A MapReduce Based High Performance Neural Network in Enabling Fast Stability Assessment of Power Systems

Yang Liu, Youbo Liu, Junyong Liu, Maozhen Li, Tingjian Liu, Gareth Taylor and Kunyu Zuo

Mathematical Problems in Engineering, 2017, vol. 2017, 1-12

Abstract:

Transient 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.

Date: 2017
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2017/4030146.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2017/4030146.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:4030146

DOI: 10.1155/2017/4030146

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().