Extracting Most Discriminative Features on Transient Multivariate Time Series by Bi-Mode Hybrid Feature Selection Scheme for Transient Stability Prediction

Real-time transient stability assessment (TSA) of power systems based on mining system dynamic response has been widely considered by scholars. In this regard, extracting the most discriminative transient features (MDTFs) to achieve high-performance transient stability prediction (TSP) should be reg...

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Autor principal:	Seyed Alireza Bashiri Mosavi
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Fuzzy imperialist competitive algorithm (FICA) most discriminative transient features (MDTFs) support vector machine (SVM) transient stability assessment (TSA) Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/b685491dca1a4766b3e462f8f7551787
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spelling	oai:doaj.org-article:b685491dca1a4766b3e462f8f75517872021-12-03T00:00:19ZExtracting Most Discriminative Features on Transient Multivariate Time Series by Bi-Mode Hybrid Feature Selection Scheme for Transient Stability Prediction2169-353610.1109/ACCESS.2021.3108393https://doaj.org/article/b685491dca1a4766b3e462f8f75517872021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9524605/https://doaj.org/toc/2169-3536Real-time transient stability assessment (TSA) of power systems based on mining system dynamic response has been widely considered by scholars. In this regard, extracting the most discriminative transient features (MDTFs) to achieve high-performance transient stability prediction (TSP) should be regarded as a fundamental issue in the transient learning strategy. In fact, MDTFs extraction is raised to make a trade-off between paradoxically intertwined indices, namely the accuracy and processing time of TSP. To this end, we offer a bi-mode hybrid feature selection scheme called BMHFSS for extracting MDTFs in high dimensional transient multivariate time series (TMTS). First, we used the TMTS, which are effective features on TSA. Next, the trajectory-based filter-wrapper mode (TFWM) is applied on TMTS to surmount the curse of dimensionality in two phases. In the filter phase, statistical and intrinsic characteristics of the TMTS in the form of agglomerative hierarchical clustering (AHC) are measured, and relevant TMTS (RTMTS) is selected according to obtained weight. In the wrapper phase, the RTMTS is entered into the trihedral kernel-based approach, including both fuzzy imperialist competitive algorithm (FICA) and incremental wrapper subset selection (IWSS) to find the intersected most RTMTS (IMRTMTS). As a complementary step, the filter-wrapper scenario in point-based mode (PFWM) is conducted for selecting MDTFs per time series in IMRTMTS. Finally, the aggregated MDTFs (AMDTFs) are tested to verify their efficacy for TSP based on cross-validation. The results show that the proposed framework has prediction accuracy greater than 98 % and a processing time of 52.94 milliseconds for TSA.Seyed Alireza Bashiri MosaviIEEEarticleFuzzy imperialist competitive algorithm (FICA)most discriminative transient features (MDTFs)support vector machine (SVM)transient stability assessment (TSA)Electrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 121087-121110 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Fuzzy imperialist competitive algorithm (FICA) most discriminative transient features (MDTFs) support vector machine (SVM) transient stability assessment (TSA) Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Fuzzy imperialist competitive algorithm (FICA) most discriminative transient features (MDTFs) support vector machine (SVM) transient stability assessment (TSA) Electrical engineering. Electronics. Nuclear engineering TK1-9971 Seyed Alireza Bashiri Mosavi Extracting Most Discriminative Features on Transient Multivariate Time Series by Bi-Mode Hybrid Feature Selection Scheme for Transient Stability Prediction
description	Real-time transient stability assessment (TSA) of power systems based on mining system dynamic response has been widely considered by scholars. In this regard, extracting the most discriminative transient features (MDTFs) to achieve high-performance transient stability prediction (TSP) should be regarded as a fundamental issue in the transient learning strategy. In fact, MDTFs extraction is raised to make a trade-off between paradoxically intertwined indices, namely the accuracy and processing time of TSP. To this end, we offer a bi-mode hybrid feature selection scheme called BMHFSS for extracting MDTFs in high dimensional transient multivariate time series (TMTS). First, we used the TMTS, which are effective features on TSA. Next, the trajectory-based filter-wrapper mode (TFWM) is applied on TMTS to surmount the curse of dimensionality in two phases. In the filter phase, statistical and intrinsic characteristics of the TMTS in the form of agglomerative hierarchical clustering (AHC) are measured, and relevant TMTS (RTMTS) is selected according to obtained weight. In the wrapper phase, the RTMTS is entered into the trihedral kernel-based approach, including both fuzzy imperialist competitive algorithm (FICA) and incremental wrapper subset selection (IWSS) to find the intersected most RTMTS (IMRTMTS). As a complementary step, the filter-wrapper scenario in point-based mode (PFWM) is conducted for selecting MDTFs per time series in IMRTMTS. Finally, the aggregated MDTFs (AMDTFs) are tested to verify their efficacy for TSP based on cross-validation. The results show that the proposed framework has prediction accuracy greater than 98 % and a processing time of 52.94 milliseconds for TSA.
format	article
author	Seyed Alireza Bashiri Mosavi
author_facet	Seyed Alireza Bashiri Mosavi
author_sort	Seyed Alireza Bashiri Mosavi
title	Extracting Most Discriminative Features on Transient Multivariate Time Series by Bi-Mode Hybrid Feature Selection Scheme for Transient Stability Prediction
title_short	Extracting Most Discriminative Features on Transient Multivariate Time Series by Bi-Mode Hybrid Feature Selection Scheme for Transient Stability Prediction
title_full	Extracting Most Discriminative Features on Transient Multivariate Time Series by Bi-Mode Hybrid Feature Selection Scheme for Transient Stability Prediction
title_fullStr	Extracting Most Discriminative Features on Transient Multivariate Time Series by Bi-Mode Hybrid Feature Selection Scheme for Transient Stability Prediction
title_full_unstemmed	Extracting Most Discriminative Features on Transient Multivariate Time Series by Bi-Mode Hybrid Feature Selection Scheme for Transient Stability Prediction
title_sort	extracting most discriminative features on transient multivariate time series by bi-mode hybrid feature selection scheme for transient stability prediction
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/b685491dca1a4766b3e462f8f7551787
work_keys_str_mv	AT seyedalirezabashirimosavi extractingmostdiscriminativefeaturesontransientmultivariatetimeseriesbybimodehybridfeatureselectionschemefortransientstabilityprediction
_version_	1718374009983205376

Extracting Most Discriminative Features on Transient Multivariate Time Series by Bi-Mode Hybrid Feature Selection Scheme for Transient Stability Prediction

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