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36th Annual Conference of the IEEE Industrial Electronics Society, IECON 2010

作者： Qin, Hengsi Kimball, Jonathan W. Venayagamoorthy, Ganesh K. Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO 65409-0040 United States Real-Time Power and Intelligent Systems Laboratory Missouri University of Science and Technology Rolla MO 65409-0040 United States

ISBN: (纸本)9781424452262

A high frequency transformer is a critical component in a dual active bridge converter (DAB) used in a power electronics-based solid state transformer. Operation of a DAB converter requires its transformer to have a specific amount of winding leakage inductance. The demand for high efficiency requires minimization of transformer copper loss and core loss. Furthermore, available window area limits the winding arrangement of transformer. A hybrid of particle swarm optimization (PSO) and differential evolution (DE) is proposed to solve this multi-objective problem with constraints. DE provides diversity to pbest of each particle, which is missing in standard PSO. The differential evolution particle swarm optimization (DEPSO) algorithm is applied to find optimal transformer designs for DAB converters. Results show the DEPSO method is a generic effective way to find optimal high frequency transformer design. © 2010 IEEE.

关键词： Particle swarm optimization (PSO)

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Intelligent Coordinated Control of a Wind Farm and Distributed Smartparks

Intelligent Coordinated Control of a Wind Farm and Distribut...

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Conference Record of the IEEE Industry Applications Society Annual Meeting (IAS)

作者： Pinaki Mitra Ganesh K. Venayagamoorthy Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA

Energy storage is generally recommended in presence of an intermittent source like wind farm for a better control over the power generation from the wind turbine with the variation of the wind speed. In this paper, the potential of plug-in electric vehicle parking lot (SmartPark) as an energy storage in a power system with a large wind farm has been investigated. Also, a fuzzy logic based coordination controller of the wind farm and the distributed SmartParks has been proposed in this paper. The fuzzy controller uses the total state-of-charge of the SmartParks and the difference between instantaneous demand and the available wind power generation as the inputs and thereby generates the charging or discharging power commands of the SmartParks and the pitch angle reference for the wind turbine. A 12-bus multimachine power system with a 400 MW wind farm is used as a test system. Six SmartParks are also connected to the same bus where the wind farm is connected. The entire model is developed in real-time Digital Simulator (RTDS) for power system. The results demonstrate the action of the coordinated controller to reduce the oscillations in the tie-line power flow with the sudden variations of the wind speed.

关键词： Wind farms Wind power generation Wind speed Niobium Energy storage Inverters Vehicles

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STLF using Neural Networks and Fuzzy for anomalous load scenarios - A case study for Hajj

STLF using Neural Networks and Fuzzy for anomalous load scen...

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International Conference on Electrical and computer Engineering (ICECE)

作者： Suman Ahmmed Mohammad Asif Ashraf Khan Md. Khairul Hasan Ahmed Yousuf Saber Mohammad Nurul Huda Mohammad Zahidur Rahman Department of Computer Science and Engineering United International University Dhaka Bangladesh Department of Computer Science and Engineering Ahsanullah University of Science and Technology Bangladesh Real-Time Power and Intelligent Systems Laboratory Missouri University of Science and Technology USA Department of Computer Science and Engineering Jahangir Nagar University Dhaka Bangladesh

Load Forecasting has an important role in load generation, scheduling, planning etc. in power system. Different computational intelligent techniques are used in Short Term Load Forecasting (STLF) to make it more effective. Neural Networks (NN) is an effective mapping algorithm that can map complex input-output relationships, which is an important technique to do STLF having existing dataset. Usually a proper NN is sufficient to achieve accepted level of performance. But different load dataset may bear some irregular nature of load demand scenario due to having special events, where accuracy of NN suffers significantly. To enhance the performance for those situations, the authors propose a hybrid STLF approach-Neural Networks and Fuzzy (NNF) method. The authors first try to select the best possible trained NN and do STLF. Considering historical data trend and of existing errors of NN solution, for those special days, NNF determine the Load Change trend. Fuzzy Inference Rules (FIR) have been developed to further improvement by fuzzy method. In fuzzy part the NNF apply FIR on two inputs: STLF of NN and Load Change trend, to enhance the performance of STLF for special events. To evaluate the proposed method it is applied on the dataset of Regional Power Control Center of Saudi Electricity Company, Western Operation Area (SEC-WOA). Since the authors deal with the daily load dataset of Saudia Arabia of Hijri years (Arabian years), Hajj has been chosen as one of the anomalous load scenario. Empirical results show that for Hajj event of Hijri 1428 year, the accuracy of STLF by NN is approximately 6.37%, whereas proposed NNF can decline the error at only 1.92%.

