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Operating Analysis and Data Mining System for Power Grid Dispatching

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DOI: 10.4236/epe.2013.54B119    3,517 Downloads   4,682 Views   Citations

ABSTRACT

The dispatching center of power-grid companies is also the data center of the power grid where gathers great amount of operating information. The valuable information contained in these data means a lot for power grid operating management, but at present there is no special method for the management of operating data resource. This paper introduces the operating analysis and data mining system for power grid dispatching. The technique of data warehousing online analytical processing has been used to manage and analysis the great capacity of data. This analysis system is based on the real-time data of the power grid to dig out the potential rule of the power grid operating. This system also provides a research platform for the dispatchers, help to improve the JIT (Just in Time) management of power system.

Cite this paper

H. Zhou, D. Liu, D. Li, G. Shao and Q. Li, "Operating Analysis and Data Mining System for Power Grid Dispatching," Energy and Power Engineering, Vol. 5 No. 4B, 2013, pp. 616-620. doi: 10.4236/epe.2013.54B119.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] J. Q. Zhao, S. Yan, X. Xiao and Y. Z. Zhou, “Design and Implementation of Double-core Redundant Power Grid Dispatch Automation System,” Automation of Electric Power Systems, Vol. 33, No. 21, 2009, pp. 101-103.
[2] S. M. Wang, S. N. Wu, D. L. Zhou and W. C. Wu, “Research on Dispatch Training Base Construction Scheme for Jiangxi Power Grid,” Electric Power, Vol. 42, No. 4, 2009, pp. 70-74.
[3] “ISO Market Monitoring & Information Protocol,” California Independent System Operator Corporation FERC Electric Tariff First Replacement, Vol. 2, No. 497.
[4] H. L. Jin and H. Liu, “Research on visualization techniques in data mining,” Proceedings of the 2009 International Conference on Computational Intelligence and Software Engineering, 2009, p. 3.
[5] A. Koretsune, S. Aoki, T. Konzo, H. Tsuji, S. Shimano and E. Mimura, “DEA-based data mining for energy consumption,” 10th IEEE International Conference on Emerging Technologies and Factory Automation, Vol. 1, 2005, p. 4. doi:10.1109/ETFA.2005.1612646

  
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