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Resource allocation for periodic applications in a shipboard environment

Resource allocation for periodic applications in a shipboard...

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19th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2005

作者： Shestak, Vladimir Chong, Edwin K. P. Maciejewski, Anthony A. Siegel, H.J. Benmohamed, Lotfi Wang, I.-Jeng Daley, Rose Colorado State University Department of Electrical and Computer Engineering Fort Collins CO 80523-1373 Colorado State University Department of Computer Science Fort Collins CO 80523-1373 Johns Hopkins University Applied Physics Laboratory 11100 Johns Hopkins Road Laurel MD 20723-6099 Department of Electrical and Computer Engineering Colorado State University Department of Electrical and Computer Engineering Department of Mathematics Colorado State University IEEE Colorado State University Department of Computer Science ACM Eta Kappa Nu Electrical Engineering Honor Society Sigma Xi Science Honor Society Upsilon Pi Epsilon Computing Sciences Honor Society Johns Hopkins University Applied Physics Laboratory JHU/APL Applied Physics Lab. IR and D

ISBN: (纸本)0769523129

Providing efficient workload management is an important issue for a large-scale heterogeneous distributed computing environment where a set of periodic applications is executed. The considered distributed system is expected to operate in an environment where the input workload is likely to change unpredictably, possibly invalidating a resource allocation that was based on the initial workload estimate. The tasks consist of multiple application strings, each made up of an ordered sequence of applications. There are quality of service (QoS) constraints that must be satisfied for each string. This work addresses the problem of finding a robust initial allocation of resources to application strings that is able to absorb some level of unknown input workload increase without rescheduling. An allocation feasibility analysis is presented followed by four heuristics for finding a near-optimal allocation of resources. The performance of the proposed heuristics is evaluated and compared using simulation. The proposed heuristics also are compared to a mathematically derived upper bound. © 2005 IEEE.

关键词： Resource allocation

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A dynamic recurrent neural network for wide area identification of a multimachine power system with a FACTS device

A dynamic recurrent neural network for wide area identificat...

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International Conference on Intelligent Systems Applications to Power Systems (ISAP)

作者： S. Mohagheghi G.K. Venayagamoorthy R.G. Harley Georgia Institute of Technology School of Electrical and Computer Engineering Atlanta GA USA Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla MO USA

Multilayer perceptron and radial basis function neural networks have been traditionally used for plant identification in power systems applications of neural networks. While being efficient in tracking the plant dynamics in a relatively small system, their performance degrades as the dimensions of the plant to be identified are increased, for example in supervisory level identification of a multimachine power system for wide area control purposes. Recurrent neural networks can deal with such a problem by modeling the system as a set of differential equations and with less order of complexity. Such a recurrent neural network identifier is designed and implemented for supervisory level identification of a multimachine power system with a FACTS device. Simulation results are provided to show that the neuroidentifier can track the system dynamics with sufficient accuracy

关键词： Power system dynamics Recurrent neural networks Power systems Power system modeling Power system simulation Multilayer perceptrons Radial basis function networks Neural networks Multi-layer neural network Degradation

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A neural network based Wide Area Monitor for a power system

A neural network based Wide Area Monitor for a power system

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2005 IEEE Power engineering Society General Meeting

作者： Li, Xiaomeng Venayagamoorthy, Ganesh K. IEEE Real-Time Power and Intelligent Systems Laboratory University of Missouri Rolla MO 65409 United States Department of Electrical and Computer Engineering University of Missouri Rolla United States South African Institute of Electrical Engineers IEEE St. Louis Computational Intelligence Department of Task Force on Intelligent Control Systems Secretary of the Intelligent Systems Subcommittee IEEE Power Engineering Society

ISBN: (纸本)078039156X

With the deregulation of power industry, many tie lines between control areas are driven to operate near their maximum capacity, especially those serving heavy load centers. Wide area controllers (WACs) using wide-area or global signals can provide remote auxiliary control signals to local controllers such as automatic voltage regulators, power system stabilizers, etc to damp out inter-area oscillations. The power system is highly nonlinear system with fast changing dynamics. In order to have an efficient WAC, an online system monitor/predictor is required to provide inter-area information to the WAC from time to time. This paper presents the design of an online Wide Area Monitor (WAM) using a neural network called the Wide Area Neuroidentifier (WANI). The WANI is used to predict ahead the speed deviations of generators in the different areas using phasor measurement unit (PMU). Results are presented to show the effectiveness of the wide area monitor for different disturbances. © 2005 IEEE.

