IDEAS home Printed from https://ideas.repec.org/a/ids/ijmtma/v35y2021i2p155-179.html
   My bibliography  Save this article

An adaptive method for improving the dynamic performance of two area power system using CS based neural network

Author

Listed:
  • J. Jeha
  • S. Charles Raja

Abstract

An adaptive method is the cuckoo search (CS) algorithm between neural networks (NN) are burdened to enhance the steadiness of the summarised two region power scheme illustration and assessed the power producer reactance and inactivity in the transmission demonstration. The originality of the predictable system is to assess and preserve the inter region energetic constraint of radial, two apparatus shortened apparatus power system illustration with the help of transactional energetic voltage and current constraint regulator. At this time the voltage regulator alike to TCSC device is engaged to accomplish the voltage constraint and feat the bus voltage phasor information at many buses along with the voltage manage bus and the currents on the power transmission demonstration. The anticipated method is affianced with the help of the TCSC for comforting the voltage and disrespects the blooper signal in the system, the three step blunder signal is beneficial in the two region power system. Subsequently, the anticipated procedure is implemented in MATLAB/Simulink functioning platform and also the productivity performance is assessed and compared with the prevailing methods like deficient of FACTS devices, with FACTS devices, three phase blunder condition, GA together with TCSC and ABC with TCSC congruently.

Suggested Citation

  • J. Jeha & S. Charles Raja, 2021. "An adaptive method for improving the dynamic performance of two area power system using CS based neural network," International Journal of Manufacturing Technology and Management, Inderscience Enterprises Ltd, vol. 35(2), pages 155-179.
  • Handle: RePEc:ids:ijmtma:v:35:y:2021:i:2:p:155-179
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=114756
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ids:ijmtma:v:35:y:2021:i:2:p:155-179. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=21 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.