CQUniversity
Browse

File(s) not publicly available

Improved ANN based tap-changer controller using modified cascade-correlation algorithm

journal contribution
posted on 2017-12-06, 00:00 authored by Md Fakhrul IslamMd 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.