.hubzero.org
SEQUENCE:0
DTSTAMP:20191212T144314Z
DTSTART:20140806T150000Z
DTEND:20140806T160000Z
CREATED:20150217T011649Z
LAST-MODIFIED:20150217T011649Z
SUMMARY:Colin Grudzien: A Survey of Particle Filter Convergence Results Part 1
DESCRIPTION:
END:VEVENT
BEGIN:VEVENT
UID:13@mcrn.hubzero.org
SEQUENCE:0
DTSTAMP:20191212T144314Z
DTSTART:20140813T150000Z
DTEND:20140813T160000Z
CREATED:20150217T011810Z
LAST-MODIFIED:20150217T011810Z
SUMMARY:Colin Grudzien: A Survey of Particle Filter Convergence Results Part 2
DESCRIPTION:
END:VEVENT
BEGIN:VEVENT
UID:14@mcrn.hubzero.org
SEQUENCE:0
DTSTAMP:20191212T144314Z
DTSTART:20140902T150000Z
DTEND:20140902T160000Z
CREATED:20150217T011951Z
LAST-MODIFIED:20150217T011951Z
SUMMARY:Colin Grudzien: A Survey of Data Assimilation Applications in Phytoplankton Models
DESCRIPTION:
END:VEVENT
BEGIN:VEVENT
UID:15@mcrn.hubzero.org
SEQUENCE:0
DTSTAMP:20191212T144314Z
DTSTART:20141002T150000Z
DTEND:20141002T160000Z
CREATED:20150217T012149Z
LAST-MODIFIED:20150217T012149Z
SUMMARY:Naratip Santitissadeekorn: An Introduction to Variational Data Assimilation
DESCRIPTION:
END:VEVENT
BEGIN:VEVENT
UID:16@mcrn.hubzero.org
SEQUENCE:0
DTSTAMP:20191212T144314Z
DTSTART:20141009T150000Z
DTEND:20141009T160000Z
CREATED:20150217T012335Z
LAST-MODIFIED:20150217T012335Z
SUMMARY:Andy Reagan: Masters Thesis
DESCRIPTION:Andy Reagan at UVM will be giving a talk about his masters work on uncertainty quantification and data assimilation to improve the numerical prediction of fluids in the thermosyphon. This talk will involve several data assimilation methods, including variational techniques, but will focus on LETKF.
END:VEVENT
BEGIN:VEVENT
UID:17@mcrn.hubzero.org
SEQUENCE:0
DTSTAMP:20191212T144314Z
DTSTART:20141023T150000Z
DTEND:20141023T160000Z
CREATED:20150217T012532Z
LAST-MODIFIED:20150217T012532Z
SUMMARY:Colin Grudzien: Practical Session on the adjoint method for computing the cost function gradient
DESCRIPTION:
END:VEVENT
BEGIN:VEVENT
UID:18@mcrn.hubzero.org
SEQUENCE:0
DTSTAMP:20191212T144314Z
DTSTART:20141030T150000Z
DTEND:20141030T160000Z
CREATED:20150217T012727Z
LAST-MODIFIED:20150217T012727Z
SUMMARY:Laura Slivinski: An Introduction to Lagrangian Data Assimilation Part 1
DESCRIPTION:
END:VEVENT
BEGIN:VEVENT
UID:19@mcrn.hubzero.org
SEQUENCE:0
DTSTAMP:20191212T144314Z
DTSTART:20141113T160000Z
DTEND:20141113T170000Z
CREATED:20150217T012810Z
LAST-MODIFIED:20150217T012810Z
SUMMARY:Laura Slivinski: An Introduction to Lagrangian Data Assimilation Part 2
DESCRIPTION:
END:VEVENT
BEGIN:VEVENT
UID:20@mcrn.hubzero.org
SEQUENCE:0
DTSTAMP:20191212T144314Z
DTSTART:20150129T153000Z
DTEND:20150129T163000Z
CREATED:20150217T012949Z
LAST-MODIFIED:20150217T012949Z
SUMMARY:Trevor Gionet: An Introduction to LETKF
DESCRIPTION:
END:VEVENT
BEGIN:VEVENT
UID:21@mcrn.hubzero.org
SEQUENCE:0
DTSTAMP:20191212T144314Z
DTSTART:20150213T153000Z
DTEND:20150213T170000Z
CREATED:20150217T013153Z
LAST-MODIFIED:20150217T013153Z
SUMMARY:Matthew Smith: Environmental past, present and futures… fast!
