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    Application of artificial neural networks to improve power transfer capability through OLTC

    137725_20438_Application of Artificial Neural_IJEST paper.pdf (3.944Mb)
    Access Status
    Open access
    Authors
    Abu-Siada, Ahmed
    Islam, Syed
    Mohamed, E.
    Date
    2010
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Abu-Siada, A. and Islam, S. and Mohamed, E. 2010. Application of artificial neural networks to improve power transfer capability through OLTC. International Journal of Engineering, Science and Technology. 2 (3): pp. 8-18.
    Source Title
    International Journal of Engineering, Science and Technology
    ISSN
    21412820
    Faculty
    Department of Electrical and Computer Engineering
    School of Engineering
    Faculty of Science and Engineering
    URI
    http://hdl.handle.net/20.500.11937/22300
    Collection
    • Curtin Research Publications
    Abstract

    On load tap changing (OLTC) transformer has become a vital link in modern power systems. It acts to maintain the load bus voltage within its permissible limits despite any load changes. This paper discusses the effect of different static loads namely; constant power (CP), constant current (CI) and constant impedance (CZ) on the maximum power transfer limit from the generation to the load centre through the OLTC branch and in turn on the static stability margin of power systems. Then the paper introduces a novel approach for the on-line determination of the OLTC settings using artificial neural network (ANN) technique in order to improve the power transfer capability of transmission systems. The proposed approach is tested on a six-bus IEEE system. Numerical results show that the setting of OLTC transformer in terms of the load model has a major effect on the maximum power transfer in power systems and hte proposed ANN technique is very accurate and reliable. The adaptive settings of OLTC improve the power transfer capability according to the system operating condition.

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