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Big Data Storage and Parallel Analysis of Grid Equipment Monitoring System

Volume 14, Number 2, February 2018, pp. 202-209
DOI: 10.23940/ijpe.18.02.p2.202209

Xiaoming Zhoua, Anlong Sua, Guanghan Lia, Weiqi Gaob, Chunhua Linb, Shidong Zhuc,*, Zhenliu Zhouc

aState Grid Liaoning Electric Power Co., Ltd, Shenyang, 110004, China

bState Grid Dalian Electric Power Co., Ltd, Dalian, 116001, China

cShenyang Institute of Engineering, Shenyang, 110136, China



Abstract:

With the analysis on data feature of grid equipment operation monitoring, this work focuses on discussing the big data storage scheme for grid equipment online monitoring data, and describes optimization measure of grid monitoring data analysis. Based on the characteristics of large data scale, multiple data types and low value density with the online monitoring data, we provide a big data storage scheme based on HDFS cloud platform using consistent hashing. Meanwhile, we also employ a multi-channel data acquisition system using multiscale multivariate entropy as the feature extraction algorithm of the multi-source power grid monitoring data. To validate the efficiency of the algorithm, we perform experiments using power grid equipment ledger data, chromatographic hydrocarbons data of transformer oil, microclimate data, and transformer vibration data for association analysis. The big data storage scheme and the feature extraction algorithm proved that it could reduce the communication overhead between storage nodes, efficiently improve system performance, and is suitable for the actual application of power grid monitoring system.

 

References: 12

  1. M. U. Ahmed and D. P. Mandic, “Multivariate Multiscale Entropy Analysis,” Signal Processing Letters, vol. 9, no. 2, pp. 91-94, 2012
  2. Z. Fadika, M. Govindaraju, and R. Canon, “Evaluating Hadoop for Data-intensive Scientific Operations,” in Proceedings of 2012 IEEE 5th International Conference on Cloud Computing (CLOUD), pp. 67–74, Hawaii, USA, June 2012
  3. G. L. Qu, H. S. Yang, and Y. Zhang, “Quick Resolution about Mass Data of Power Quality Data Exchange File Based on Map-Reduce Module,” Power System Technology, vol. 38, no. 6, pp. 1705-1711, 2014
  4. G. Reeves, J. Liu, and S. Nath, “Managing Massive Time Series Streams with Multi-scale Compressed Trickles,” Proceedings of the Vldb Endowment, vol. 2, no. 1, pp. 97-108, 2009
  5. S. Rusitschka, K. Eger, and C. Gerdes, “Smart Grid Data Cloud: A model for Utilizing Cloud Computing in the Smart Grid Domain,” in Proceedings of 2010 First IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 483–488, Mayland, UK, Jan 2010
  6. S. Singh and Y. Liu, “A Cloud Service Architecture for Analyzing Big Monitoring Data,” Tsinghua Science & Technology, vol. 21, no. 1, pp. 50-70, 2016
  7. D. W. Wang and X. J. Liu, “Parallel Fault Diagnosis Method of Power Equipment Based on MapReduce,” Electric Power Automation Equipment, vol. 34, no. 10, pp. 116-120, 2014
  8. D. W. Wang, Y. Q. Song, and Y. L. Zhu, “The Information Platform of Smart Power Grids Based on Cloud Computing,” Power System Automation, vol. 34, no. 22, pp. 7-12, 2010
  9. X. W. Wang, X. M. Zhai, and X. L. Jiang, “Self-adaption SPIHT Data Compression of Insulator Leakage Current,” Transactions of China Electro Technical Society, vol. 26, no. 12, pp. 190-196, 2011
  10. D. S. Yang, J. J. Chen, and M. Zhang, “Research and Application on Key Technology of High-speed Storage and Retrieval for Big Data,” Electronic Testing, vol. 3, pp.62-63, 2014
  11. Q. L. Zhang, Z. Q. Zhou, and S. Q Gu, “Application for Mass Data of Monitor Lightning in Cloud,” Automatic Control on Electrical Power System, vol. 36, no. 24, pp. 58-63, 2012
  12. Y. Zhang, H. G. Yang, and M. Q. Ye, “Management Program of Mass Electric Energy Quality Monitoring Data Based on Distributed File System,” Power System Automation, vol. 38, no. 2, pp. 116-120, August 2014

 

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