Online coherency identification and stability condition for large interconnected power systems using an unsupervised data mining technique

Identification of coherent generators and the determination of the stability system condition in large interconnected power system is one of the key steps to carry out different control system strategies to avoid a partial or complete blackout of a power system. However, the oscillatory trends, the larger amount data available and the non-linear dynamic behaviour of the frequency measurements often mislead the appropriate knowledge of the actual coherent groups, making wide-area coherency monitoring a challenging task. This paper presents a novel online unsupervised data mining technique to identify coherent groups, to detect the power system disturbance event and determine status stability condition of the system. The innovative part of the proposed approach resides on combining traditional plain algorithms such as singular value decomposition (SVD) and K -means for clustering together with new concept based on clustering slopes. The proposed combination provides an added value to other applications relying on similar algorithms available in the literature. To validate the effectiveness of the proposed method, two case studies are presented, where data is extracted from the large and comprehensive initial dynamic model of ENTSO-E and the results compared to other alternative methods available in the literature

Rejection-Cascade of Gaussians: Real-time adaptive background subtraction framework

Background-Foreground classification is a well-studied problem in computer vision. Due to the pixel-wise nature of modeling and processing in the algorithm, it is usually difficult to satisfy real-time constraints. There is a trade-off between the speed (because of model complexity) and accuracy. Inspired by the rejection cascade of Viola-Jones classifier, we decompose the Gaussian Mixture Model (GMM) into an adaptive cascade of Gaussians(CoG). We achieve a good improvement in speed without compromising the accuracy with respect to the baseline GMM model. We demonstrate a speed-up factor of 4-5x and 17 percent average improvement in accuracy over Wallflowers surveillance datasets. The CoG is then demonstrated to over the latent space representation of images of a convolutional variational autoencoder(VAE). We provide initial results over CDW-2014 dataset, which could speed up background subtraction for deep architectures.Comment: Accepted for National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG 2019

Diagnostic Applications for Micro-Synchrophasor Measurements

This report articulates and justifies the preliminary selection of diagnostic applications for data from micro-synchrophasors (µPMUs) in electric power distribution systems that will be further studied and developed within the scope of the three-year ARPA-e award titled Micro-synchrophasors for Distribution Systems

Bayesian meta-analysis for identifying periodically expressed genes in fission yeast cell cycle

The effort to identify genes with periodic expression during the cell cycle from genome-wide microarray time series data has been ongoing for a decade. However, the lack of rigorous modeling of periodic expression as well as the lack of a comprehensive model for integrating information across genes and experiments has impaired the effort for the accurate identification of periodically expressed genes. To address the problem, we introduce a Bayesian model to integrate multiple independent microarray data sets from three recent genome-wide cell cycle studies on fission yeast. A hierarchical model was used for data integration. In order to facilitate an efficient Monte Carlo sampling from the joint posterior distribution, we develop a novel Metropolis--Hastings group move. A surprising finding from our integrated analysis is that more than 40% of the genes in fission yeast are significantly periodically expressed, greatly enhancing the reported 10--15% of the genes in the current literature. It calls for a reconsideration of the periodically expressed gene detection problem.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS300 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

A toolbox for animal call recognition

Monitoring the natural environment is increasingly important as habit degradation and climate change reduce theworld’s biodiversity.We have developed software tools and applications to assist ecologists with the collection and analysis of acoustic data at large spatial and temporal scales.One of our key objectives is automated animal call recognition, and our approach has three novel attributes. First, we work with raw environmental audio, contaminated by noise and artefacts and containing calls that vary greatly in volume depending on the animal’s proximity to the microphone. Second, initial experimentation suggested that no single recognizer could dealwith the enormous variety of calls. Therefore, we developed a toolbox of generic recognizers to extract invariant features for each call type. Third, many species are cryptic and offer little data with which to train a recognizer. Many popular machine learning methods require large volumes of training and validation data and considerable time and expertise to prepare. Consequently we adopt bootstrap techniques that can be initiated with little data and refined subsequently. In this paper, we describe our recognition tools and present results for real ecological problems

