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dc.contributor.authorLiu, Y-
dc.contributor.authorLiu, Y-
dc.contributor.authorLiu, J-
dc.contributor.authorLi, M-
dc.contributor.authorMa, Z-
dc.contributor.authorTaylor, G-
dc.identifier.citationJournal of Modern Power Systems and Clean Energy, 4 (3): pp. 414 - 426, (2016)en_US
dc.description.abstractA 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 behaviour 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.en_US
dc.format.extent414 - 426 (13)-
dc.subjectTransient stabilityen_US
dc.subjectCritical unstable generator (CUG)en_US
dc.subjectHigh-performance computing (HPC)en_US
dc.subjectMapReduce based parallel BPNNen_US
dc.titleHigh-performance predictor for critical unstable generators based on scalable parallelized neural networksen_US
dc.relation.isPartOfJournal of Modern Power Systems and Clean Energy-
Appears in Collections:Dept of Electronic and Electrical Engineering Research Papers

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