Abstract
At present, the evaluation of power grid operations is still in the qualitative stage, which is lack of quantitative evaluation and analysis methods, and cannot meet the requirements of modern power grid development and the needs of the lean management of Power Grid Corp. Based on the characteristics of the power grid operations, constructing the power distribution network operation monitoring platform based on big data technology is an effective way to achieve quantitative evaluation of operational efficiency, power supply capacity, and region monitoring. This paper introduces the construction and application of big data based power grid network operation monitoring platform, including the system architecture of the power distribution network operation monitoring platform, the evaluation model of power supply capacity and operation efficiency of distribution network, functions of data access and quality control, power grid operation online visual monitoring analysis, and operation monitoring and control cooperation. Actual operation of the system shows that the power grid network operation monitoring platform provides a quantitative evaluation and decision basis for power grid planning, construction and production operations, makes full use of the existing data value, provides decision support for the business sector, and further enhances the strategic decision-making and operation monitoring center operation control and risk prevention ability. At the same time, the power operation monitoring platform has also played a good reference role for the construction of other power operation monitoring platform.
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This work was supported by the State Grid Corporation Science and Technology Project (Contract No.: SGLNXT00YJJS1800110).
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Su, S. et al. (2019). A Power Grid Operations Monitoring Platform Based on Big Data Technology. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11633. Springer, Cham. https://doi.org/10.1007/978-3-030-24265-7_45
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DOI: https://doi.org/10.1007/978-3-030-24265-7_45
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