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Implementation of artificial neural network to allocate transmission usage in bilateral trade power market

journal contribution
posted on 06.12.2017, 00:00 authored by S Khalid, M Mustafa, H Shareef, A Khairuddin, A Kalam, Amanullah Maung Than OoAmanullah Maung Than Oo
This paper proposes a method to allocate transmission usage for simultaneous bilateral transactions using artificial neural network (ANN). The basic idea is to use supervised learning paradigm to train the ANN, utilising a conventional circuit theory method as the trainer. Based on solved load flow and followed by a procedure to decouple the line usage on the basis of transaction pairs, the description of inputs and outputs of the training data for the ANN is obtained. The structure of artificial neural network is designed to assess the extent of line usage by each generator while supplying to their respective customer. Most commonly used feed forward architecture has been chosen for the proposed ANN based transmission usage allocation technique. Almost all the system variables obtained from load flow solutions are utilized as an input to the neural network. Moreover, tan-sigmoid activation functions are incorporated in the hidden layer to realize the non linear nature of the transmission usage allocation. The proposed ANN provides promising results in terms of accuracy and computation time. A 6-bus and also the modified IEEE 14-bus network is utilized as test systems to illustrate the effectiveness of the ANN output compared to that of conventional methods.

Funding

Category 2 - Other Public Sector Grants Category

History

Volume

3

Issue

2

Start Page

253

End Page

264

Number of Pages

12

ISSN

1827-6660

Location

Napoli, Italy

Publisher

Praise Worthy Prize

Language

en-aus

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Faculty of Sciences, Engineering and Health; Institute for Resource Industries and Sustainability (IRIS); University Technologi Malaysia; Victoria University (Melbourne, Vic.);

Era Eligible

Yes

Journal

International review of electrical engineering.