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. © 2011 IEEE.
Recommended Citation
D. Molina et al., "Comparison of TDNN and RNN Performances for Neuro-identification on Small to Medium-sized Power Systems," IEEE SSCI 2011 - Symposium Series on Computational Intelligence - CIASG 2011: 2011 IEEE Symposium on Computational Intelligence Applications in Smart Grid, pp. 109 - 116, article no. 5953344, Institute of Electrical and Electronics Engineers, Aug 2011.
The definitive version is available at https://doi.org/10.1109/CIASG.2011.5953344
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
artificial neural networks; power system identification; recurrent neural network; time delay neural network
International Standard Book Number (ISBN)
978-142449894-9
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
17 Aug 2011
