Enhanced Algorithms By Combining Gauss-Seidel And Newton-Raphson In Load Flow Analysis

Traditional load flow solution methods like Newton-Raphson has a great convergence characteristics with regards to its number of iterations and computing time, but suffers from poor convergence when used to solve ill-conditioned networks or if the starting initial values are far from solution. To ov...

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Main Author: Abouhasera, Mohamed Khalid Mohamed
Format: Monograph
Language:English
Published: Universiti Sains Malaysia 2017
Subjects:
Online Access:http://eprints.usm.my/53046/
http://eprints.usm.my/53046/1/Enhanced%20Algorithms%20By%20Combining%20Gauss-Seidel%20And%20Newton-Raphson%20In%20Load%20Flow%20Analysis_Mohamed%20Khalid%20Mohamed%20Abouhasera_E3_2017.pdf
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author Abouhasera, Mohamed Khalid Mohamed
author_facet Abouhasera, Mohamed Khalid Mohamed
author_sort Abouhasera, Mohamed Khalid Mohamed
building USM Institutional Repository
collection Online Access
description Traditional load flow solution methods like Newton-Raphson has a great convergence characteristics with regards to its number of iterations and computing time, but suffers from poor convergence when used to solve ill-conditioned networks or if the starting initial values are far from solution. To overcome these concerns, we present enhanced algorithms for load flow analysis by combining Gauss-Seidel and Newton-Raphson methods that incorporate constant Jacobian to give a more dependable method with tolerable accuracy and shorter computation time.
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format Monograph
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institution Universiti Sains Malaysia
institution_category Local University
language English
last_indexed 2025-11-15T18:34:35Z
publishDate 2017
publisher Universiti Sains Malaysia
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spelling usm-530462022-06-24T08:35:20Z http://eprints.usm.my/53046/ Enhanced Algorithms By Combining Gauss-Seidel And Newton-Raphson In Load Flow Analysis Abouhasera, Mohamed Khalid Mohamed T Technology TK Electrical Engineering. Electronics. Nuclear Engineering Traditional load flow solution methods like Newton-Raphson has a great convergence characteristics with regards to its number of iterations and computing time, but suffers from poor convergence when used to solve ill-conditioned networks or if the starting initial values are far from solution. To overcome these concerns, we present enhanced algorithms for load flow analysis by combining Gauss-Seidel and Newton-Raphson methods that incorporate constant Jacobian to give a more dependable method with tolerable accuracy and shorter computation time. Universiti Sains Malaysia 2017-06-01 Monograph NonPeerReviewed application/pdf en http://eprints.usm.my/53046/1/Enhanced%20Algorithms%20By%20Combining%20Gauss-Seidel%20And%20Newton-Raphson%20In%20Load%20Flow%20Analysis_Mohamed%20Khalid%20Mohamed%20Abouhasera_E3_2017.pdf Abouhasera, Mohamed Khalid Mohamed (2017) Enhanced Algorithms By Combining Gauss-Seidel And Newton-Raphson In Load Flow Analysis. Project Report. Universiti Sains Malaysia, Pusat Pengajian Kejuruteraan Elektrik & Elektronik. (Submitted)
spellingShingle T Technology
TK Electrical Engineering. Electronics. Nuclear Engineering
Abouhasera, Mohamed Khalid Mohamed
Enhanced Algorithms By Combining Gauss-Seidel And Newton-Raphson In Load Flow Analysis
title Enhanced Algorithms By Combining Gauss-Seidel And Newton-Raphson In Load Flow Analysis
title_full Enhanced Algorithms By Combining Gauss-Seidel And Newton-Raphson In Load Flow Analysis
title_fullStr Enhanced Algorithms By Combining Gauss-Seidel And Newton-Raphson In Load Flow Analysis
title_full_unstemmed Enhanced Algorithms By Combining Gauss-Seidel And Newton-Raphson In Load Flow Analysis
title_short Enhanced Algorithms By Combining Gauss-Seidel And Newton-Raphson In Load Flow Analysis
title_sort enhanced algorithms by combining gauss-seidel and newton-raphson in load flow analysis
topic T Technology
TK Electrical Engineering. Electronics. Nuclear Engineering
url http://eprints.usm.my/53046/
http://eprints.usm.my/53046/1/Enhanced%20Algorithms%20By%20Combining%20Gauss-Seidel%20And%20Newton-Raphson%20In%20Load%20Flow%20Analysis_Mohamed%20Khalid%20Mohamed%20Abouhasera_E3_2017.pdf