DocumentCode :
3221335
Title :
Developing neural networks library in RSCAD for real-time power system simulation
Author :
Luitel, Bipul ; Kumar Venayagamoorthy, Ganesh ; Oliveira, Gustavo
Author_Institution :
Real-Time Power & Intell. Syst. Lab., Clemson Univ., Clemson, SC, USA
fYear :
2013
fDate :
16-19 April 2013
Firstpage :
130
Lastpage :
137
Abstract :
Real-time digital simulators (RTDS) are used for real-time simulation of power systems. As the complexity of the electric power grid and associated control increases in the future, modeling and simulation of the power network as well as the control becomes essential. This requirement will be even more prominent in the context of smart grid. As enabling technology, intelligent methods of monitoring and control that utilize computational intelligence techniques are expected to be an integral part of smart grids. Therefore integration of computational intelligence based tools in power system simulation tools is an important aspect of smart grid research. Most past and current applications of neural networks in power systems are carried out offline in non-real-time platforms. In this study, neural networks libraries are developed to run on RTDS. The neural networks component is then used to predict the speed deviation of a generator in a multi-machine power system. These neural networks components can be trained in real-time and hence can be useful tool for smart grid applications.
Keywords :
neural nets; power engineering computing; power system simulation; real-time systems; smart power grids; RSCAD; RTDS; computational intelligence; electric power grid; multimachine power system; neural networks library; nonreal-time platforms; power network; real-time digital simulators; real-time power system simulation; smart grid; Artificial neural networks; Libraries; Neurons; Real-time systems; Smart grids; Neural networks; RSCAD; RTDS; Real-Time Power System Simulation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence Applications In Smart Grid (CIASG), 2013 IEEE Symposium on
Conference_Location :
Singapore
ISSN :
2326-7682
Type :
conf
DOI :
10.1109/CIASG.2013.6611509
Filename :
6611509
Link To Document :
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