DESCRIPTION:Eight years ago my lab set out to make, enable and accelerate transformative advances in science in areas of societal importance. We’ve had some impact with our science but what has also been satisfying is seeing the impact we can have with tools that make working with ecological and environmental data really easy. I’ll talk about Fetchclimate http://fetchclimate2.cloudapp.net/ a fast, free, intelligent climate information service that operates over the cloud to return exactly the information you need, explaining why we built it, what it does, how you can use it, and where it’s going. I’ll explain the scientific needs we and colleagues were experiencing that Fethclimate helped fulfill – like helping us build the first fully data-constrained model of the terrestrial carbon cycle (EVERY parameter is inferred as a probability distribution using Bayesian inference). I’ll explain our plans to enable data-constrained modelling at the speed of thought – so that a broader community of scientists can contribute to improving biological forecast models (thing predicting disease spread, like Ebola, or the dynamics of fisheries). For a sneak peek of one of our prototypes see: http://youtu.be/uyJxzO2SLwQ. Also see here for some of our first tranche of tools made to speed up the process of scientific discovery: http://research.microsoft.com/science/tools. Looking forward to discussions.
END:VEVENT
BEGIN:VEVENT
UID:22@mcrn.hubzero.org
SEQUENCE:0
DTSTAMP:20191212T144314Z
DTSTART:20150219T153000Z
DTEND:20150219T170000Z
CREATED:20150217T013309Z
LAST-MODIFIED:20150217T013309Z
SUMMARY:Patrick Raanes: Extending the square root method to account for model noise in the ensemble Kalman filter
DESCRIPTION:A novel approach to account for model noise in the forecast step of the Ensemble Kalman filter (EnKF) is proposed.\nThe core method is based on the approach of the analysis step of ensemble square root filters (ETKF),\nand consists in right-multiplying the ensemble matrix during the forecast step by a particular ``transform matrix''.\nTheoretical advantages include respecting linear constraints,\nminimising ensemble disarrangement, preserving covariance structures, and modularity.\nA fundamental problem due to the size of the ensemble subspace is discussed,\nand possible solutions that complement the core method are suggested and studied.\nBenchmarks from twin experiments with simple, low order, nonlinear dynamics\nindicate improved performance over standard approaches to dealing with model noise\nsuch as additive, simulated noise and multiplicative inflation.
END:VEVENT
BEGIN:VEVENT
UID:23@mcrn.hubzero.org
SEQUENCE:0
DTSTAMP:20191212T144314Z
DTSTART:20150226T153000Z
DTEND:20150226T170000Z
CREATED:20150217T014308Z
LAST-MODIFIED:20150323T175912Z
SUMMARY:Juan Durazo - Ionospheric weather forecasting using the LETKF
DESCRIPTION:We track the three-dimensional global distribution of electron density in the ionosphere using the\nLocal Ensemble Kalman Filter (LETKF) and the Thermosphere-Ionosphere-Electrodynamics Global\nCirculation Model (TIEGCM) by assimilating globally distributed electron density profiles. The\nTIEGCM is a three-dimensional non-linear representation of the coupled ionosphere-thermosphere\nsystem on a global grid with resolution of 5◦ × 5\nfrom 97 km to 600 km in elevation depending on solar activity levels. Observations are obtained from\nthe COSMIC satellite mission and are assimilated into the ensemble forecast at every hour, using\nobservations available within 30 minutes of analysis time. The ionosphere-thermosphere system\nis strongly driven by external solar and magetospheric forcing, which is represented in TIEGCM\nthrough parametrized input that is specified through auxiliary empirical models. The one way\ncoupling between the parametrized input and the I-T system poses some challenges for the LETKF\nthat include estimation of the parameterized forcing and ensemble inflation of the parameters to\nadequately represent appropriate ensemble forecast error distribution. Results are validated with\nother electron density profiles derived from other satellite missions (CHAMP and GRACE) and\nmeasurements of peak electron density and peak elevation in the F2-layer as given by ionosonde\nstations all over the world.
