39,718 research outputs found

    Optimal load shedding for microgrids with unlimited DGs

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    Recent years, increasing trends on electrical supply demand, make us to search for the new alternative in supplying the electrical power. A study in micro grid system with embedded Distribution Generations (DGs) to the system is rapidly increasing. Micro grid system basically is design either operate in islanding mode or interconnect with the main grid system. In any condition, the system must have reliable power supply and operating at low transmission power loss. During the emergency state such as outages of power due to electrical or mechanical faults in the system, it is important for the system to shed any load in order to maintain the system stability and security. In order to reduce the transmission loss, it is very important to calculate best size of the DGs as well as to find the best positions in locating the DG itself.. Analytical Hierarchy Process (AHP) has been applied to find and calculate the load shedding priorities based on decision alternatives which have been made. The main objective of this project is to optimize the load shedding in the micro grid system with unlimited DG’s by applied optimization technique Gravitational Search Algorithm (GSA). The technique is used to optimize the placement and sizing of DGs, as well as to optimal the load shedding. Several load shedding schemes have been proposed and studied in this project such as load shedding with fixed priority index, without priority index and with dynamic priority index. The proposed technique was tested on the IEEE 69 Test Bus Distribution system

    Detecting gravitational radiation from neutron stars using a six-parameter adaptive MCMC method

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    We present a Markov chain Monte Carlo technique for detecting gravitational radiation from a neutron star in laser interferometer data. The algorithm can estimate up to six unknown parameters of the target, including the rotation frequency and frequency derivative, using reparametrization, delayed rejection and simulated annealing. We highlight how a simple extension of the method, distributed over multiple computer processors, will allow for a search over a narrow frequency band. The ultimate goal of this research is to search for sources at a known locations, but uncertain spin parameters, such as may be found in SN1987A.Comment: Submitted to Classical and Quantum Gravity for GWDAW-8 proceeding

    A burst search for gravitational waves from binary black holes

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    Compact binary coalescence (CBC) is one of the most promising sources of gravitational waves. These sources are usually searched for with matched filters which require accurate calculation of the GW waveforms and generation of large template banks. We present a complementary search technique based on algorithms used in un-modeled searches. Initially designed for detection of un-modeled bursts, which can span a very large set of waveform morphologies, the search algorithm presented here is constrained for targeted detection of the smaller subset of CBC signals. The constraint is based on the assumption of elliptical polarisation for signals received at the detector. We expect that the algorithm is sensitive to CBC signals in a wide range of masses, mass ratios, and spin parameters. In preparation for the analysis of data from the fifth LIGO-Virgo science run (S5), we performed preliminary studies of the algorithm on test data. We present the sensitivity of the search to different types of simulated CBC waveforms. Also, we discuss how to extend the results of the test run into a search over all of the current LIGO-Virgo data set.Comment: 12 pages, 4 figures, 2 tables, submitted for publication in CQG in the special issue for the conference proceedings of GWDAW13; corrected some typos, addressed some minor reviewer comments one section restructured and references updated and correcte

    Implementation of barycentric resampling for continuous wave searches in gravitational wave data

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    We describe an efficient implementation of a coherent statistic for continuous gravitational wave searches from neutron stars. The algorithm works by transforming the data taken by a gravitational wave detector from a moving Earth bound frame to one that sits at the Solar System barycenter. Many practical difficulties arise in the implementation of this algorithm, some of which have not been discussed previously. These difficulties include constraints of small computer memory, discreteness of the data, losses due to interpolation and gaps in real data. This implementation is considerably more efficient than previous implementations of these kinds of searches on Laser Interferometer Gravitational Wave (LIGO) detector data.Comment: 10 pages, 3 figure

    A Metropolis-Hastings algorithm for extracting periodic gravitational wave signals from laser interferometric detector data

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    The Markov chain Monte Carlo methods offer practical procedures for detecting signals characterized by a large number of parameters and under conditions of low signal-to-noise ratio. We present a Metropolis-Hastings algorithm capable of inferring the spin and orientation parameters of a neutron star from its periodic gravitational wave signature seen by laser interferometric detector

    An all-sky search algorithm for continuous gravitational waves from spinning neutron stars in binary systems

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    Rapidly spinning neutron stars with non-axisymmetric mass distributions are expected to generate quasi-monochromatic continuous gravitational waves. While many searches for unknown, isolated spinning neutron stars have been carried out, there have been no previous searches for unknown sources in binary systems. Since current search methods for unknown, isolated neutron stars are already computationally limited, expanding the parameter space searched to include binary systems is a formidable challenge. We present a new hierarchical binary search method called TwoSpect, which exploits the periodic orbital modulations of the continuous waves by searching for patterns in doubly Fourier-transformed data. We will describe the TwoSpect search pipeline, including its mitigation of detector noise variations and corrections for Doppler frequency modulation caused by changing detector velocity. Tests on Gaussian noise and on a set of simulated signals will be presented.Comment: 22 pages, 10 figures, 1 table, Submitted to Classical and Quantum Gravit

