Cover Image

A Particle Swarm Optimization Trained Feedforward Neural Network for Under-Voltage Load Shedding

This paper suggests an under-voltage load shedding (UVLS) approach to avoid voltage collapse in stressed distribution systems. Prior to a blackout, a failing system reaches an emergency state, and UVLS is executed as the final option to prevent voltage collapse. Hence, this article introduces a...

Full description

Saved in:
Bibliographic Details
Main Authors: Sharman Sundarajoo, Sharman Sundarajoo, Dur Muhammad Soomro, Dur Muhammad Soomro
Format: Article
Language:English
Published: 2023
Subjects:
T Technology (General)
Online Access:http://eprints.uthm.edu.my/11528/1/J16050_015ea7b1ef862d03660635135064b672.pdf
http://eprints.uthm.edu.my/11528/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.uthm.eprints.11528
record_format eprints
spelling my.uthm.eprints.115282024-08-15T02:00:24Z http://eprints.uthm.edu.my/11528/ A Particle Swarm Optimization Trained Feedforward Neural Network for Under-Voltage Load Shedding Sharman Sundarajoo, Sharman Sundarajoo Dur Muhammad Soomro, Dur Muhammad Soomro T Technology (General) This paper suggests an under-voltage load shedding (UVLS) approach to avoid voltage collapse in stressed distribution systems. Prior to a blackout, a failing system reaches an emergency state, and UVLS is executed as the final option to prevent voltage collapse. Hence, this article introduces an optimal UVLS method using a feedforward artificial neural network (ANN) model trained with the particle swarm optimization (PSO) algorithm to obtain the optimal load shedding amount for a distribution system. PSO is used to obtain the best topology and optimum initial weights of the ANN model to enhance the precision of the ANN model. Thus, the dispute between the optimum fitting regression of the allocation of ANN nodes and computational time was disclosed, while the MSE of the ANN model was minimized. Moreover, the proposed method uses the stability index (SI) to identify the weak buses in the system following an emergency state. Different overload scenarios are examined on the IEEE 33-bus distribution network to validate the efficacy of the suggested UVLS scheme. A comparative study is performed to further assess the performance of the proposed technique. The comparison indicates that the recommended method is effective in terms of voltage stability and remaining load. 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/11528/1/J16050_015ea7b1ef862d03660635135064b672.pdf Sharman Sundarajoo, Sharman Sundarajoo and Dur Muhammad Soomro, Dur Muhammad Soomro (2023) A Particle Swarm Optimization Trained Feedforward Neural Network for Under-Voltage Load Shedding. A PARTICLE SWARM OPTIMIZATION TRAINED FEEDFORWARD NEURAL NETWORK FOR UNDER-VOLTAGE LOAD SHEDDING. pp. 1-16.
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Sharman Sundarajoo, Sharman Sundarajoo
Dur Muhammad Soomro, Dur Muhammad Soomro
A Particle Swarm Optimization Trained Feedforward Neural Network for Under-Voltage Load Shedding
description This paper suggests an under-voltage load shedding (UVLS) approach to avoid voltage collapse in stressed distribution systems. Prior to a blackout, a failing system reaches an emergency state, and UVLS is executed as the final option to prevent voltage collapse. Hence, this article introduces an optimal UVLS method using a feedforward artificial neural network (ANN) model trained with the particle swarm optimization (PSO) algorithm to obtain the optimal load shedding amount for a distribution system. PSO is used to obtain the best topology and optimum initial weights of the ANN model to enhance the precision of the ANN model. Thus, the dispute between the optimum fitting regression of the allocation of ANN nodes and computational time was disclosed, while the MSE of the ANN model was minimized. Moreover, the proposed method uses the stability index (SI) to identify the weak buses in the system following an emergency state. Different overload scenarios are examined on the IEEE 33-bus distribution network to validate the efficacy of the suggested UVLS scheme. A comparative study is performed to further assess the performance of the proposed technique. The comparison indicates that the recommended method is effective in terms of voltage stability and remaining load.
format Article
author Sharman Sundarajoo, Sharman Sundarajoo
Dur Muhammad Soomro, Dur Muhammad Soomro
author_facet Sharman Sundarajoo, Sharman Sundarajoo
Dur Muhammad Soomro, Dur Muhammad Soomro
author_sort Sharman Sundarajoo, Sharman Sundarajoo
title A Particle Swarm Optimization Trained Feedforward Neural Network for Under-Voltage Load Shedding
title_short A Particle Swarm Optimization Trained Feedforward Neural Network for Under-Voltage Load Shedding
title_full A Particle Swarm Optimization Trained Feedforward Neural Network for Under-Voltage Load Shedding
title_fullStr A Particle Swarm Optimization Trained Feedforward Neural Network for Under-Voltage Load Shedding
title_full_unstemmed A Particle Swarm Optimization Trained Feedforward Neural Network for Under-Voltage Load Shedding
title_sort particle swarm optimization trained feedforward neural network for under-voltage load shedding
publishDate 2023
url http://eprints.uthm.edu.my/11528/1/J16050_015ea7b1ef862d03660635135064b672.pdf
http://eprints.uthm.edu.my/11528/
_version_ 1809145064378597376
score 13.211869

Similar Items