High-performance predictor for critical unstable generators based on scalable parallelized neural networks

A high-performance predictor for critical unstable generators (CUGs) of power systems is presented in this paper. The predictor is driven by the MapReduce based parallelized neural networks. Specifically, a group of back propagation neural networks (BPNNs), fed by massive response trajectories data,...
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Autor*in:	Youbo Liu [verfasserIn] Yang Liu [verfasserIn] Junyong Liu [verfasserIn] Maozhen Li [verfasserIn] Zhibo Ma [verfasserIn] Gareth Taylor [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	Transient stability Critical unstable generator (CUG) High-performance computing (HPC) MapReduce based parallel BPNN Hadoop

Übergeordnetes Werk:	In: Journal of Modern Power Systems and Clean Energy - IEEE, 2016, 4(2016), 3, Seite 414-426
Übergeordnetes Werk:	volume:4 ; year:2016 ; number:3 ; pages:414-426

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1007/s40565-016-0209-4

Katalog-ID:	DOAJ001821423

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520			\|a A high-performance predictor for critical unstable generators (CUGs) of power systems is presented in this paper. The predictor is driven by the MapReduce based parallelized neural networks. Specifically, a group of back propagation neural networks (BPNNs), fed by massive response trajectories data, are efficiently organized and concurrently trained in Hadoop to identify dynamic behavior of individual generator. Rather than simply classifying global stability of power systems, the presented approach is able to distinguish unstable generators accurately with a few cycles of synchronized trajectories after fault clearing, enabling more in-depth emergency awareness based on wide-area implementation. In addition, the technique is of rich scalability due to Hadoop framework, which can be deployed in the control centers as a high-performance computing infrastructure for real-time instability alert. Numerical examples are studied using NPCC 48-machines test system and a realistic power system of China.
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spelling	10.1007/s40565-016-0209-4 doi (DE-627)DOAJ001821423 (DE-599)DOAJ15413f0d687a44b5863d5bdb4ea36b59 DE-627 ger DE-627 rakwb eng TK1001-1841 TJ807-830 Youbo Liu verfasserin aut High-performance predictor for critical unstable generators based on scalable parallelized neural networks 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A high-performance predictor for critical unstable generators (CUGs) of power systems is presented in this paper. The predictor is driven by the MapReduce based parallelized neural networks. Specifically, a group of back propagation neural networks (BPNNs), fed by massive response trajectories data, are efficiently organized and concurrently trained in Hadoop to identify dynamic behavior of individual generator. Rather than simply classifying global stability of power systems, the presented approach is able to distinguish unstable generators accurately with a few cycles of synchronized trajectories after fault clearing, enabling more in-depth emergency awareness based on wide-area implementation. In addition, the technique is of rich scalability due to Hadoop framework, which can be deployed in the control centers as a high-performance computing infrastructure for real-time instability alert. Numerical examples are studied using NPCC 48-machines test system and a realistic power system of China. Transient stability Critical unstable generator (CUG) High-performance computing (HPC) MapReduce based parallel BPNN Hadoop Production of electric energy or power. Powerplants. Central stations Renewable energy sources Yang Liu verfasserin aut Junyong Liu verfasserin aut Maozhen Li verfasserin aut Zhibo Ma verfasserin aut Gareth Taylor verfasserin aut In Journal of Modern Power Systems and Clean Energy IEEE, 2016 4(2016), 3, Seite 414-426 (DE-627)75682821X (DE-600)2727912-1 21965420 nnns volume:4 year:2016 number:3 pages:414-426 https://doi.org/10.1007/s40565-016-0209-4 kostenfrei https://doaj.org/article/15413f0d687a44b5863d5bdb4ea36b59 kostenfrei https://ieeexplore.ieee.org/document/8939552/ kostenfrei https://doaj.org/toc/2196-5420 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2016 3 414-426
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allfieldsGer	10.1007/s40565-016-0209-4 doi (DE-627)DOAJ001821423 (DE-599)DOAJ15413f0d687a44b5863d5bdb4ea36b59 DE-627 ger DE-627 rakwb eng TK1001-1841 TJ807-830 Youbo Liu verfasserin aut High-performance predictor for critical unstable generators based on scalable parallelized neural networks 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A high-performance predictor for critical unstable generators (CUGs) of power systems is presented in this paper. The predictor is driven by the MapReduce based parallelized neural networks. Specifically, a group of back propagation neural networks (BPNNs), fed by massive response trajectories data, are efficiently organized and concurrently trained in Hadoop to identify dynamic behavior of individual