Paper Info
Title
Comparison of TDNN and RNN performances for neuro-identification on small to medium-sized power systems
Abstract
For Artificial Neural Networks (ANN) to become more widely used in power systems and the future smart grids, ANN based algorithms must be capable of scaling up as they try to identify and control larger and larger parts of a power system. This paper goes through the process of scaling up an ANN based identifier as it is driven to identify increasingly larger portions of a power system. Distributed and centralized approaches for scaling up are taken and the pros and cons of each are presented. The New England/New York 68-bus power network is used as the test bed for the studies. It is shown that while a fully-connected (centralized) ANNs is capable of identification of the system with appropriate accuracy, the increase in the training times required to obtain an acceptable set of weights becomes prohibitive as the system size is increased.
Year
DOI
Venue
2011
10.1109/CIASG.2011.5953344
Computational Intelligence Applications In Smart Grid
Keywords
Field
DocType
delays,power engineering computing,power system identification,recurrent neural nets,smart power grids,ANN,New England-New York 68-bus power network,RNN performance,TDNN performance,artificial neural network,neuroidentification,power system,recurrent neural network performance,scaling up centralized approach,scaling up distributed approach,smart grid,time-delay neural network performance,artificial neural networks,power system identification,recurrent neural network,time delay neural network
Identifier,Smart grid,Recurrent neural network,Power network,Electric power system,Control engineering,Time delay neural network,Engineering,Artificial neural network,Computer engineering,Scaling
Conference
ISBN
Citations 
PageRank 
978-1-4244-9893-2
2
0.51
References 
Authors
6
4
Name
Order
Citations
PageRank
Diogenes Molina120.84
Jiaqi Liang2645.85
Ronald G. Harley320.51
Ganesh K. Venayagamoorthy42297200.90