Frequency Monitoring Network (FNET) Data Center Development and Data Analysis

Frequency Monitoring Network (FNET) is an Internet-based, wide-area phasor measurement system that collects power system data using Frequency Disturbance Recorders (FDRs) that are installed at the distribution level. The FNET data center enables the monitoring of bulk power systems, and provides wide-area situational awareness and disturbance analysis for understanding power system disturbances and system operations. Therefore, the data center plays a very critical role in the entire FNET system framework. In recent years, many potential challenges brought by the rapid expansion of the FNET system have underlined the importance of designing the next-generation FNET data center. More discussions about the motivation and guidelines to design the next-generation FNET data center will be presented in Chapter 2, along with a brief introduction of the new infrastructure composing of multiple data storage and application layers. A distributed alarming agent that communicates between real-time applications and near-real-time applications is discussed in detail. Chapter 3 proposes the data storage solutions for FNET time-series measurement data, configuration data and analysis records. Chapter 4 addresses the challenges of the real-time application development. The algorithm, configuration parameters and data processing procedures of the real-time event detection, oscillation detection, and islanding detection are presented in detail. Chapter 5 introduces the implementation of the FNET map-based web display using the measurement data feed provided by the openHistorian data publisher service. Besides contributing to the situation awareness applications, the researches presented here explore novel data analysis perspectives to investigate power grids’ behavior. Chapter 6 introduces a frequency distribution probability calculation method, applies this method to frequency measurement data from 2005-2013 collected by the FNET system, investigates the distribution probability of frequency data over North American and also worldwide power grids, and compares the distribution patterns during different years, seasons, days of a week and periods of a day. Chapter 7 presents a solution method to produce replay videos based on FDRs’ normalized voltage magnitude data and investigates the voltage magnitude pattern changes over the Eastern Interconnection (EI) during events and days by using historical FNET measurement data. Conclusions and possible future research topics are given in Chapter 8

Systematic search for gamma-ray periodicity in active galactic nuclei detected by the Fermi Large Area Telescope

We use nine years of gamma-ray data provided by the Fermi Large Area Telescope (LAT) to systematically study the light curves of more than two thousand active galactic nuclei (AGN) included in recent Fermi-LAT catalogs. Ten different techniques are used, which are organized in an automatic periodicity-search pipeline, in order to search for evidence of periodic emission in gamma rays. Understanding the processes behind this puzzling phenomenon will provide a better view about the astrophysical nature of these extragalactic sources. However, the observation of temporal patterns in gamma-ray light curves of AGN is still challenging. Despite the fact that there have been efforts on characterizing the temporal emission of some individual sources, a systematic search for periodicities by means of a full likelihood analysis applied to large samples of sources was missing. Our analysis finds 11 AGN, of which 9 are identified for the first time, showing periodicity at more than 4sigma in at least four algorithms. These findings will help in solving questions related to the astrophysical origin of this periodic behavior.Comment: 16 pages, 5 figures, 4 tables. Accepted by Ap

An ancient extrasolar system with five sub-Earth-size planets

The chemical composition of stars hosting small exoplanets (with radii less than four Earth radii) appears to be more diverse than that of gas-giant hosts, which tend to be metal-rich. This implies that small, including Earth-size, planets may have readily formed at earlier epochs in the Universe's history when metals were more scarce. We report Kepler spacecraft observations of Kepler-444, a metal-poor Sun-like star from the old population of the Galactic thick disk and the host to a compact system of five transiting planets with sizes between those of Mercury and Venus. We validate this system as a true five-planet system orbiting the target star and provide a detailed characterization of its planetary and orbital parameters based on an analysis of the transit photometry. Kepler-444 is the densest star with detected solar-like oscillations. We use asteroseismology to directly measure a precise age of 11.2+/-1.0 Gyr for the host star, indicating that Kepler-444 formed when the Universe was less than 20% of its current age and making it the oldest known system of terrestrial-size planets. We thus show that Earth-size planets have formed throughout most of the Universe's 13.8-billion-year history, leaving open the possibility for the existence of ancient life in the Galaxy. The age of Kepler-444 not only suggests that thick-disk stars were among the hosts to the first Galactic planets, but may also help to pinpoint the beginning of the era of planet formation.Comment: Accepted for publication in ApJ; 42 pages, 10 figures, 4 table