关键词： Artificial neural networks Data structures Boolean functions Load forecasting Finite impulse response filter Forecasting Load modeling

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Particle swarm optimization of high-frequency transformer

Particle swarm optimization of high-frequency transformer

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Annual Conference of Industrial Electronics Society

作者： Hengsi Qin Jonathan W. Kimball Ganesh K. Venayagamoorthy Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA Real-Time Power and Intelligent Systems Laboratory Missouri University of Science and Technology Rolla MO USA

关键词： Windings Inductance Magnetic cores Converters Resistance Particle swarm optimization Optimization

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Performance evaluation of a PBIL-based power system damping controller

Performance evaluation of a PBIL-based power system damping ...

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Congress on Evolutionary Computation

作者： K. A. Folly G. K. Venayagamoorthy Department of Electrical Engineering University of cape Town South Africa Real-Time Power Intelligent System laboratory Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA

Recently Population-Based Incremental Learning (PBIL) algorithm has been applied to a range of problems in engineering with promising results. However, in most of the literature the standard PBIL with fixed learning rate has been used. It has been shown that when applied to dynamic environment as the one encountered in power systems, adaptive learning rate is more appropriate to use. In this paper, Population-Based Incremental Learning (PBIL) algorithm with adaptive learning rate is used to tune the parameters of a power system controller for damping power oscillations in a multi-machine power system. The optimization of controller's parameters has been performed over pre-specified range of system operating conditions. Robustness Evaluation of the proposed controller based on eigenvalue analysis and time domain simulation shows that the proposed controller is more robust than the conventional controller over the range of operating conditions considered.

关键词： time domain analysis Analytical models Power generation Eigenvalues and eigenfunctions

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M-Polar: Channel Allocation for Throughput Maximization in SDR Mesh Networks

M-Polar: Channel Allocation for Throughput Maximization in S...

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2010 Proceedings IEEE INFOCOM

作者： Eugene Chai Kang G. Shin Real-Time Computing Laboratory Department of Electrical Engioneering and Computer Science The University of Michigan Ann Arbor MI 48109-2121 U.S.A.

ISBN: (纸本)9781424458363

In traditional wireless networks, nodes use only a single channel per radio interface, thus limiting the overall channel diversity of the network. This restriction is due to the inherent limitations of commercially-available RF devices. With the advent of high-bandwidth software-defined radios (SDRs), we now have the option of assigning multiple contiguous, independent channels to a single wireless interface. This new-found opportunity raises an important question: how do we assign contiguous channels to nodes in order to maximize overall network throughput? This question lies at the often-ignored intersection of single-radio-multi-channel and multi-radio-multi-channel assignment schemes. In this paper, we develop a protocol that assigns contiguous channels with the goal of evenly spreading the load across the multiple channels. Neighboring nodes greedily adjust their channel ranges according to channel conditions to achieve an overall pattern of partially-overlapping bandwidths that maximizes the network throughput. The end-result is a network that can dynamically adapt its bandwidth usage to the network load and the conditions of the different channels. The proposed protocol is evaluated with a prototype built upon the USRP as well as with detailed simulation.

关键词： Channel allocation RF Devices radio interface Ganglia Mesh networks single-channel MULTICHANNEL Network throughput SDPR gene LOAD protocols Software radio Bandwidth

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ERRATUM: 'FERMI DETECTION OF γ-RAY EMISSION FROM THE M2 SOFT X-RAY FLARE ON 2010 JUNE 12' (2012, ApJ, 745, 144)

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The Astrophysical Journal 2012年第2期748卷

