The full text of this article

 

Application of soft computing neural network tools to line congestion study of electrical power systems
by Pradyumna Kumar Sahoo; Prasanta Kumar Satpathy; Srikanta Patnaik
International Journal of Information and Communication Technology (IJICT), Vol. 13, No. 2, 2018

 

Abstract: This paper presents a scheme for application of soft computing neural network tools namely feed forward neural network with backpropagation, and radial basis function neural network for the study of transmission line congestion in electrical power systems. The authors performed sequential training of the two proposed neural networks for monitoring the level of line congestion in the system. Finally, a comparative analysis is drawn between the two neural networks and it is observed that radial basis function neural network yields fastest convergence. The proposed method is tested on the IEEE 30-bus test system subject to various operating conditions.

Online publication date: Wed, 14-Mar-2018

 

is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

 
Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

 
Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Information and Communication Technology (IJICT):
Login with your Inderscience username and password:

 

    Username:        Password:         

Forgotten your password?


 
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

 
If you still need assistance, please email subs@inderscience.com