Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/14571
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dc.contributor.authorLiu, Y-
dc.contributor.authorLiu, J-
dc.contributor.authorLi, M-
dc.contributor.authorLiu, T-
dc.contributor.authorTaylor, G-
dc.contributor.authorZuo, K-
dc.date.accessioned2017-05-22T12:35:12Z-
dc.date.available2017-01-01-
dc.date.available2017-05-22T12:35:12Z-
dc.date.issued2016-
dc.identifier.citationMathematical Problems in Engineering, 2017, 2017en_US
dc.identifier.issn1024-123X-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/14571-
dc.description.abstractTransient stability assessment is playing a vital role in modern power systems. For this purpose, machine learning techniques have been widely employed to find critical conditions and recognize transient behaviors based on massive data analysis. However, an ever increasing volume of data generated from power systems poses a number of challenges to traditional machine learning techniques, which are computationally intensive running on standalone computers. This paper presents a MapReduce based high performance neural network to enable fast stability assessment of power systems. Hadoop, which is an open source implementation of the MapReduce model, is first employed to parallelize the neural network. The parallel neural network is further enhanced with HaLoop to reduce the computation overhead incurred in the iteration process of the neural network. In addition, ensemble techniques are employed to accommodate the accuracy loss of the parallelized neural network in classification. The parallelized neural network is evaluated with both the IEEE 68-node system and a real power system from the aspects of computation speedup and stability assessment.en_US
dc.language.isoenen_US
dc.publisherMathematical Problems in Engineeringen_US
dc.titleA MapReduce Based High Performance Neural Network in Enabling Fast Stability Assessment of Power Systemsen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1155/2017/4030146-
dc.relation.isPartOfMathematical Problems in Engineering-
pubs.publication-statusPublished-
pubs.volume2017-
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers



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