Parallel Pattern Recognition of Leak Current Data Using Spark-KNN

Authors

Li Li, School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China;
Yongli Zhu, School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China;
Yaqi Song, School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China;

Abstract

Abstract: With the rapid development of smart grid, the status monitoring data of power grid equipment increase exponentially and gradually form the big data. Traditional computing architectures are no longer to meet the demand of computing performance. This paper explores how Spark and Cloud computing can accelerate performance of missive insulator leak current data pattern recognition. The Parallel KNN (k-Nearest Neighbor) algorithm is designed and implemented by using Spark and Aliyun E-MapReduce cloud computing platform. The results from experiments show that the performance of Spark-KNN is 2.97 times of MapReduce-KNN and gains acceleration of 8.8 times. The experimental results confirm that Spark is more suitable for real time data processing tasks than MapReduce.

Recommended Citation

Li, Li; Zhu, Yongli; and Song, Yaqi (2019) "Parallel Pattern Recognition of Leak Current Data Using Spark-KNN," Journal of System Simulation: Vol. 30: Iss. 4, Article 32.
DOI: 10.16182/j.issn1004731x.joss.201804032
Available at: https://dc-china-simulation.researchcommons.org/journal/vol30/iss4/32

First Page

1473

Revised Date

2016-07-15

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201804032

Last Page

1481

CLC

TM764

Recommended Citation

Li Li, Zhu Yongli, Song Yaqi. Parallel Pattern Recognition of Leak Current Data Using Spark-KNN[J]. Journal of System Simulation, 2018, 30(4): 1473-1481.

DOI

10.16182/j.issn1004731x.joss.201804032