generator. Rather than simply classifying global stability of power systems, the presented approach is able to distinguish unstable generators accurately with a few cycles of synchronized trajectories after fault clearing, enabling more in-depth emergency awareness based on wide-area implementation. In addition, the technique is of rich scalability due to Hadoop framework, which can be deployed in the control centers as a high-performance computing infrastructure for real-time instability alert. Numerical examples are studied using NPCC 48-machines test system and a realistic power system of China. Transient stability Critical unstable generator (CUG) High-performance computing (HPC) MapReduce based parallel BPNN Hadoop Production of electric energy or power. Powerplants. Central stations Renewable energy sources Yang Liu verfasserin aut Junyong Liu verfasserin aut Maozhen Li verfasserin aut Zhibo Ma verfasserin aut Gareth Taylor verfasserin aut In Journal of Modern Power Systems and Clean Energy IEEE, 2016 4(2016), 3, Seite 414-426 (DE-627)75682821X (DE-600)2727912-1 21965420 nnns volume:4 year:2016 number:3 pages:414-426 https://doi.org/10.1007/s40565-016-0209-4 kostenfrei https://doaj.org/article/15413f0d687a44b5863d5bdb4ea36b59 kostenfrei https://ieeexplore.ieee.org/document/8939552/ kostenfrei https://doaj.org/toc/2196-5420 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2016 3 414-426
allfieldsSound	10.1007/s40565-016-0209-4 doi (DE-627)DOAJ001821423 (DE-599)DOAJ15413f0d687a44b5863d5bdb4ea36b59 DE-627 ger DE-627 rakwb eng TK1001-1841 TJ807-830 Youbo Liu verfasserin aut High-performance predictor for critical unstable generators based on scalable parallelized neural networks 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A high-performance predictor for critical unstable generators (CUGs) of power systems is presented in this paper. The predictor is driven by the MapReduce based parallelized neural networks. Specifically, a group of back propagation neural networks (BPNNs), fed by massive response trajectories data, are efficiently organized and concurrently trained in Hadoop to identify dynamic behavior of individual generator. Rather than simply classifying global stability of power systems, the presented approach is able to distinguish unstable generators accurately with a few cycles of synchronized trajectories after fault clearing, enabling more in-depth emergency awareness based on wide-area implementation. In addition, the technique is of rich scalability due to Hadoop framework, which can be deployed in the control centers as a high-performance computing infrastructure for real-time instability alert. Numerical examples are studied using NPCC 48-machines test system and a realistic power system of China. Transient stability Critical unstable generator (CUG) High-performance computing (HPC) MapReduce based parallel BPNN Hadoop Production of electric energy or power. Powerplants. Central stations Renewable energy sources Yang Liu verfasserin aut Junyong Liu verfasserin aut Maozhen Li verfasserin aut Zhibo Ma verfasserin aut Gareth Taylor verfasserin aut In Journal of Modern Power Systems and Clean Energy IEEE, 2016 4(2016), 3, Seite 414-426 (DE-627)75682821X (DE-600)2727912-1 21965420 nnns volume:4 year:2016 number:3 pages:414-426 https://doi.org/10.1007/s40565-016-0209-4 kostenfrei https://doaj.org/article/15413f0d687a44b5863d5bdb4ea36b59 kostenfrei https://ieeexplore.ieee.org/document/8939552/ kostenfrei https://doaj.org/toc/2196-5420 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2016 3 414-426
language	English
source	In Journal of Modern Power Systems and Clean Energy 4(2016), 3, Seite 414-426 volume:4 year:2016 number:3 pages:414-426
sourceStr	In Journal of Modern Power Systems and Clean Energy 4(2016), 3, Seite 414-426 volume:4 year:2016 number:3 pages:414-426
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Transient stability Critical unstable generator (CUG) High-performance computing (HPC) MapReduce based parallel BPNN Hadoop Production of electric energy or power. Powerplants. Central stations Renewable energy sources
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callnumber-first	T - Technology
author	Youbo Liu
spellingShingle	Youbo Liu misc TK1001-1841 misc TJ807-830 misc Transient stability misc Critical unstable generator (CUG) misc High-performance computing (HPC) misc MapReduce based parallel BPNN misc Hadoop misc Production of electric energy or power. Powerplants. Central stations misc Renewable energy sources High-performance predictor for critical unstable generators based on scalable parallelized neural networks
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topic_title	TK1001-1841 TJ807-830 High-performance predictor for critical unstable generators based on scalable parallelized neural networks Transient stability Critical unstable generator (CUG) High-performance computing (HPC) MapReduce based parallel BPNN Hadoop
topic	misc TK1001-1841 misc TJ807-830 misc Transient stability misc Critical unstable generator (CUG) misc High-performance computing (HPC) misc MapReduce based parallel BPNN misc Hadoop misc Production of electric energy or power. Powerplants. Central stations misc Renewable energy sources
