Abstract
The big data technology provides a new opportunity to the electric power system in various fields. In view of the shortage in traditional power system computing platform in computing, storage, information integration and analysis, this article puts forward a platform of power system based on cloud computing. Firstly, this paper outlines the development of power system can produce a large amount of data, and cloud computing technology is widely used in big data processing as a kind of new model, we can take chance of its application in power system. This paper discusses the relationship between cloud computing, big data and power system, and makes the conclusion that cloud computing technology can meet the demand of mass data storage and computing power system. Then we do research about the architecture, software technology of cloud platform system in electric power system. Finally, the feasibility of the application and development trend were discussed.
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References
Kwon, O., Lee, N., Shin, B.: Data quality management, data usage experience and acquisition intention of big data analytics. Int. J. Inf. Manag. 18(1), 156–157 (2014)
Yaqi, S., Guoliang, Z., Yongli, Z.: Present status and challenges of big data processing in smart grid. Power Syst. Technol. 37(4), 927–935 (2013)
Xiaofeng, M., Xiang, C.: Big data management: concepts, techniques and challenges. J. Comput. Res. Dev. 50(1), 146–169 (2013)
Divyakant, A., Philip, B., Elisa, B., et al.: Challenges and opportunities with big data. Proc. VLDB Endowment 5(12), 2032–2033 (2012)
Meng, L., Xiaodong, C., Wen, Z., et al.: Design of cloud computing architecture for distributed load control. Power Syst. Technol. 36(8), 140–144 (2012)
Xiaoyang, T., Shengyong, Y.: A survey on application of data mining in transient stability assessment of power system. Power Syst. Technol. 33(20), 88–93 (2009)
Chen, C.L.P., Zhang, C.-Y.: Data-intensive applications, challenges, techniques and technologies: a survey on big data. Inf. Sci. 14(9), 118–119 (2014)
Ni Zhang, Yu., Yan, S.X., Wencong, S.: A distributed data storage and processing framework for next-generation residential distribution systems. Electr. Power Syst. Res. 19(26), 77–80 (2014)
Prasad, A., Green, P., Heales, J.: On governance structures for the cloud computing services and assessing their effectiveness. Int. J. Account. Inf. Syst. 15(4), 335–356 (2014)
Zhao, X., Zhou, C., Zhao, L.: Condition evaluation model of fluid power system in gradual failure based on data envelopment analysis. Comput. Fluids 27(31), 199–201 (2014)
Pereira, L.E.S., da Costa, V.M.: Interval analysis applied to the maximum loading point of electric power systems considering load data uncertainties. Int. J. Electr. Power Energ. Syst. 9(14), 97–99 (2014)
Shin, S.-J., Woo, J., Rachuri, S.: Predictive analytics model for power consumption in manufacturing. Procedia CIRP 29(30), 191–194 (2014)
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© 2016 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Chen, N. et al. (2016). Research About Solutions to the Bottleneck of Big Data Processing in Power System. In: Wan, J., Humar, I., Zhang, D. (eds) Industrial IoT Technologies and Applications. Industrial IoT 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 173. Springer, Cham. https://doi.org/10.1007/978-3-319-44350-8_5
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DOI: https://doi.org/10.1007/978-3-319-44350-8_5
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