END:VEVENT
BEGIN:VEVENT
UID:1364@mcrn.hubzero.org
SEQUENCE:0
DTSTAMP:20191212T144314Z
DTSTART:20150904T080000Z
DTEND:20150905T040000Z
CREATED:20150916T210834Z
LAST-MODIFIED:20150916T210834Z
SUMMARY:Test Event
DESCRIPTION:
END:VEVENT
BEGIN:VEVENT
UID:2355@mcrn.hubzero.org
SEQUENCE:0
DTSTAMP:20191212T144314Z
DTSTART:20151209T150000Z
DTEND:20151210T160000Z
CREATED:20151203T174643Z
LAST-MODIFIED:20151203T174643Z
SUMMARY:JDAS Seminar
DESCRIPTION:Title: Indistinguishable states I: perfect model scenario\n\nAbstract:\nIn this presentation I will discuss the paper by Kevin Judd and Leonard Smith on Indistinguishable states for perfect models (Physica D 151, 125 (2001)). The aim of this paper is to make an accurate forecast of states for nonlinear systems. It has been shown that even in the presence of infinite past observations, uncertainty in the observations make (true) state estimation impossible even for deterministic model. There will be states those are indistinguishable from the true state. In this scenario, (may be) the only way of making any forecast of the (true) state is by measuring the probability density of indistinguishability. I will discuss a formal derivation of the probability density of being indistinguishability. Later in this paper the authors discuss a new method of state estimation by using gradient descent method.
END:VEVENT
BEGIN:VEVENT
UID:622@mcrn.hubzero.org
SEQUENCE:0
DTSTAMP:20191212T144314Z
DTSTART:20150416T143000Z
DTEND:20150416T153000Z
CREATED:20150509T003944Z
LAST-MODIFIED:20191126T150213Z
SUMMARY:Javier Amezcua - Particle filters for geophysical applications
DESCRIPTION:Abstract: Avoiding degeneracy is a crucial challenge for particle filters. Results have shown that the number of particles scales exponentially with respect to the number of independent observations. In this talk I will review attempts to counteract this phenomenon by exploiting proposal densities. I will put special emphasis to the Equivalent Weight Particle Filter (van Leeuwen, 2010), the Implicit Particle Filter (Chorin and Tu,2010), and culminate with the merging of ideas from these two into the Targeted Implicit Particle filter of (Amezcua and van Leeuwen, 2015).
END:VEVENT
BEGIN:VEVENT
UID:623@mcrn.hubzero.org
SEQUENCE:0
DTSTAMP:20191212T144314Z
DTSTART:20150320T143000Z
DTEND:20150320T153000Z
CREATED:20150509T003944Z
LAST-MODIFIED:20191126T150213Z
SUMMARY:Peter Challenor - Emulators in climate science. Uncertainty, sensitivity, calibration and more.
DESCRIPTION:An emulator or a surrogate is a statistical approximation of a complex numerical model. Emulators are fast to run and include a measure of their own uncertainty. This makes them suitable for a number of applications in climate science. Emulators were originally devised for uncertainty quantification but their application goes far beyond simply estimated the errors on model outputs. I will explain what an emulator is, how it can be calculated and some applications in model prediction, sensitivity analysis and quantifying uncertainty. Current research topics will also be discussed.
END:VEVENT
BEGIN:VEVENT
UID:624@mcrn.hubzero.org
SEQUENCE:0
DTSTAMP:20191212T144314Z
DTSTART:20150423T143000Z
DTEND:20150423T153000Z
CREATED:20150509T003944Z
LAST-MODIFIED:20191126T150213Z
SUMMARY:Talea Mayo - STATISTICAL DATA ASSIMILATION FOR PARAMETER ESTIMATION IN COASTAL OCEAN HYDRODYNAMICS MODELING
DESCRIPTION:Abstract: Coastal ocean models are used for a variety of applications, including modeling tides and hurricane storm surge. These models numerically solve the shallow water equations, which are derived by depth integrating the Navier-Stokes equations. The inherent uncertainties in coastal ocean models are a result of many factors, including unknown model parameters. Parameters of particular importance are those used to define the bottom friction of the physical domain. In this work we will estimate bottom friction coefficients using statistical data assimilation methods. Statistical data assimilation methods, such as the ensemble Kalman filter, are generally used for state estimation. However, the implementation of statistical data assimilation methods for parameter estimation is straightforward. The evolution of the model parameters is considered to be a stationary process; model parameters are “forecasted” by adding a small amount of random noise to the initial estimates. The forecasted parameters are then updated by data; they are first projected into an observation space using the numerical model, and then the residual between the forecasted model parameters and the observed data is weighted and used to update the original estimate. Statistical data assimilation for parameter estimation is a promising method for reducing the uncertainty in coastal ocean models.
