A high level language abstraction for reconfigurable computing

Type :

Term papers

Pages :

7 pages

Format :

.pdf

Published date :

06/11/2009

Category :

Science & technology

Sub category :

Computer science

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Summary :

Abstract
Introduction
Current systems
Where is RC important today?
Reconfigurable computing
External visible internal state
Run time environments
Three layer archtecture of run time environmemnt
Rate of reconfiguration
Tool flow
Looking forward
Refrences

Abstract

The gain of SVC depends upon the type of reactive power load for optimum performance. As the load and input wind power conditions are variable, the gain setting of SVC needs to be adjusted or tuned. In this paper, an ANN based approach has been used to tune the gain parameters of the SVC controller over a wide range of load characteristics. The multi-layer feed-forward ANN tool with the error back propagation training method is employed. Loads have been taken as the function of voltage. Analytical techniques have mostly been based on impedance load reduced network models, which suffer from several disadvantages, including inadequate load representation and lack of structural integrity. The ability of ANNs to spontaneously learn from examples, reason over inexact and fuzzy data and provide adequate and quick responses to new information not previously stored in memory has generated high performance dynamical system with unprecedented robustness. ANNs models have been developed for different hybrid power system configurations for tuning the proportional-integral controller for SVC. Keywords: Performance analysis, profile, trace, reconfigurable computing,FPGA,high level language, C, Carte, Reprogrammability, Recongigurable, hardware, Granularity, Hardware Object