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Principal component analysis based compression scheme for power system steady state operational data

机译:基于主组件分析的电力系统稳态运行数据压缩方案

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Growing use of digital instruments in smart grids (SG) is resulting in the rapid increase of the measured data volume. In future SG, vast amount of data will be generated by smart meters, PMU based WAMS, SCADA and other monitoring devices. While research has been done to find suitable compression technologies to store power system disturbance and PMU data; there is lack of research on the storage of SCADA data which is basically steady state operational data of the grid. Presently, utilities store some important SCADA data for a limited period and then they either delete them or store them in unreliable manner (CD/DVD etc.). The investigations presented here explore the application of Principal Component Analysis based lossy compression technique for archiving the steady state operational data. Four important operational data - voltage, line flow, MW and MVAr generation are considered for the study. The effectiveness of the proposed method is evaluated considering practical data pertaining to the Southern Regional Grid of India. The results illustrate the usefulness of the technique.
机译:在智能电网(SG)中不断使用数字仪器(SG)的使用导致测量数据量的快速增加。在未来的SG中,大量数据将由智能电表,基于PMU的WAMS,SCADA和其他监控设备生成。虽然已经进行了研究以找到适当的压缩技术来存储电力系统干扰和PMU数据;对SCADA数据的存储缺乏研究,该数据基本稳定的网格运行数据。目前,实用程序将一些重要的SCADA数据存储有限的时期,然后删除它们或以不可靠的方式存储它们(CD / DVD等)。这里提出的调查探讨了基于主成分分析的损耗压缩技术应用于归档稳态运行数据。研究进行了四个重要的操作数据 - 电压,线路流量,MW和MVAR生成。考虑到了与印度南部区域网格有关的实际数据,评估了该方法的有效性。结果说明了该技术的有用性。

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