END:VEVENT
BEGIN:VEVENT
UID:625@mcrn.hubzero.org
SEQUENCE:0
DTSTAMP:20191212T144314Z
DTSTART:20150326T143000Z
DTEND:20150326T153000Z
CREATED:20150509T003944Z
LAST-MODIFIED:20191126T150213Z
SUMMARY:Elaine Spiller - Toward a hybrid particle-ensemble Kalman filter for assimilating data from Lagrangian instruments into high dimensional models
DESCRIPTION:We discuss a recently proposed hybrid particle-ensemble Kalman filter for assimilating Lagrangian data, and apply it to a high-dimensional quasi-geostrophic ocean model. Effectively the hybrid filter applies a particle filter to the highly nonlinear, low-dimensional Lagrangian instrument variables while applying an ensemble Kalman type update to the high-dimensional Eulerian flow field. We present some initial results from this hybrid filter and compare those to results from a standard ensemble Kalman filter and an ensemble run without assimilation.
END:VEVENT
BEGIN:VEVENT
UID:626@mcrn.hubzero.org
SEQUENCE:0
DTSTAMP:20191212T144314Z
DTSTART:20150213T153000Z
DTEND:20150213T163000Z
CREATED:20150509T003944Z
LAST-MODIFIED:20191126T150213Z
SUMMARY:Matthew Smith
DESCRIPTION:
END:VEVENT
BEGIN:VEVENT
UID:627@mcrn.hubzero.org
SEQUENCE:0
DTSTAMP:20191212T144314Z
DTSTART:20150226T153000Z
DTEND:20150226T163000Z
CREATED:20150509T003944Z
LAST-MODIFIED:20191126T150213Z
SUMMARY:Juan Durazo
DESCRIPTION:
END:VEVENT
BEGIN:VEVENT
UID:628@mcrn.hubzero.org
SEQUENCE:0
DTSTAMP:20191212T144314Z
DTSTART:20150306T153000Z
DTEND:20150306T163000Z
CREATED:20150509T003944Z
LAST-MODIFIED:20191126T150213Z
SUMMARY:Sarah Dance - Correlated Observation Errors in Data Assimilation
DESCRIPTION:Remote sensing observations often have correlated errors, but the correlations are typically ignored in data assimilation for numerical weather prediction. The assumption of zero correlations is often used with data thinning methods, resulting in a loss of information, and reduction in analysis accuracy. As operational centres move towards higher-resolution forecasting, there is a requirement to retain data providing detail on appropriate scales. Thus an alternative approach to dealing with observation error correlations is needed. A popular diagnostic for estimating observation error correlations makes use of statistical averages of background and analysis innovations. In this talk we will discuss new mathematical results giving a theoretical understanding of the diagnostic, and also give some examples of how we have used the diagnostic with the UK Met Office operational system to diagnose spatial and interchannel error correlations for radar and SEVIRI data.
END:VEVENT
BEGIN:VEVENT
UID:629@mcrn.hubzero.org
SEQUENCE:0
DTSTAMP:20191212T144314Z
DTSTART:20141009T150000Z
DTEND:20141009T163000Z
CREATED:20150509T003944Z
LAST-MODIFIED:20191126T150213Z
SUMMARY:JDAS Meeting 11:00 EST - Andy Reagan Master Thesis
DESCRIPTION:
END:VEVENT
BEGIN:VEVENT
UID:630@mcrn.hubzero.org
SEQUENCE:0
DTSTAMP:20191212T144314Z
DTSTART;VALUE=DATE:20141002
DTEND;VALUE=DATE:20141003
CREATED:20150509T003944Z
LAST-MODIFIED:20191126T150213Z
SUMMARY:Nara - Introduction to Variational Data Assimilation
DESCRIPTION:
END:VEVENT
BEGIN:VEVENT
UID:631@mcrn.hubzero.org
SEQUENCE:0
DTSTAMP:20191212T144314Z
DTSTART:20140925T150000Z
DTEND:20140925T163000Z
RRULE:FREQ=WEEKLY;INTERVAL=1;COUNT=6;BYDAY=TH
CREATED:20150509T003944Z
LAST-MODIFIED:20191126T150213Z
SUMMARY:JDAS Meeting 11:00 EST
DESCRIPTION:
END:VEVENT
BEGIN:VEVENT
UID:632@mcrn.hubzero.org
SEQUENCE:0
DTSTAMP:20191212T144314Z
DTSTART:20150519T053000Z
DTEND:20150519T073000Z
CREATED:20150509T004717Z
LAST-MODIFIED:20191126T150213Z
SUMMARY:MS63 Applications of Ensemble Data Assimilation Methods to Climate Processes
DESCRIPTION:1:30 PM - 3:30 PM
Room: Wasatch A
Data assimilation methods seek to improve estimates from a predictive model by combining them with observed data. Most realistic applications, such as those used in climate modeling, involve an underlying dynamical system which is nonlinear. Many ensemble methods have been designed to handle this nonlinearity, and additionally, often provide an estimate of the uncertainty in the prediction via the ensemble spread. This session will include applications of ensemble data assimilation methods to several aspects of the climate, including oceans, sea ice, and the ionosphere.