作者： M. Ackermann M. Ajello A. Allafort W. B. Atwood L. Baldini G. Barbiellini D. Bastieri K. Bechtol R. Bellazzini P. N. Bhat R. D. Blandford E. Bonamente A. W. Borgland J. Bregeon M. S. Briggs M. Brigida P. Bruel R. Buehler J. M. Burgess S. Buson G. A. Caliandro R. A. Cameron J. M. Casandjian C. Cecchi E. Charles A. Chekhtman J. Chiang S. Ciprini R. Claus J. Cohen-Tanugi V. Connaughton J. Conrad S. Cutini B. R. Dennis F. de Palma C. D. Dermer S. W. Digel E. do Couto e Silva P. S. Drell A. Drlica-Wagner R. Dubois C. Favuzzi S. J. Fegan E. C. Ferrara P. Fortin Y. Fukazawa P. Fusco F. Gargano S. Germani N. Giglietto F. Giordano M. Giroletti T. Glanzman G. Godfrey L. Grillo J. E. Grove D. Gruber S. Guiriec D. Hadasch M. Hayashida E. Hays D. Horan G. Iafrate G. Jóhannesson A. S. Johnson W. N. Johnson T. Kamae R. M. Kippen J. Knödlseder M. Kuss J. Lande L. Latronico F. Longo F. Loparco B. Lott M. N. Lovellette P. Lubrano M. N. Mazziotta J. E. McEnery C. Meegan J. Mehault P. F. Michelson W. Mitthumsiri C. Monte M. E. Monzani A. Morselli I. V. Moskalenko S. Murgia R. Murphy M. Naumann-Godo E. Nuss T. Nymark M. Ohno T. Ohsugi A. Okumura N. Omodei E. Orlando W. S. Paciesas J. H. Panetta D. Parent M. Pesce-Rollins V. Petrosian M. Pierbattista F. Piron G. Pivato H. Poon T. A. Porter R. Preece S. Rainò R. Rando M. Razzano S. Razzaque A. Reimer O. Reimer S. Ritz C. Sbarra R. A. Schwartz C. Sgrò G. H. Share E. J. Siskind P. Spinelli H. Takahashi T. Tanaka Y. Tanaka J. B. Thayer L. Tibaldo M. Tinivella A. K. Tolbert G. Tosti E. Troja Y. Uchiyama T. L. Usher J. Vandenbroucke V. Vasileiou G. Vianello V. Vitale A. vonKienlin A. P. Waite C. Wilson-Hodge D. L. Wood K. S. Wood Z. Yang Deutsches Elektronen Synchrotron DESY D-15738 Zeuthen Germany W. W. Hansen Experimental Physics Laboratory Kavli Institute for Particle Astrophysics and Cosmology Department of Physics and SLAC National Accelerator Laboratory Stanford University Stanford CA 94305 USA Santa Cruz Institute for Particle Physics Department of Physics and Department of Astronomy and Astrophysics University of California at Santa Cruz Santa Cruz CA 95064 USA Istituto Nazionale di Fisica Nucleare Sezione di Pisa I-56127 Pisa Italy Istituto Nazionale di Fisica Nucleare Sezione di Trieste I-34127 Trieste Italy Dipartimento di Fisica Università di Trieste I-34127 Trieste Italy Istituto Nazionale di Fisica Nucleare Sezione di Padova I-35131 Padova Italy Dipartimento di Fisica 'G. Galilei ' Università di Padova I-35131 Padova Italy Center for Space Plasma and Aeronomic Research (CSPAR) University of Alabama in Huntsville Huntsville AL 35899 USA Istituto Nazionale di Fisica Nucleare Sezione di Perugia I-06123 Perugia Italy Dipartimento di Fisica Università degli Studi di Perugia I-06123 Perugia Italy Dipartimento di Fisica 'M. Merlin ' dell'Università e del Politecnico di Bari I-70126 Bari Italy Istituto Nazionale di Fisica Nucleare Sezione di Bari I-70126 Bari Italy Laboratoire Leprince-Ringuet École polytechnique CNRS/IN2P3 Palaiseau France Institut de Ciències de l'Espai (IEEE-CSIC) Campus UAB E-08193 Barcelona Spain Laboratoire AIM CEA-IRFU/CNRS/Université Paris Diderot Service d'Astrophysique CEA Saclay 91191 Gif sur Yvette France Artep Inc. 2922 Excelsior Springs Court Ellicott City MD 21042 USA Resident at Naval Research Laboratory Washington DC 20375 USA. ASI Science Data Center I-00044 Frascati (Roma) Italy Laboratoire Univers et Particules de Montpellier Université Montpellier 2 CNRS/IN2P3 Montpellier France Department of Physics Stockholm University AlbaNova SE-106 91 Stockholm Sweden The Oskar Klein Centre for Cosmoparticle Physics AlbaNova SE-106 91 Stockhol

Due to an error at the publisher, the times given for the major tick marks in the X-axis in Figure 1 of the published article are incorrect. The correctly labeled times should be '00:52:00,' '00:54:00,'..., and '01:04:00.' The correct version of Figure 1 and its caption is shown below. IOP Publishing sincerely regrets this error.

