Solving load flow solution using evolutionary programming method / Nurul-Huda Ismail

The load flow problem can be expressed a set of nonlinear simultaneous algebraic equations, then it is impossible to have multiple solutions. To overcome the limitations of conventional load flow problem, a genetic based load flow optimisation is developed. This report presents an alternative method...

Full description

Saved in:
Bibliographic Details
Main Author: Ismail, Nurul-Huda
Format: Thesis
Language:English
Published: 2003
Subjects:
Online Access:https://ir.uitm.edu.my/id/eprint/78085/1/78085.pdf
https://ir.uitm.edu.my/id/eprint/78085/
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Universiti Teknologi Mara
Language: English
id my.uitm.ir.78085
record_format eprints
spelling my.uitm.ir.780852023-07-20T02:20:10Z https://ir.uitm.edu.my/id/eprint/78085/ Solving load flow solution using evolutionary programming method / Nurul-Huda Ismail Ismail, Nurul-Huda Evolutionary programming (Computer science). Genetic algorithms The load flow problem can be expressed a set of nonlinear simultaneous algebraic equations, then it is impossible to have multiple solutions. To overcome the limitations of conventional load flow problem, a genetic based load flow optimisation is developed. This report presents an alternative method for solving the load flow using the evolutionary programming (EP) method. The principal information obtained from a power-flow study is the magnitude and phase angle of the voltage at each bus and the real and reactive power flowing in each line. The EP developed uses the total active and reactive power mismatches as the objective functions for the load flow solution. It is found that the result from the EP are closed to these obtained from the traditional method. 2003 Thesis NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/78085/1/78085.pdf Solving load flow solution using evolutionary programming method / Nurul-Huda Ismail. (2003) Degree thesis, thesis, Universiti Teknologi MARA (UiTM).
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Evolutionary programming (Computer science). Genetic algorithms
spellingShingle Evolutionary programming (Computer science). Genetic algorithms
Ismail, Nurul-Huda
Solving load flow solution using evolutionary programming method / Nurul-Huda Ismail
description The load flow problem can be expressed a set of nonlinear simultaneous algebraic equations, then it is impossible to have multiple solutions. To overcome the limitations of conventional load flow problem, a genetic based load flow optimisation is developed. This report presents an alternative method for solving the load flow using the evolutionary programming (EP) method. The principal information obtained from a power-flow study is the magnitude and phase angle of the voltage at each bus and the real and reactive power flowing in each line. The EP developed uses the total active and reactive power mismatches as the objective functions for the load flow solution. It is found that the result from the EP are closed to these obtained from the traditional method.
format Thesis
author Ismail, Nurul-Huda
author_facet Ismail, Nurul-Huda
author_sort Ismail, Nurul-Huda
title Solving load flow solution using evolutionary programming method / Nurul-Huda Ismail
title_short Solving load flow solution using evolutionary programming method / Nurul-Huda Ismail
title_full Solving load flow solution using evolutionary programming method / Nurul-Huda Ismail
title_fullStr Solving load flow solution using evolutionary programming method / Nurul-Huda Ismail
title_full_unstemmed Solving load flow solution using evolutionary programming method / Nurul-Huda Ismail
title_sort solving load flow solution using evolutionary programming method / nurul-huda ismail
publishDate 2003
url https://ir.uitm.edu.my/id/eprint/78085/1/78085.pdf
https://ir.uitm.edu.my/id/eprint/78085/
_version_ 1772815549980475392