Cover Image

A new technique for maximum load margin estimation and prediction

This paper presents the application of Fast Artificial Immune System (FAIS) for maximum load margin estimation and hybrid Fast Artificial Immune Support Vector Machine (FAISVM) for maximum load margin prediction. The newly developed techniques are marked by its significant fast computation time. A n...

Full description

Saved in:
Bibliographic Details
Main Authors: Aziz N.F.A., Rahman T.K.A., Yasin Z.M., Zakaria Z.
Other Authors: 57221906825
Format: Article
Published: Asian Research Publishing Network 2023
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.uniten.dspace-22461
record_format dspace
spelling my.uniten.dspace-224612023-05-29T14:01:07Z A new technique for maximum load margin estimation and prediction Aziz N.F.A. Rahman T.K.A. Yasin Z.M. Zakaria Z. 57221906825 8922419700 57211410254 56276791800 This paper presents the application of Fast Artificial Immune System (FAIS) for maximum load margin estimation and hybrid Fast Artificial Immune Support Vector Machine (FAISVM) for maximum load margin prediction. The newly developed techniques are marked by its significant fast computation time. A new developed index, Voltage Stability Condition Indicator (VSCI) was used as the fitness function for FAIS and FAISVM in order to evaluate the stability condition of load bus in the system. In FAIS, various mechanisms techniques of AIS were investigated and intensive comparisons were made in order to obtain the best implementation of AIS for maximum load margin estimation. The mechanisms were investigated and compared on three main AIS principles; cloning, mutation and selection. In addition, FAISVM is another new hybrid technique developed for maximum load margin prediction that integrates the application of FAIS and Support Vector Machine (SVM). For validation, FAISVM was compared with Evolutionary Support Vector Machine (ESVM) that uses Evolutionary Programming (EP) as the search algorithm. Based on the results, it shows that FAISVM outperforms ESVM with a higher accuracy prediction value. Final 2023-05-29T06:01:07Z 2023-05-29T06:01:07Z 2015 Article 2-s2.0-84953410909 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84953410909&partnerID=40&md5=7bf3c59391ca80e03603f465c18e829a https://irepository.uniten.edu.my/handle/123456789/22461 10 23 17566 17572 Asian Research Publishing Network Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description This paper presents the application of Fast Artificial Immune System (FAIS) for maximum load margin estimation and hybrid Fast Artificial Immune Support Vector Machine (FAISVM) for maximum load margin prediction. The newly developed techniques are marked by its significant fast computation time. A new developed index, Voltage Stability Condition Indicator (VSCI) was used as the fitness function for FAIS and FAISVM in order to evaluate the stability condition of load bus in the system. In FAIS, various mechanisms techniques of AIS were investigated and intensive comparisons were made in order to obtain the best implementation of AIS for maximum load margin estimation. The mechanisms were investigated and compared on three main AIS principles; cloning, mutation and selection. In addition, FAISVM is another new hybrid technique developed for maximum load margin prediction that integrates the application of FAIS and Support Vector Machine (SVM). For validation, FAISVM was compared with Evolutionary Support Vector Machine (ESVM) that uses Evolutionary Programming (EP) as the search algorithm. Based on the results, it shows that FAISVM outperforms ESVM with a higher accuracy prediction value.
author2 57221906825
author_facet 57221906825
Aziz N.F.A.
Rahman T.K.A.
Yasin Z.M.
Zakaria Z.
format Article
author Aziz N.F.A.
Rahman T.K.A.
Yasin Z.M.
Zakaria Z.
spellingShingle Aziz N.F.A.
Rahman T.K.A.
Yasin Z.M.
Zakaria Z.
A new technique for maximum load margin estimation and prediction
author_sort Aziz N.F.A.
title A new technique for maximum load margin estimation and prediction
title_short A new technique for maximum load margin estimation and prediction
title_full A new technique for maximum load margin estimation and prediction
title_fullStr A new technique for maximum load margin estimation and prediction
title_full_unstemmed A new technique for maximum load margin estimation and prediction
title_sort new technique for maximum load margin estimation and prediction
publisher Asian Research Publishing Network
publishDate 2023
_version_ 1806428124252995584
score 13.211869

Similar Items