    A Bayesian Approach to the Detection Problem in Gravitational Wave Astronomy

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    The analysis of data from gravitational wave detectors can be divided into three phases: search, characterization, and evaluation. The evaluation of the detection - determining whether a candidate event is astrophysical in origin or some artifact created by instrument noise - is a crucial step in the analysis. The on-going analyses of data from ground based detectors employ a frequentist approach to the detection problem. A detection statistic is chosen, for which background levels and detection efficiencies are estimated from Monte Carlo studies. This approach frames the detection problem in terms of an infinite collection of trials, with the actual measurement corresponding to some realization of this hypothetical set. Here we explore an alternative, Bayesian approach to the detection problem, that considers prior information and the actual data in hand. Our particular focus is on the computational techniques used to implement the Bayesian analysis. We find that the Parallel Tempered Markov Chain Monte Carlo (PTMCMC) algorithm is able to address all three phases of the anaylsis in a coherent framework. The signals are found by locating the posterior modes, the model parameters are characterized by mapping out the joint posterior distribution, and finally, the model evidence is computed by thermodynamic integration. As a demonstration, we consider the detection problem of selecting between models describing the data as instrument noise, or instrument noise plus the signal from a single compact galactic binary. The evidence ratios, or Bayes factors, computed by the PTMCMC algorithm are found to be in close agreement with those computed using a Reversible Jump Markov Chain Monte Carlo algorithm.Comment: 19 pages, 12 figures, revised to address referee's comment

    Use of the MultiNest algorithm for gravitational wave data analysis

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    We describe an application of the MultiNest algorithm to gravitational wave data analysis. MultiNest is a multimodal nested sampling algorithm designed to efficiently evaluate the Bayesian evidence and return posterior probability densities for likelihood surfaces containing multiple secondary modes. The algorithm employs a set of live points which are updated by partitioning the set into multiple overlapping ellipsoids and sampling uniformly from within them. This set of live points climbs up the likelihood surface through nested iso-likelihood contours and the evidence and posterior distributions can be recovered from the point set evolution. The algorithm is model-independent in the sense that the specific problem being tackled enters only through the likelihood computation, and does not change how the live point set is updated. In this paper, we consider the use of the algorithm for gravitational wave data analysis by searching a simulated LISA data set containing two non-spinning supermassive black hole binary signals. The algorithm is able to rapidly identify all the modes of the solution and recover the true parameters of the sources to high precision.Comment: 18 pages, 4 figures, submitted to Class. Quantum Grav; v2 includes various changes in light of referee's comment

    Spectral Line Removal in the LIGO Data Analysis System (LDAS)

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    High power in narrow frequency bands, spectral lines, are a feature of an interferometric gravitational wave detector's output. Some lines are coherent between interferometers, in particular, the 2 km and 4 km LIGO Hanford instruments. This is of concern to data analysis techniques, such as the stochastic background search, that use correlations between instruments to detect gravitational radiation. Several techniques of `line removal' have been proposed. Where a line is attributable to a measurable environmental disturbance, a simple linear model may be fitted to predict, and subsequently subtract away, that line. This technique has been implemented (as the command oelslr) in the LIGO Data Analysis System (LDAS). We demonstrate its application to LIGO S1 data.Comment: 11 pages, 5 figures, to be published in CQG GWDAW02 proceeding

    Hydra: An Adaptive--Mesh Implementation of PPPM--SPH

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    We present an implementation of Smoothed Particle Hydrodynamics (SPH) in an adaptive-mesh PPPM algorithm. The code evolves a mixture of purely gravitational particles and gas particles. The code retains the desirable properties of previous PPPM--SPH implementations; speed under light clustering, naturally periodic boundary conditions and accurate pairwise forces. Under heavy clustering the cycle time of the new code is only 2--3 times slower than for a uniform particle distribution, overcoming the principal disadvantage of previous implementations\dash a dramatic loss of efficiency as clustering develops. A 1000 step simulation with 65,536 particles (half dark, half gas) runs in one day on a Sun Sparc10 workstation. The choice of time integration scheme is investigated in detail. A simple single-step Predictor--Corrector type integrator is most efficient. A method for generating an initial distribution of particles by allowing a a uniform temperature gas of SPH particles to relax within a periodic box is presented. The average SPH density that results varies by ∌±1.3\sim\pm1.3\%. We present a modified form of the Layzer--Irvine equation which includes the thermal contribution of the gas together with radiative cooling. Tests of sound waves, shocks, spherical infall and collapse are presented. Appropriate timestep constraints sufficient to ensure both energy and entropy conservation are discussed. A cluster simulation, repeating Thomas andComment: 29 pp, uuencoded Postscrip
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