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,...
Ausführliche Beschreibung
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: |
Critical unstable generator (CUG) |
---|
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: |
---|
DOI / URN: |
10.1007/s40565-016-0209-4 |
---|
Katalog-ID: |
DOAJ001821423 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | DOAJ001821423 | ||
003 | DE-627 | ||
005 | 20230309164432.0 | ||
007 | cr uuu---uuuuu | ||
008 | 230225s2016 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1007/s40565-016-0209-4 |2 doi | |
035 | |a (DE-627)DOAJ001821423 | ||
035 | |a (DE-599)DOAJ15413f0d687a44b5863d5bdb4ea36b59 | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
050 | 0 | |a TK1001-1841 | |
050 | 0 | |a TJ807-830 | |
100 | 0 | |a Youbo Liu |e verfasserin |4 aut | |
245 | 1 | 0 | |a High-performance predictor for critical unstable generators based on scalable parallelized neural networks |
264 | 1 | |c 2016 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
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. | ||
650 | 4 | |a Transient stability | |
650 | 4 | |a Critical unstable generator (CUG) | |
650 | 4 | |a High-performance computing (HPC) | |
650 | 4 | |a MapReduce based parallel BPNN | |
650 | 4 | |a Hadoop | |
653 | 0 | |a Production of electric energy or power. Powerplants. Central stations | |
653 | 0 | |a Renewable energy sources | |
700 | 0 | |a Yang Liu |e verfasserin |4 aut | |
700 | 0 | |a Junyong Liu |e verfasserin |4 aut | |
700 | 0 | |a Maozhen Li |e verfasserin |4 aut | |
700 | 0 | |a Zhibo Ma |e verfasserin |4 aut | |
700 | 0 | |a Gareth Taylor |e verfasserin |4 aut | |
773 | 0 | 8 | |i In |t Journal of Modern Power Systems and Clean Energy |d IEEE, 2016 |g 4(2016), 3, Seite 414-426 |w (DE-627)75682821X |w (DE-600)2727912-1 |x 21965420 |7 nnns |
773 | 1 | 8 | |g volume:4 |g year:2016 |g number:3 |g pages:414-426 |
856 | 4 | 0 | |u https://doi.org/10.1007/s40565-016-0209-4 |z kostenfrei |
856 | 4 | 0 | |u https://doaj.org/article/15413f0d687a44b5863d5bdb4ea36b59 |z kostenfrei |
856 | 4 | 0 | |u https://ieeexplore.ieee.org/document/8939552/ |z kostenfrei |
856 | 4 | 2 | |u https://doaj.org/toc/2196-5420 |y Journal toc |z kostenfrei |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_DOAJ | ||
912 | |a GBV_ILN_11 | ||
912 | |a GBV_ILN_20 | ||
912 | |a GBV_ILN_22 | ||
912 | |a GBV_ILN_23 | ||
912 | |a GBV_ILN_24 | ||
912 | |a GBV_ILN_31 | ||
912 | |a GBV_ILN_39 | ||
912 | |a GBV_ILN_40 | ||
912 | |a GBV_ILN_60 | ||
912 | |a GBV_ILN_62 | ||
912 | |a GBV_ILN_63 | ||
912 | |a GBV_ILN_65 | ||
912 | |a GBV_ILN_69 | ||
912 | |a GBV_ILN_70 | ||
912 | |a GBV_ILN_73 | ||
912 | |a GBV_ILN_95 | ||
912 | |a GBV_ILN_105 | ||
912 | |a GBV_ILN_110 | ||
912 | |a GBV_ILN_151 | ||
912 | |a GBV_ILN_161 | ||
912 | |a GBV_ILN_170 | ||
912 | |a GBV_ILN_213 | ||
912 | |a GBV_ILN_230 | ||
912 | |a GBV_ILN_285 | ||
912 | |a GBV_ILN_293 | ||
912 | |a GBV_ILN_370 | ||
912 | |a GBV_ILN_602 | ||
912 | |a GBV_ILN_2014 | ||
912 | |a GBV_ILN_4012 | ||
912 | |a GBV_ILN_4037 | ||
912 | |a GBV_ILN_4112 | ||
912 | |a GBV_ILN_4125 | ||
912 | |a GBV_ILN_4126 | ||
912 | |a GBV_ILN_4249 | ||
912 | |a GBV_ILN_4305 | ||
912 | |a GBV_ILN_4306 | ||
912 | |a GBV_ILN_4307 | ||
912 | |a GBV_ILN_4313 | ||
912 | |a GBV_ILN_4322 | ||
912 | |a GBV_ILN_4323 | ||
912 | |a GBV_ILN_4324 | ||
912 | |a GBV_ILN_4325 | ||
912 | |a GBV_ILN_4335 | ||
912 | |a GBV_ILN_4338 | ||
912 | |a GBV_ILN_4367 | ||
912 | |a GBV_ILN_4700 | ||
951 | |a AR | ||
952 | |d 4 |j 2016 |e 3 |h 414-426 |
author_variant |
y l yl y l yl j l jl m l ml z m zm g t gt |
---|---|
matchkey_str |
article:21965420:2016----::ihefracpeitrociiausalgnrtrbsdnclbe |
hierarchy_sort_str |
2016 |
callnumber-subject-code |
TK |
publishDate |
2016 |
allfields |
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 |
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 |
allfields_unstemmed |
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 |
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 |
isfreeaccess_bool |
true |
container_title |
Journal of Modern Power Systems and Clean Energy |
authorswithroles_txt_mv |
Youbo Liu @@aut@@ Yang Liu @@aut@@ Junyong Liu @@aut@@ Maozhen Li @@aut@@ Zhibo Ma @@aut@@ Gareth Taylor @@aut@@ |
publishDateDaySort_date |
2016-01-01T00:00:00Z |
hierarchy_top_id |
75682821X |
id |
DOAJ001821423 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ001821423</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230309164432.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230225s2016 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s40565-016-0209-4</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ001821423</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ15413f0d687a44b5863d5bdb4ea36b59</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TK1001-1841</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TJ807-830</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Youbo Liu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">High-performance predictor for critical unstable generators based on scalable parallelized neural networks</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Transient stability</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Critical unstable generator (CUG)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">High-performance computing (HPC)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">MapReduce based parallel BPNN</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hadoop</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Production of electric energy or power. Powerplants. Central stations</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Renewable energy sources</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yang Liu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Junyong Liu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Maozhen Li</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zhibo Ma</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Gareth