Improved ANN based tap-changer controller using modified cascade-correlation algorithm
journal contribution
posted on 2017-12-06, 00:00authored byMd Fakhrul Islam
Artificial Neural Network (ANN) based tap changer control of closed primary bus and cross network connected parallel transformers has demonstrated potential use in power distribution system [1– 3]. In those research works the proposed ANN for application in this control were developed using various algorithms and concluded that a network trained by Bayesian Regularization (BR) backpropagation algorithm produced the best performance measured in terms of correct tap changing decisions. However, further improvement of ANN based transformer tap changer operation is always desirable. A general rule for obtaining good generalization is to use the smallest network that solves the problem [4]. In this paper, we show that a small sized ANN is obtainable for further improvement of transformer tap changer operation by modifying the standard Cascade-Correlation algorithm. The modification incorporates weight smoothing of output layer weights in Cascade-Correlation learning using Bayesian frame work. Experimental results demonstrate that significant improvement in performance is achieved when an ANN is trained by modified Cascade-Correlation algorithm instead of standard Cascade-Correlation or Bayesian Regularization backpropagation algorithm. A comparison of performances of different algorithms in application to transformer tap changer operation is analyzed and the results are presented.
Funding
Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)
History
Volume
9
Issue
3
Start Page
100
End Page
109
Number of Pages
10
eISSN
1883-8014
ISSN
1343-0130
Location
Japan
Publisher
Fuji Technology Press Ltd.
Language
en-aus
Peer Reviewed
Yes
Open Access
No
External Author Affiliations
Monash University;
Era Eligible
Yes
Journal
Journal of advanced computational intelligence and intelligent informatics.