关键词： acceleration of particles Sun: flares Sun: particle emission Sun: X-rays, gamma rays

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Constraining Dark Matter Models from a Combined Analysis of Milky Way Satellites with the Fermi Large Area Telescope

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Physical Review Letters 2011年第24期107卷 241302-241302页

作者： M. Ackermann M. Ajello A. Albert W. B. Atwood L. Baldini J. Ballet G. Barbiellini D. Bastieri K. Bechtol R. Bellazzini B. Berenji R. D. Blandford E. D. Bloom E. Bonamente A. W. Borgland J. Bregeon M. Brigida P. Bruel R. Buehler T. H. Burnett S. Buson G. A. Caliandro R. A. Cameron B. Cañadas P. A. Caraveo J. M. Casandjian C. Cecchi E. Charles A. Chekhtman J. Chiang S. Ciprini R. Claus J. Cohen-Tanugi J. Conrad S. Cutini A. de Angelis F. de Palma C. D. Dermer S. W. Digel E. do Couto e Silva P. S. Drell A. Drlica-Wagner L. Falletti C. Favuzzi S. J. Fegan E. C. Ferrara Y. Fukazawa S. Funk P. Fusco F. Gargano D. Gasparrini N. Gehrels S. Germani N. Giglietto F. Giordano M. Giroletti T. Glanzman G. Godfrey I. A. Grenier S. Guiriec M. Gustafsson D. Hadasch M. Hayashida E. Hays R. E. Hughes T. E. Jeltema G. Jóhannesson R. P. Johnson A. S. Johnson T. Kamae H. Katagiri J. Kataoka J. Knödlseder M. Kuss J. Lande L. Latronico A. M. Lionetto M. Llena Garde F. Longo F. Loparco B. Lott M. N. Lovellette P. Lubrano G. M. Madejski M. N. Mazziotta J. E. McEnery J. Mehault P. F. Michelson W. Mitthumsiri T. Mizuno C. Monte M. E. Monzani A. Morselli I. V. Moskalenko S. Murgia M. Naumann-Godo J. P. Norris E. Nuss T. Ohsugi A. Okumura N. Omodei E. Orlando J. F. Ormes M. Ozaki D. Paneque D. Parent M. Pesce-Rollins M. Pierbattista F. Piron G. Pivato T. A. Porter S. Profumo S. Rainò M. Razzano A. Reimer O. Reimer S. Ritz M. Roth H. F.-W. Sadrozinski C. Sbarra J. D. Scargle T. L. Schalk C. Sgrò E. J. Siskind G. Spandre P. Spinelli L. Strigari D. J. Suson H. Tajima H. Takahashi T. Tanaka J. G. Thayer J. B. Thayer D. J. Thompson L. Tibaldo M. Tinivella D. F. Torres E. Troja Y. Uchiyama J. Vandenbroucke V. Vasileiou G. Vianello V. Vitale A. P. Waite P. Wang B. L. Winer K. S. Wood M. Wood Z. Yang S. Zimmer M. Kaplinghat G. D. Martinez W. W. Hansen Experimental Physics Laboratory Kavli Institute for Particle Astrophysics and Cosmology Department of Physics and SLAC National Accelerator Laboratory Stanford University Stanford California 94305 USA Department of Physics Center for Cosmology and Astro-Particle Physics The Ohio State University Columbus Ohio 43210 USA Santa Cruz Institute for Particle Physics Department of Physics and Department of Astronomy and Astrophysics University of California at Santa Cruz Santa Cruz California 95064 USA Istituto Nazionale di Fisica Nucleare Sezione di Pisa I-56127 Pisa Italy Laboratoire AIM CEA-IRFU/CNRS/Université Paris Diderot Service d’Astrophysique CEA Saclay 91191 Gif sur Yvette France Istituto Nazionale di Fisica Nucleare Sezione di Trieste I-34127 Trieste Italy Dipartimento di Fisica Università di Trieste I-34127 Trieste Italy Istituto Nazionale di Fisica Nucleare Sezione di Padova I-35131 Padova Italy Dipartimento di Fisica “G. Galilei ” Università di Padova I-35131 Padova Italy Istituto Nazionale di Fisica Nucleare Sezione di Perugia I-06123 Perugia Italy Dipartimento di Fisica Università degli Studi di Perugia I-06123 Perugia Italy Dipartimento di Fisica “M. Merlin” dell’Università e del Politecnico di Bari I-70126 Bari Italy Istituto Nazionale di Fisica Nucleare Sezione di Bari 70126 Bari Italy Laboratoire Leprince-Ringuet École polytechnique CNRS/IN2P3 Palaiseau France Department of Physics University of Washington Seattle Washington 98195-1560 USA Institut de Ciències de l’Espai (IEEE-CSIC) Campus UAB 08193 Barcelona Spain Istituto Nazionale di Fisica Nucleare Sezione di Roma “Tor Vergata” I-00133 Roma Italy Dipartimento di Fisica Università di Roma “Tor Vergata” I-00133 Roma Italy INAF-Istituto di Astrofisica Spaziale e Fisica Cosmica I-20133 Milano Italy Artep Inc. 2922 Excelsior Springs Court Ellicott City Maryland 21042 USA ASI Science Data Center I-00044 Frascati (Roma) Italy Laboratoire Univers et Particules de Montpell