Taylor</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Journal of Modern Power Systems and Clean Energy</subfield><subfield code="d">IEEE, 2016</subfield><subfield code="g">4(2016), 3, Seite 414-426</subfield><subfield code="w">(DE-627)75682821X</subfield><subfield code="w">(DE-600)2727912-1</subfield><subfield code="x">21965420</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:4</subfield><subfield code="g">year:2016</subfield><subfield code="g">number:3</subfield><subfield code="g">pages:414-426</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1007/s40565-016-0209-4</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/15413f0d687a44b5863d5bdb4ea36b59</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://ieeexplore.ieee.org/document/8939552/</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2196-5420</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">4</subfield><subfield code="j">2016</subfield><subfield code="e">3</subfield><subfield code="h">414-426</subfield></datafield></record></collection>
|
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 |
authorStr |
Youbo Liu |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)75682821X |
format |
electronic Article |
delete_txt_mv |
keep |
author_role |
aut aut aut aut aut aut |
collection |
DOAJ |
remote_str |
true |
callnumber-label |
TK1001-1841 |
illustrated |
Not Illustrated |
issn |
21965420 |
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 |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
cr |
hierarchy_parent_title |
Journal of Modern Power Systems and Clean Energy |
hierarchy_parent_id |
75682821X |
hierarchy_top_title |
Journal of Modern Power Systems and Clean Energy |
isfreeaccess_txt |
true |
familylinks_str_mv |
(DE-627)75682821X (DE-600)2727912-1 |
title |
High-performance predictor for critical unstable generators based on scalable parallelized neural networks |
ctrlnum |
(DE-627)DOAJ001821423 (DE-599)DOAJ15413f0d687a44b5863d5bdb4ea36b59 |
title_full |
High-performance predictor for critical unstable generators based on scalable parallelized neural networks |
author_sort |
Youbo Liu |
journal |
Journal of Modern Power Systems and Clean Energy |
journalStr |
Journal of Modern Power Systems and Clean Energy |
callnumber-first-code |
T |
lang_code |
eng |
isOA_bool |
true |
recordtype |
marc |
publishDateSort |
2016 |
contenttype_str_mv |
txt |
container_start_page |
414 |
author_browse |
Youbo Liu Yang Liu Junyong Liu Maozhen Li Zhibo Ma Gareth Taylor |
container_volume |
4 |
class |
TK1001-1841 TJ807-830 |
format_se |
Elektronische Aufsätze |
author-letter |
Youbo Liu |
doi_str_mv |
10.1007/s40565-016-0209-4 |
author2-role |
verfasserin |
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. |
collection_details |
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 |
container_issue |
3 |
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 |
remote_bool |
true |
author2 |
Yang Liu Junyong Liu Maozhen Li Zhibo Ma Gareth Taylor |
author2Str |
Yang Liu Junyong Liu Maozhen Li Zhibo Ma Gareth Taylor |
ppnlink |
75682821X |
callnumber-subject |
TK - Electrical and Nuclear Engineering |
mediatype_str_mv |
c |
isOA_txt |
true |
hochschulschrift_bool |
false |
doi_str |
10.1007/s40565-016-0209-4 |
callnumber-a |
TK1001-1841 |
up_date |
2024-07-03T22:34:23.146Z |
_version_ |
1803599020714622976 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ001821423</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230309164432.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230225s2016 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s40565-016-0209-4</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ001821423</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ15413f0d687a44b5863d5bdb4ea36b59</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TK1001-1841</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TJ807-830</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Youbo Liu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">High-performance predictor for critical unstable generators based on scalable parallelized neural networks</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Transient stability</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Critical unstable generator (CUG)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">High-performance computing (HPC)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">MapReduce based parallel BPNN</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hadoop</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Production of electric energy or power. Powerplants. Central stations</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Renewable energy sources</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yang Liu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Junyong Liu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Maozhen Li</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zhibo Ma</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Gareth Taylor</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Journal of Modern Power Systems and Clean Energy</subfield><subfield code="d">IEEE, 2016</subfield><subfield code="g">4(2016), 3, Seite 414-426</subfield><subfield code="w">(DE-627)75682821X</subfield><subfield code="w">(DE-600)2727912-1</subfield><subfield code="x">21965420</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:4</subfield><subfield code="g">year:2016</subfield><subfield code="g">number:3</subfield><subfield code="g">pages:414-426</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1007/s40565-016-0209-4</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/15413f0d687a44b5863d5bdb4ea36b59</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://ieeexplore.ieee.org/document/8939552/</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2196-5420</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">4</subfield><subfield code="j">2016</subfield><subfield code="e">3</subfield><subfield code="h">414-426</subfield></datafield></record></collection>
|
score |
7.3989916 |