Hyperparameter Analysis-based Long-Short Term Memory (LSTM) for Power Quality Disturbances Classification

Hyperparameter Analysis-based Long-Short Term Memory (LSTM) for Power Quality Disturbances Classification

Power quality disturbances are a critical issue in electrical power systems, as they can lead to more severe problems with electrical machines or equipment, resulting in significant losses. While such disturbances are rare, using binary classification to differentiate between normal and abnormal pow...

Full description

Bibliographic Details
Published in:2024 IEEE International Conference on Power and Energy, PECon 2024
Main Author: Fadzli M.F.H.M.; Shahbudin S.
Format: Conference paper
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2024
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85217379198&doi=10.1109%2fPECON62060.2024.10827412&partnerID=40&md5=ca1bb516f1ea2e09cc96fd117950055d
id 2-s2.0-85217379198
spelling 2-s2.0-85217379198
Fadzli M.F.H.M.; Shahbudin S.
Hyperparameter Analysis-based Long-Short Term Memory (LSTM) for Power Quality Disturbances Classification
2024
2024 IEEE International Conference on Power and Energy, PECon 2024


10.1109/PECON62060.2024.10827412
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85217379198&doi=10.1109%2fPECON62060.2024.10827412&partnerID=40&md5=ca1bb516f1ea2e09cc96fd117950055d
Power quality disturbances are a critical issue in electrical power systems, as they can lead to more severe problems with electrical machines or equipment, resulting in significant losses. While such disturbances are rare, using binary classification to differentiate between normal and abnormal power quality signals has proven effective in detecting disruptions in power systems. This paper proposes a binary classification approach for identifying power quality disturbances, employing the Long Short-Term Memory (LSTM) algorithm. A hyperparameter analysis was conducted to optimize performance, and the results indicated that the highest accuracy of 91.67% was achieved with a sigmoid activation function, a learning rate of 0.0001, 40 epochs, and a batch size of 128. ©2024 IEEE.
Institute of Electrical and Electronics Engineers Inc.

English
Conference paper
author Fadzli M.F.H.M.; Shahbudin S.
spellingShingle Fadzli M.F.H.M.; Shahbudin S.
Hyperparameter Analysis-based Long-Short Term Memory (LSTM) for Power Quality Disturbances Classification
author_facet Fadzli M.F.H.M.; Shahbudin S.
author_sort Fadzli M.F.H.M.; Shahbudin S.
title Hyperparameter Analysis-based Long-Short Term Memory (LSTM) for Power Quality Disturbances Classification
title_short Hyperparameter Analysis-based Long-Short Term Memory (LSTM) for Power Quality Disturbances Classification
title_full Hyperparameter Analysis-based Long-Short Term Memory (LSTM) for Power Quality Disturbances Classification
title_fullStr Hyperparameter Analysis-based Long-Short Term Memory (LSTM) for Power Quality Disturbances Classification
title_full_unstemmed Hyperparameter Analysis-based Long-Short Term Memory (LSTM) for Power Quality Disturbances Classification
title_sort Hyperparameter Analysis-based Long-Short Term Memory (LSTM) for Power Quality Disturbances Classification
publishDate 2024
container_title 2024 IEEE International Conference on Power and Energy, PECon 2024
container_volume
container_issue
doi_str_mv 10.1109/PECON62060.2024.10827412
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85217379198&doi=10.1109%2fPECON62060.2024.10827412&partnerID=40&md5=ca1bb516f1ea2e09cc96fd117950055d
description Power quality disturbances are a critical issue in electrical power systems, as they can lead to more severe problems with electrical machines or equipment, resulting in significant losses. While such disturbances are rare, using binary classification to differentiate between normal and abnormal power quality signals has proven effective in detecting disruptions in power systems. This paper proposes a binary classification approach for identifying power quality disturbances, employing the Long Short-Term Memory (LSTM) algorithm. A hyperparameter analysis was conducted to optimize performance, and the results indicated that the highest accuracy of 91.67% was achieved with a sigmoid activation function, a learning rate of 0.0001, 40 epochs, and a batch size of 128. ©2024 IEEE.
publisher Institute of Electrical and Electronics Engineers Inc.
issn
language English
format Conference paper
accesstype
record_format scopus
collection Scopus
_version_ 1825722578486951936

Similar Items