Performance tradeoffs of dynamically controlled grid-connected inverters in low inertia power systems

Implementing frequency response using grid-connected inverters is one of the popular proposed alternatives to mitigate the dynamic degradation experienced in low inertia power systems. However, such solution faces several challenges as inverters do not intrinsically possess the natural response to power fluctuations that synchronous generators have. Thus, to synthetically generate this response, inverters need to take frequency measurements, which are usually noisy, and subsequently make changes in the output power, which are therefore delayed. This paper explores the system-wide performance tradeoffs that arise when measurement noise, power disturbances, and delayed actions are considered in the design of dynamic controllers for grid-connected inverters. Using a recently proposed dynamic droop (iDroop) control for grid-connected inverters, which is inspired by classical first order lead-lag compensation, we show that the sets of parameters that result in highest noise attenuation, power disturbance mitigation, and delay robustness do not necessarily have a common intersection. In particular, lead compensation is desired in systems where power disturbances are the predominant source of degradation, while lag compensation is a better alternative when the system is dominated by delays or frequency noise. Our analysis further shows that iDroop can outperform the standard droop alternative in both joint noise and disturbance mitigation, and delay robustness

Mining Pure, Strict Epistatic Interactions from High-Dimensional Datasets: Ameliorating the Curse of Dimensionality

Background: The interaction between loci to affect phenotype is called epistasis. It is strict epistasis if no proper subset of the interacting loci exhibits a marginal effect. For many diseases, it is likely that unknown epistatic interactions affect disease susceptibility. A difficulty when mining epistatic interactions from high-dimensional datasets concerns the curse of dimensionality. There are too many combinations of SNPs to perform an exhaustive search. A method that could locate strict epistasis without an exhaustive search can be considered the brass ring of methods for analyzing high-dimensional datasets. Methodology/Findings: A SNP pattern is a Bayesian network representing SNP-disease relationships. The Bayesian score for a SNP pattern is the probability of the data given the pattern, and has been used to learn SNP patterns. We identified a bound for the score of a SNP pattern. The bound provides an upper limit on the Bayesian score of any pattern that could be obtained by expanding a given pattern. We felt that the bound might enable the data to say something about the promise of expanding a 1-SNP pattern even when there are no marginal effects. We tested the bound using simulated datasets and semi-synthetic high-dimensional datasets obtained from GWAS datasets. We found that the bound was able to dramatically reduce the search time for strict epistasis. Using an Alzheimer's dataset, we showed that it is possible to discover an interaction involving the APOE gene based on its score because of its large marginal effect, but that the bound is most effective at discovering interactions without marginal effects. Conclusions/Significance: We conclude that the bound appears to ameliorate the curse of dimensionality in high-dimensional datasets. This is a very consequential result and could be pivotal in our efforts to reveal the dark matter of genetic disease risk from high-dimensional datasets. © 2012 Jiang, Neapolitan

Characterization of Epoxy Resin (SU-8) Film Using Thickness-Shear Mode (TSM) Resonator under Various Conditions

Characterization of an epoxy resin film, commonly known as SU-8, is presented using thickness shear mode (TSM) quartz resonator. The impedance-admittance characteristics of the equivalent circuit models of the unperturbed and coated resonators are analyzed to extract the storage modulus and loss modulus (G\u27 and G\u27\u27). Those parameters are needed to establish SU-8 film as an effective wave-guiding layer in the implementation of guided shear-horizontal surface acoustic wave (SH-SAW) sensor platforms. Both cured and uncured polymer films are studied at the fundamental and third harmonic frequencies of the TSM resonators. The storage modulus (G\u27) and loss modulus (G\u27\u27) of the SU-8 film approach constant values of 1.66 × 1010 dyne/cm2 and 6.0 × 108 dyne/cm2, respectively, for relatively thicker films (\u3e20 μm) at a relatively low frequency of 9 MHz. The most accurate values for the extracted shear moduli G (G = G\u27 + jG\u27\u27) are obtained at high thickness where the viscoelastic contribution to the TSM response is substantial. The effect of temperature on the storage and loss moduli is determined for the range of −75 to 40 °C. It is found that the polymer approaches a totally glassy state below −60 °C. Exposure to water appears to follow Fickian diffusion behavior at short times and this exposure also results in changes to both G\u27 and G\u27\u27. However, stability is rapidly reached with exposure to water, indicating relatively lower water absorption, consistent with the extracted diffusion coefficient

Information Visualization Of An Agent-Based Financial System Model

This paper considers the application of information visualization techniques to an agent-based model of a financial system. The minority game is a simple agent-based model which can be used to simulate the events in a real-world financial market. To aid understanding of this model, we can apply information visualization techniques. Treemap and sunburst are two such information visualization techniques, which previous research tells us can effectively represent information similar to that generated by the minority game. Another information visualization technique, called logical fisheye-lens, can be used to augment treemap and sunburst, allowing users to magnify areas of interest in these visualizations. In this paper, treemap and sunburst, both with and without fisheye-lens, are applied to the minority game, and their effectiveness is evaluated. This evaluation is carried out through an analysis of users performing various tasks on (simulated) financial market data using the visualization techniques. A subjective questionnaire is also used to measure the users’ impressions of the visualization techniques.Dynamic Models, Minority Game, Visualization

Space-filling Techniques in Visualizing Output from Computer Based Economic Models

One important factor concerning economic models is that frequently large amounts of data are produced. There is the research issue of how end-users (who may not be researchers or model developers) can be presented with this data so that maximum benefits can be attained from the data production. The usual approach with economic models is a series of tables or data series plots. In this paper we use space-filling information visualization techniques as an aid to user’s understanding of data from an economic model. Based upon evaluation of the effectiveness of existing treemap and sunburst techniques through user experimentation, we introduce two new space-filling visualization techniques. We also describe fisheye-lens techniques applicable to these new visualizations.User Interfaces, Information visualisation, Minority Game

A Mobile Geo-Communication Dataset for Physiology-Aware DASH in Rural Ambulance Transport

Use of telecommunication technologies for remote, continuous monitoring of patients can enhance effectiveness of emergency ambulance care during transport from rural areas to a regional center hospital. However, the communication along the various routes in rural areas may have wide bandwidth ranges from 2G to 4G; some regions may have only lower satellite bandwidth available. Bandwidth fluctuation together with real-time communication of various clinical multimedia pose a major challenge during rural patient ambulance transport.; AB@The availability of a pre-transport route-dependent communication bandwidth database is an important resource in remote monitoring and clinical multimedia transmission in rural ambulance transport. Here, we present a geo-communication dataset from extensive profiling of 4 major US mobile carriers in Illinois, from the rural location of Hoopeston to the central referral hospital center at Urbana. In collaboration with Carle Foundation Hospital, we developed a profiler, and collected various geographical and communication traces for realistic emergency rural ambulance transport scenarios. Our dataset is to support our ongoing work of proposing "physiology-aware DASH", which is particularly useful for adaptive remote monitoring of critically ill patients in emergency rural ambulance transport. It provides insights on ensuring higher Quality of Service (QoS) for most critical clinical multimedia in response to changes in patients' physiological states and bandwidth conditions. Our dataset is available online for research community.Comment: Proceedings of the 8th ACM on Multimedia Systems Conference (MMSys'17), Pages 158-163, Taipei, Taiwan, June 20 - 23, 201