关键词： Wide area networks

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A novel method for predicting harmonic current injection from non-linear loads using neural networks

A novel method for predicting harmonic current injection fro...

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Annual Conference of Industrial Electronics Society

作者： J. Mazumdar R. Harley F. Lambert G.K. Venayagamoorthy School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla Rolla MA USA

Generation of harmonics and the existence of waveform pollution in power system networks is one of the major problems facing the utilities. This paper proposes a neural network solution methodology for the problem of measuring the actual amount of harmonic current injected into a power network by a nonlinear load. The determination of harmonic currents is complicated by the fact that the supply voltage waveform is distorted by other loads and is rarely a pure sinusoid. A recurrent neural network trained with the backpropagation through time (BPTT) training algorithm is used to find a way of distinguishing between the load harmonics and supply harmonics, without disconnecting the load from the network. The biggest advantage of this method is that only waveforms of voltages and currents have to be measured. This method is applicable for both single and three phase loads. This technology could be fabricated into a commercial instrument that could be installed in substations of large customer loads, or used as a hand-held clip on instrument

关键词： Neural networks Power system harmonics Current measurement Pollution measurement Voltage Instruments Power generation Power system measurements Distortion measurement Power measurement

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Using a Neural Network to Distinguish Between the Contributions to Harmonic Pollution of Non-Linear Loads and the Rest of the Power System

Using a Neural Network to Distinguish Between the Contributi...

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Annual IEEE Conference on Power Electronics Specialists (PESC)

Harmonics are one of the important power quality measurable quantities. This paper proposes a neural network solution methodology for the problem of measuring the actual amount of harmonic current injected into a power network by a non-linear load. The determination of harmonic currents is complicated by the fact that the supply voltage waveform is distorted by other loads and is rarely a pure sinusoid. Harmonics may therefore be classified as contributions from the load on the one hand and contributions from the power system or supply harmonics on the other hand. A recurrent neural network architecture based method is used to find a way of distinguishing between the load contributed harmonics and supply harmonics, without disconnecting the load from the network. The main advantage of this method is that only waveforms of voltages and currents have to be measured. This method is applicable for both single and three phase loads. This could be fabricated into a commercial instrument that could be installed in substations of large customer loads, or used as a hand-held clip on instrument

关键词： Power system harmonics Neural networks Pollution measurement Distortion measurement Power measurement Current measurement Voltage Instruments Power system measurements Power quality

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Adaptive critic designs for optimal control of power systems

Adaptive critic designs for optimal control of power systems

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International Conference on Intelligent Systems Applications to Power Systems (ISAP)

作者： G.K. Venayagamoorthy R.G. Harley Real-Time Power and Intelligent Systems (RTPIS) Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla MO USA School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

The increasing complexity of the modern power grid highlights the need for advanced modeling and control techniques for effective control of excitation, turbine and flexible AC transmission systems (FACTS). The crucial factors affecting the modern power systems today is voltage and load flow control. Simulation studies in the PSCAD/EMTDC environment and realtime laboratory experimental studies carried out are described and the results show the successful control of the power system elements and the entire power system with adaptive and optimal neurocontrol schemes. Performances of the neurocontrollers are compared with the conventional PI controllers for damping under different operating conditions for small and large disturbances

关键词： Programmable control Adaptive control Optimal control Power system control Power systems Power system simulation PSCAD Power system modeling Flexible AC transmission systems Power grids

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Hardware implementation of a Mamdani fuzzy logic controller for a static compensator in a multimachine power system

Hardware implementation of a Mamdani fuzzy logic controller ...

