Adaptive technique for transient stability constraints optimal power flow

Venkatachalam Manjula and Ahamed Khan Mahabub Basha

International Journal of Operational Research, 2020, vol. 39, issue 1, 1-23

Abstract: This document explains about an adaptive method for optimal power flow (OPF) of the power system, which is depending on the transient constancy restraints. The adaptive method is the mixture of both Cuckoo Search (CS) algorithm and artificial neural network (ANN). The innovative anticipated adaptive method is extremely flexible in nonlinear loads, suitable for user interface and logical reasoning, and allowing controlling formats. In the predefined generator, the CS algorithm optimises the generator arrangements by the load demand. The foremost intention of the CS algorithm is to reduce the fuel cost and emission cost. The obtainable ANN method is mainly used to develop the levy flight searching activities of the CS algorithm. The levy flight parameters are generally used to meet of the requirements the ANN, which envisage the precise consequences at the testing time. The anticipated adaptive method is executed in the MATLAB/Simulink platform and the efficiency of the anticipated procedure is investigated by the comparison analysis.

Keywords: optimal power flow; CS algorithm; artificial neural networks; cost minimisation; power loss reduction; synchronous generator. (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=108833 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijores:v:39:y:2020:i:1:p:1-23

Access Statistics for this article

More articles in International Journal of Operational Research from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().