Satellite galaxies of the Milky Way are among the most promising targets for dark matter searches in gamma rays. We present a search for dark matter consisting of weakly interacting massive particles, applying a joint likelihood analysis to 10 satellite galaxies with 24 months of data of the Fermi Large Area Telescope. No dark matter signal is detected. Including the uncertainty in the dark matter distribution, robust upper limits are placed on dark matter annihilation cross sections. The 95% confidence level upper limits range from about 10−26 cm3 s−1 at 5 GeV to about 5×10−23 cm3 s−1 at 1 TeV, depending on the dark matter annihilation final state. For the first time, using gamma rays, we are able to rule out models with the most generic cross section (∼3×10−26 cm3 s−1 for a purely s-wave cross section), without assuming additional boost factors.

关键词： Satellites

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Impact of mobility on spectrum sensing in cognitive radio networks

Impact of mobility on spectrum sensing in cognitive radio ne...

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2009 ACM Workshop on Cognitive Radio Networks, CoRoNet'09, Co-located with the Mobile computing and Networking Conference, MobiCom'09

作者： Min, Alexander W. Shin, Kang G. Real-Time Computing Laboratory Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI 48109-2121 United States

ISBN: (纸本)9781605587387

In cognitive radio networks (CRNs), spectrum sensing is key to opportunistic spectrum access while preventing any unacceptable interference to primary users' communications. Although cognitive radios function as spectrum sensors and move around, most, if not all, of existing approaches assume stationary spectrum sensors, thus providing inaccurate sensing results. As part of our effort to solve/alleviate this problem, we consider the impact of sensor mobility on spectrum sensing performance in a joint optimization framework for sensor cooperation and sensing scheduling. We show that sensor mobility increases spatio-temporal diversity in received primary signal strengths, and thus, improves the sensing performance. This is intuitively plausible, but have not been tested previously. Based on this observation, we propose a sensing strategy that minimizes the sensing overhead by finding an optimal combination of the number of sensors to cooperate and the number of times spectrum sensing must be scheduled. This result provides a useful insight to understand the spectrum sensing and its coupling with sensor mobility. Copyright 2009 ACM.

关键词： Cognitive radio

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Constraints on dark matter models from a Fermi LAT search for high-energy cosmic-ray electrons from the Sun

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Physical Review D 2011年第3期84卷 032007-032007页