引用

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

作者： S. Mohagheghi G.K. Venayagamoorthy S. Rajagopalan R.G. Harley School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla Rolla MO USA

A Mamdani based fuzzy logic controller is designed and implemented for controlling a STATCOM, which is connected to a 10 bus multimachine power system. Such a controller does not need any prior knowledge of the plant to be controlled and can efficiently provide control signals for the STATCOM during different disturbances in the network The proposed controller is implemented using the M67 DSP board and is interfaced to the multimachine power system simulated on a real-time digital simulator (RTDS). Experimental results are provided, showing that the proposed controller provides more effective damping than the conventional PI controller in a typical large scale disturbance, i.e., a three phase short circuit.

关键词： Hardware Fuzzy logic Control systems STATCOM Power systems Power system simulation Automatic voltage control Circuit simulation Control system synthesis Digital signal processing

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Optimal dynamic neurocontrol of a gate-controlled series capacitor in a multi-machine power system

Optimal dynamic neurocontrol of a gate-controlled series cap...

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International Conference on Intelligent Systems Applications to Power Systems (ISAP)

作者： S. Ray G.K. Venayagamoorthy E.H. Watanabe F.D. de Jesus Real-Time Power and Intelligent Systems (RTPIS) Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla MO USA Department of Electrical Engineering Federal University of Rio Grande do Sul Brazil

This paper presents the design of an optimal dynamic neurocontroller for a new type of FACTS device - the gate controlled series capacitor (GCSC) incorporated in a multi-machine power system. The optimal neurocontroller is developed based on the heuristic dynamic programming (HDP) approach. In addition, a dynamic identifier/model and controller structure using the recurrent neural network trained with backpropagation through time (BPTT) is employed. Simulation results are presented to show the effectiveness of the dynamic neurocontroller and its performance is compared with that of the conventional PI controller under small and large disturbances

关键词： Power system dynamics Capacitors Power systems Neurocontrollers Power system modeling Power system simulation Optimal control Control systems Dynamic programming Recurrent neural networks

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Random beamforming in MIMO systems exploiting efficient multiuser diversity

Random beamforming in MIMO systems exploiting efficient mult...

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IEEE Conference on Vehicular Technology (VTC)

作者： Eun Yong Kim Joohwan Chun Scientific Computing Laboratory Department of Electrical Engineering and Computer Science Korea Advanced Institute of Science and Technology Daejeon South Korea

We propose a new random beamforming technique in forward link multiuser MIMO systems that exploits efficient multiuser diversity and spatial multiplexing gain. The common channels are allocated to more than one user simultaneously and multiple antennas at the receivers are used for interference nulling, resulting in higher throughput and reduced average waiting time. We show that the throughput of the proposed scheme outperforms the upper bound of the conventional scheme.

关键词： MIMO Array signal processing Diversity reception Base stations Receiving antennas Throughput Mobile antennas Interference Fading Transmitters

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Two-level preconditioning techniques for electromagnetic wave scattering problems

Two-level preconditioning techniques for electromagnetic wav...

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2005 IEEE/ACES International Conference on Wireless Communications and Applied Computational Electromagnetics

作者： Lee, Jeonghwa Zhang, Jun Lu, Cai-Cheng RAPT Industries Inc. 183 Northpointe Blvd. Freeport PA 16229 United States Laboratory for High Performance Scientific Computing and Computer Simulation Department of Computer Science University of Kentucky Lexington KY 40506 United States Department of Electrical and Computer Engineering University of Kentucky Lexington KY40506 United States

ISBN: (纸本)0780390687

We investigate preconditioned iterative solutions of large dense complex valued matrices arising from discretizing the hybrid integral equations of electromagnetic scattering. Due to the specific data pattern, we propose to construct two-level (TL) preconditioners to treat the conducting and dielectric parts more efficiently. The main purpose of this study is to show that the TL preconditioning is effective with the MLFMA and can reduce the number of Krylov iterations substantially by using subdivided domain of the near part matrix for each level. Our experimental results indicate that the TL preconditioning maintains the computational complexity of the MLFMA, but converges much faster, thus effectively reduces the overall simulation time for the coated material objects. © 2005 ACES.

关键词： Iterative methods

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