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A Novel Big Data Platform for City Power Grid Status Estimation

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1071))

Abstract

This paper proposed a Big Data platform solution for city power grid status estimation, with an in-memory distributed computing frame for power grid heterogeneous data analysis. A big data management system is proposed for managing high volume, heterogeneous and multi-mode power data. In addition, a high efficiency data-computing engine, a parallel computing model for power engineering and a 3D display system are consisted. Based on the platform, three real-system application cases are given in order to illustrate the practicability of the proposed platform, including high efficiency power grid load shedding computing and analysis, voltage sag association rule mining with 3D display, and real-time dynamic evaluation of operation status of wind farm units. As a conclusion, big data platform is a key technology to sufficiently realize the potential advantages of power data resources, and it can provide solid technological bases for the development of modern Smart Grid.

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References

  1. Zhou, K., Fu, C., Yang, S.: Big data driven smart energy management: from big data to big insights. Renew. Sustain. Energy Rev. 56, 215–225 (2016)

    Article  Google Scholar 

  2. Munshi, A.A., Yasser, A.R.M.: Big data framework for analytics in smart grids. Electr. Power Syst. Res. 151, 369–380 (2017)

    Article  Google Scholar 

  3. Chen, C.P., Zhang, C.Y.: Data-intensive applications, challenges, techniques and technologies: a survey on big data. Inf. Sci. 275, 314–347 (2014)

    Article  Google Scholar 

  4. Miao, X., Zhang, D.: The opportunity and challenge of big data’s application in distribution grids. In: 2014 China International Conference on Electricity Distribution (CICED), pp. 962–964. IEEE (2014)

    Google Scholar 

  5. Peng, X., Deng, D., Cheng, S., Wen, J., Li, Z., Niu, L.: Key technologies of electric power big data and its application prospects in smart grid. Proc. CSEE 35(3), 503–511 (2015)

    Google Scholar 

  6. Tu, C., He, X., Shuai, Z., Jiang, F.: Big data issues in smart grid-a review. Renew. Sustain. Energy Rev. 79, 1099–1107 (2017)

    Article  Google Scholar 

  7. Korres, G.N., Manousakis, N.M.: State estimation and bad data processing for systems including PMU and scada measurements. Electr. Power Syst. Res. 81(7), 1514–1524 (2011)

    Article  Google Scholar 

  8. Xue, Y., Lai, Y.: Integration of macro energy thinking and big data thinking part one big data and power big data. Autom. Electr. Power Syst. 40, 1–8 (2016)

    Google Scholar 

  9. Wang Liwei, C.X., Ting, C.: Real-time monitoring development scheme for electric transmission and transformation equipment status estimation in Fujian power gird. Power Grid Environ. 6, 56–58 (2016)

    Google Scholar 

  10. Hao, L., Jiang, C., Jia, L., Xun, G.: Research on wide-area damping control system of Mengxi power delivery passageway in North China power grid. Power Syst. Technol. (2017)

    Google Scholar 

  11. Xie Yishan, Y.Q., Chenghui, L.: Research of unified condition monitoring information model in data platform of power transmission equipment remote monitoring and diagnosis. Power Syst. Prot. Control 11, 86–91 (2014)

    Google Scholar 

  12. Chen Hongyun, C.R., Yujun, G.: Development and realization of power grid security early warning and decision support system. Guangxi Electr. Power 34, 10–12 (2011)

    Google Scholar 

  13. Cao Min, W.Y., Feng, Y.: Research and development of Yunnan power grid supervision and data analysis centre. Yunnan Electr. Power 41, 194–196 (2013)

    Google Scholar 

  14. Christopher English: Alstom brings to market digital substation 2.0 featuring smart technologies (2014). https://www.alstom.com/press-releases-news/2014/8/

  15. General Electric’s: GE automation & protection (2018). http://www.gegridsolutions.com/MD.htm

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Acknowledgments

This research is financially supported by the National High Technology Research and Development Program (2015AA050201), China NSFC under grants 51607177, China Postdoctoral Science Foundation (2018M631005), Natural Science Foundation of Guangdong Province under grants 2018A030310671.

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Correspondence to Yuanjun Guo .

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Lin, W. et al. (2019). A Novel Big Data Platform for City Power Grid Status Estimation. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2019. Communications in Computer and Information Science, vol 1071. Springer, Singapore. https://doi.org/10.1007/978-981-32-9563-6_29

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  • DOI: https://doi.org/10.1007/978-981-32-9563-6_29

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-32-9562-9

  • Online ISBN: 978-981-32-9563-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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