Organizer: Laura Slivinski
Woods Hole Oceanographic Institute, USA
1:30-1:55 An Application of Lagrangian Data Assimilation to Katama Bay, Ma
Laura Slivinski, Larry Pratt, and Irina Rypina, Woods Hole Oceanographic Institute, USA
2:00-2:25 Ensemble Inflation by Shadowing Techniques
Thomas Bellsky, University of Maine, USA; Lewis Mitchell, University of Adelaide, Australia
2:30-2:55 Ionospheric Weather Forecasting Using the Letkf Scheme
Juan Durazo, Arizona State University, USA
3:00-3:25 Predicting Flow Reversals in a Cfd Simulated Thermosyphon Using Data Assimilation
Andrew Reagan, University of Vermont, USA
LOCATION:Snowbird, UT 84092, USA
END:VEVENT
BEGIN:VEVENT
UID:633@mcrn.hubzero.org
SEQUENCE:0
DTSTAMP:20191212T144314Z
DTSTART:20150424T143000Z
DTEND:20150424T153000Z
CREATED:20150509T005759Z
LAST-MODIFIED:20191126T150214Z
SUMMARY:Takashi Nishikawa - Realistic modeling and analysis of synchronization dynamics in power-grid networks
DESCRIPTION:An imperative condition for the functioning of a power-grid network is that its power generators remain synchronized. Disturbances can prompt desynchronization, which is a process that has been involved in large power outages. In this talk I will first give a comparative review of three leading models of synchronization in power-grid networks. Each of these models can be
derived from first principles under a common framework and represents a power grid as a complex network of coupled second-order phase oscillators with both forcing and damping terms. Since these models require dynamical parameters that
are unavailable in typical power-grid datasets, I will discuss an approach to estimate these parameters. The models will be used to show that if the network structure is not homogeneous, generators with identical parameters need to be treated as non-identical oscillators in general. For one of the models, which describes the dynamics of coupled generators through a network of effective interactions, I will derive a condition under which the desired synchronous state is stable. This condition gives rise to a methodology to specify parameter assignments that can enhance synchronization of any given network, which I will demonstrate for a selection of both test systems and real power grids. These parameter assignments can be realized through very fast control
loops, and this may help devise new control schemes that offer an additional layer of protection, thus contributing to the development of smart grids that can recover from failures in real time.
END:VEVENT
BEGIN:VEVENT
UID:634@mcrn.hubzero.org
SEQUENCE:0
DTSTAMP:20191212T144314Z
DTSTART:20150327T143000Z
DTEND:20150327T153000Z
CREATED:20150509T005759Z
LAST-MODIFIED:20191126T150214Z
SUMMARY:Suman Archayya - Synchronization in power grid networks
DESCRIPTION:This survey discusses Master Stability Function (MSF) analysis for determining stability of synchronization of coupled oscillators on network. In addition, we briefly discuss the derivation of the Swing equation that determine the dynamics of a node in power grid network, and different models of the power grid networks.
References:
1. Master Stability Functions for Synchronized Coupled Systems, L. M. Pecora and T. L. Carroll, Phys. Rev. Lett. 80, 2109 (1998).http://dx.doi.org/10.1103/PhysRevLett.80.2109
2. Comparative analysis of existing models for power-grid synchronization, T. Nishikawa and A. E. Motter, New J. Phys. 17 015012 (2015). http://dx.doi.org/10.1088/1367-2630/17/1/015012
3. Spontaneous synchrony in power-grid networks, A. E. Motter, S. A. Myers, M. Anghel, and T. Nishikawa, Nature Physics 9,191 (2013). http://www.nature.com/doifinder/10.1038/nphys2535
END:VEVENT
BEGIN:VEVENT
UID:635@mcrn.hubzero.org
SEQUENCE:0
DTSTAMP:20191212T144314Z
DTSTART:20150313T143000Z
DTEND:20150313T153000Z
CREATED:20150509T005759Z
LAST-MODIFIED:20191126T150214Z
SUMMARY:Colin Grudzien - A Survey of Grid Control and Optimization
DESCRIPTION:This is an introductory survey of how control problems arise on different time scales in electric grid transmission problems. The set up and general form of such problems are considered, along with operational considerations.
END:VEVENT
END:VCALENDAR