作者： M. Ajello W. B. Atwood L. Baldini G. Barbiellini D. Bastieri R. Bellazzini B. Berenji R. D. Blandford E. D. Bloom E. Bonamente A. W. Borgland E. Bottacini A. Bouvier J. Bregeon M. Brigida P. Bruel R. Buehler S. Buson G. A. Caliandro R. A. Cameron P. A. Caraveo C. Cecchi E. Charles A. Chekhtman S. Ciprini R. Claus J. Cohen-Tanugi S. Cutini A. de Angelis F. de Palma C. D. Dermer S. W. Digel E. do Couto e Silva P. S. Drell C. Favuzzi S. J. Fegan W. B. Focke Y. Fukazawa P. Fusco F. Gargano N. Gehrels S. Germani N. Giglietto F. Giordano M. Giroletti T. Glanzman G. Godfrey I. A. Grenier S. Guiriec M. Gustafsson D. Hadasch G. Iafrate G. Jóhannesson A. S. Johnson T. Kamae H. Katagiri J. Kataoka M. Kuss L. Latronico A. M. Lionetto F. Longo F. Loparco M. N. Lovellette P. Lubrano M. N. Mazziotta J. E. McEnery P. F. Michelson T. Mizuno C. Monte M. E. Monzani A. Morselli I. V. Moskalenko S. Murgia M. Naumann-Godo J. P. Norris E. Nuss T. Ohsugi N. Omodei E. Orlando J. F. Ormes M. Ozaki D. Paneque J. H. Panetta M. Pesce-Rollins M. Pierbattista F. Piron S. Rainò R. Rando M. Razzano A. Reimer O. Reimer S. Ritz T. L. Schalk C. Sgrò J. Siegal-Gaskins E. J. Siskind P. D. Smith G. Spandre P. Spinelli D. J. Suson H. Takahashi T. Tanaka J. G. Thayer J. B. Thayer L. Tibaldo G. Tosti E. Troja T. L. Usher J. Vandenbroucke V. Vasileiou G. Vianello N. Vilchez A. P. Waite P. Wang B. L. Winer K. S. Wood Z. Yang S. Zimmer W. W. Hansen Experimental Physics Laboratory Kavli Institute for Particle Astrophysics and Cosmology Department of Physics and SLAC National Accelerator Laboratory Stanford University Stanford California 94305 USA Santa Cruz Institute for Particle Physics Department of Physics and Department of Astronomy and Astrophysics University of California at Santa Cruz Santa Cruz California 95064 USA Istituto Nazionale di Fisica Nucleare Sezione di Pisa I-56127 Pisa Italy Istituto Nazionale di Fisica Nucleare Sezione di Trieste I-34127 Trieste Italy Dipartimento di Fisica Università di Trieste I-34127 Trieste Italy Istituto Nazionale di Fisica Nucleare Sezione di Padova I-35131 Padova Italy Dipartimento di Fisica “G. Galilei ” Università di Padova I-35131 Padova Italy Istituto Nazionale di Fisica Nucleare Sezione di Perugia I-06123 Perugia Italy Dipartimento di Fisica Università degli Studi di Perugia I-06123 Perugia Italy Dipartimento di Fisica “M. Merlin” dell’Università e del Politecnico di Bari I-70126 Bari Italy Istituto Nazionale di Fisica Nucleare Sezione di Bari 70126 Bari Italy Laboratoire Leprince-Ringuet École polytechnique CNRS/IN2P3 Palaiseau France Institut de Ciències de l’Espai (IEEE-CSIC) Campus UAB 08193 Barcelona Spain INAF-Istituto di Astrofisica Spaziale e Fisica Cosmica I-20133 Milano Italy Artep Inc. 2922 Excelsior Springs Court Ellicott City Maryland 21042 USA and resident at Naval Research Laboratory Washington D.C. 20375 USA ASI Science Data Center I-00044 Frascati (Roma) Italy Laboratoire Univers et Particules de Montpellier Université Montpellier 2 CNRS/IN2P3 Montpellier France Agenzia Spaziale Italiana (ASI) Science Data Center I-00044 Frascati (Roma) Italy Dipartimento di Fisica Università di Udine and Istituto Nazionale di Fisica Nucleare Sezione di Trieste Gruppo Collegato di Udine I-33100 Udine Italy Space Science Division Naval Research Laboratory Washington D.C. 20375-5352 USA Department of Physical Sciences Hiroshima

During its first year of data taking, the Large Area Telescope (LAT) onboard the Fermi Gamma-Ray Space Telescope has collected a large sample of high-energy cosmic-ray electrons and positrons (CREs). We present the results of a directional analysis of the CRE events, in which we searched for a flux excess correlated with the direction of the Sun. Two different and complementary analysis approaches were implemented, and neither yielded evidence of a significant CRE flux excess from the Sun. We derive upper limits on the CRE flux from the Sun’s direction, and use these bounds to constrain two classes of dark matter models which predict a solar CRE flux: (1) models in which dark matter annihilates to CREs via a light intermediate state, and (2) inelastic dark matter models in which dark matter annihilates to CREs.

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