topic_unstemmed	misc TK1001-1841 misc TJ807-830 misc Transient stability misc Critical unstable generator (CUG) misc High-performance computing (HPC) misc MapReduce based parallel BPNN misc Hadoop misc Production of electric energy or power. Powerplants. Central stations misc Renewable energy sources
topic_browse	misc TK1001-1841 misc TJ807-830 misc Transient stability misc Critical unstable generator (CUG) misc High-performance computing (HPC) misc MapReduce based parallel BPNN misc Hadoop misc Production of electric energy or power. Powerplants. Central stations misc Renewable energy sources
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title	High-performance predictor for critical unstable generators based on scalable parallelized neural networks
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title_full	High-performance predictor for critical unstable generators based on scalable parallelized neural networks
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title_sort	high-performance predictor for critical unstable generators based on scalable parallelized neural networks
callnumber	TK1001-1841
title_auth	High-performance predictor for critical unstable generators based on scalable parallelized neural networks
abstract	A high-performance predictor for critical unstable generators (CUGs) of power systems is presented in this paper. The predictor is driven by the MapReduce based parallelized neural networks. Specifically, a group of back propagation neural networks (BPNNs), fed by massive response trajectories data, are efficiently organized and concurrently trained in Hadoop to identify dynamic behavior of individual generator. Rather than simply classifying global stability of power systems, the presented approach is able to distinguish unstable generators accurately with a few cycles of synchronized trajectories after fault clearing, enabling more in-depth emergency awareness based on wide-area implementation. In addition, the technique is of rich scalability due to Hadoop framework, which can be deployed in the control centers as a high-performance computing infrastructure for real-time instability alert. Numerical examples are studied using NPCC 48-machines test system and a realistic power system of China.
abstractGer	A high-performance predictor for critical unstable generators (CUGs) of power systems is presented in this paper. The predictor is driven by the MapReduce based parallelized neural networks. Specifically, a group of back propagation neural networks (BPNNs), fed by massive response trajectories data, are efficiently organized and concurrently trained in Hadoop to identify dynamic behavior of individual generator. Rather than simply classifying global stability of power systems, the presented approach is able to distinguish unstable generators accurately with a few cycles of synchronized trajectories after fault clearing, enabling more in-depth emergency awareness based on wide-area implementation. In addition, the technique is of rich scalability due to Hadoop framework, which can be deployed in the control centers as a high-performance computing infrastructure for real-time instability alert. Numerical examples are studied using NPCC 48-machines test system and a realistic power system of China.
abstract_unstemmed	A high-performance predictor for critical unstable generators (CUGs) of power systems is presented in this paper. The predictor is driven by the MapReduce based parallelized neural networks. Specifically, a group of back propagation neural networks (BPNNs), fed by massive response trajectories data, are efficiently organized and concurrently trained in Hadoop to identify dynamic behavior of individual generator. Rather than simply classifying global stability of power systems, the presented approach is able to distinguish unstable generators accurately with a few cycles of synchronized trajectories after fault clearing, enabling more in-depth emergency awareness based on wide-area implementation. In addition, the technique is of rich scalability due to Hadoop framework, which can be deployed in the control centers as a high-performance computing infrastructure for real-time instability alert. Numerical examples are studied using NPCC 48-machines test system and a realistic power system of China.
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title_short	High-performance predictor for critical unstable generators based on scalable parallelized neural networks
url	https://doi.org/10.1007/s40565-016-0209-4 https://doaj.org/article/15413f0d687a44b5863d5bdb4ea36b59 https://ieeexplore.ieee.org/document/8939552/ https://doaj.org/toc/2196-5420
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author2	Yang Liu Junyong Liu Maozhen Li Zhibo Ma Gareth Taylor
author2Str	Yang Liu Junyong Liu Maozhen Li Zhibo Ma Gareth Taylor
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doi_str	10.1007/s40565-016-0209-4
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up_date	2024-07-03T22:34:23.146Z
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