Fast Type Recognition of Missive Insulator Leakage Current Data Using Spark

Song Yaqi

doi:doi:10.11648/j.jeee.20160403.12

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Fast Type Recognition of Missive Insulator Leakage Current Data Using Spark

Song Yaqi

Published in Journal of Electrical and Electronic Engineering (Volume 4, Issue 3)

Received: 21 May 2016 Published: 24 May 2016

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Abstract

Performance is the key issue in power big data applications. One of main challenges is how to exploit these technologies in building power big data processing platform and facilitating science discoveries such as those in electric power systems. This paper explores how Spark and Cloud computing can accelerate performance of missive insulator leak current data pattern recognition. We have designed and implemented the Parallel KNN(k-NearestNeighbor) algorithm using Spark and then deployed onto the Aliyun E-MapReduce cloud computing platform. The results from experiments shows the performance and scalability can be enhanced through these advanced technologies.

Published in	Journal of Electrical and Electronic Engineering (Volume 4, Issue 3)
DOI	10.11648/j.jeee.20160403.12
Page(s)	51-56
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Insulator Leakage Current, Electric Power Big Data, Spark

References

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Song Yaqi. (2016). Fast Type Recognition of Missive Insulator Leakage Current Data Using Spark. Journal of Electrical and Electronic Engineering, 4(3), 51-56. https://doi.org/10.11648/j.jeee.20160403.12

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ACS Style

Song Yaqi. Fast Type Recognition of Missive Insulator Leakage Current Data Using Spark. J. Electr. Electron. Eng. 2016, 4(3), 51-56. doi: 10.11648/j.jeee.20160403.12

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AMA Style

Song Yaqi. Fast Type Recognition of Missive Insulator Leakage Current Data Using Spark. J Electr Electron Eng. 2016;4(3):51-56. doi: 10.11648/j.jeee.20160403.12

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@article{10.11648/j.jeee.20160403.12,
  author = {Song Yaqi},
  title = {Fast Type Recognition of Missive Insulator Leakage Current Data Using Spark},
  journal = {Journal of Electrical and Electronic Engineering},
  volume = {4},
  number = {3},
  pages = {51-56},
  doi = {10.11648/j.jeee.20160403.12},
  url = {https://doi.org/10.11648/j.jeee.20160403.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.20160403.12},
  abstract = {Performance is the key issue in power big data applications. One of main challenges is how to exploit these technologies in building power big data processing platform and facilitating science discoveries such as those in electric power systems. This paper explores how Spark and Cloud computing can accelerate performance of missive insulator leak current data pattern recognition. We have designed and implemented the Parallel KNN(k-NearestNeighbor) algorithm using Spark and then deployed onto the Aliyun E-MapReduce cloud computing platform. The results from experiments shows the performance and scalability can be enhanced through these advanced technologies.},
 year = {2016}
}

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TY  - JOUR
T1  - Fast Type Recognition of Missive Insulator Leakage Current Data Using Spark
AU  - Song Yaqi
Y1  - 2016/05/24
PY  - 2016
N1  - https://doi.org/10.11648/j.jeee.20160403.12
DO  - 10.11648/j.jeee.20160403.12
T2  - Journal of Electrical and Electronic Engineering
JF  - Journal of Electrical and Electronic Engineering
JO  - Journal of Electrical and Electronic Engineering
SP  - 51
EP  - 56
PB  - Science Publishing Group
SN  - 2329-1605
UR  - https://doi.org/10.11648/j.jeee.20160403.12
AB  - Performance is the key issue in power big data applications. One of main challenges is how to exploit these technologies in building power big data processing platform and facilitating science discoveries such as those in electric power systems. This paper explores how Spark and Cloud computing can accelerate performance of missive insulator leak current data pattern recognition. We have designed and implemented the Parallel KNN(k-NearestNeighbor) algorithm using Spark and then deployed onto the Aliyun E-MapReduce cloud computing platform. The results from experiments shows the performance and scalability can be enhanced through these advanced technologies.
VL  - 4
IS  - 3
ER  -

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Author Information

Song Yaqi

Department of Computer Science, North China Electric Power University, Baoding, China

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APA Style

Song Yaqi. (2016). Fast Type Recognition of Missive Insulator Leakage Current Data Using Spark. Journal of Electrical and Electronic Engineering, 4(3), 51-56. https://doi.org/10.11648/j.jeee.20160403.12

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ACS Style

Song Yaqi. Fast Type Recognition of Missive Insulator Leakage Current Data Using Spark. J. Electr. Electron. Eng. 2016, 4(3), 51-56. doi: 10.11648/j.jeee.20160403.12

Copy | Download

AMA Style

Song Yaqi. Fast Type Recognition of Missive Insulator Leakage Current Data Using Spark. J Electr Electron Eng. 2016;4(3):51-56. doi: 10.11648/j.jeee.20160403.12

Copy | Download

@article{10.11648/j.jeee.20160403.12,
  author = {Song Yaqi},
  title = {Fast Type Recognition of Missive Insulator Leakage Current Data Using Spark},
  journal = {Journal of Electrical and Electronic Engineering},
  volume = {4},
  number = {3},
  pages = {51-56},
  doi = {10.11648/j.jeee.20160403.12},
  url = {https://doi.org/10.11648/j.jeee.20160403.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.20160403.12},
  abstract = {Performance is the key issue in power big data applications. One of main challenges is how to exploit these technologies in building power big data processing platform and facilitating science discoveries such as those in electric power systems. This paper explores how Spark and Cloud computing can accelerate performance of missive insulator leak current data pattern recognition. We have designed and implemented the Parallel KNN(k-NearestNeighbor) algorithm using Spark and then deployed onto the Aliyun E-MapReduce cloud computing platform. The results from experiments shows the performance and scalability can be enhanced through these advanced technologies.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - Fast Type Recognition of Missive Insulator Leakage Current Data Using Spark
AU  - Song Yaqi
Y1  - 2016/05/24
PY  - 2016
N1  - https://doi.org/10.11648/j.jeee.20160403.12
DO  - 10.11648/j.jeee.20160403.12
T2  - Journal of Electrical and Electronic Engineering
JF  - Journal of Electrical and Electronic Engineering
JO  - Journal of Electrical and Electronic Engineering
SP  - 51
EP  - 56
PB  - Science Publishing Group
SN  - 2329-1605
UR  - https://doi.org/10.11648/j.jeee.20160403.12
AB  - Performance is the key issue in power big data applications. One of main challenges is how to exploit these technologies in building power big data processing platform and facilitating science discoveries such as those in electric power systems. This paper explores how Spark and Cloud computing can accelerate performance of missive insulator leak current data pattern recognition. We have designed and implemented the Parallel KNN(k-NearestNeighbor) algorithm using Spark and then deployed onto the Aliyun E-MapReduce cloud computing platform. The results from experiments shows the performance and scalability can be enhanced through these advanced technologies.
VL  - 4
IS  - 3
ER  -

Copy | Download