By Topic

A novel reactive power transfer allocation method with the application of artificial neural network

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

6 Author(s)
S. N. Khalid ; Faculty of Electrical Engineering, Universiti Teknologi Malaysia 81310, Skudai Johor, Malaysia ; M. W. Mustafa ; H. Shareef ; A. Khairuddin
more authors

This paper proposes a novel method to identify the reactive power transfer between generators and load using modified nodal equations. Based on the solved load flow solution and the network parameters, the method partitioned the Y-bus matrix to decompose the current of the load buses as a function of the generator's current and voltage. These decomposed currents are then used independently to obtain the decomposed load reactive power. The validation of the proposed methodology is demonstrated by using a simple 5-bus system. It further focuses on creating an appropriate artificial neural network (ANN) for actual 25-bus equivalent power system of south Malaysia to illustrate the effectiveness of the ANN output compared to that of the modified nodal equations method. The basic idea is to use supervised learning paradigm to train the ANN. Most commonly used feedforward architecture has been chosen for the proposed ANN reactive power transfer allocation technique. The descriptions of inputs and outputs of the training data for the ANN is easily obtained from the load flow results and developed reactive power transfer allocation method using modified nodal equations respectively. Almost all system variables obtained from load flow solutions are utilized as an input to the neural network. The ANN output provides promising results in terms of accuracy and computation time.

Published in:

Power Engineering Conference, 2008. AUPEC '08. Australasian Universities

Date of Conference:

14-17 Dec. 2008