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Mathematical Problems in Engineering
Volume 2017, Article ID 4030146, 12 pages
https://doi.org/10.1155/2017/4030146
Research Article

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

1School of Electrical Engineering and Information, Sichuan University, Chengdu 610065, China
2School of Electronic and Computer Engineering, Brunel University London, Uxbridge UB8 3PH, UK

Correspondence should be addressed to Youbo Liu; nc.ude.ucs@obuoyuil

Received 2 May 2016; Revised 7 August 2016; Accepted 23 November 2016; Published 8 February 2017

Academic Editor: Huaguang Zhang

Copyright © 2017 Yang Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.