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

机译:基于主成分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:数字表格在智能电网(SG)中的使用日益广泛,导致测量数据量的迅速增加。在未来的SG中,智能电表,基于PMU的WAMS,SCADA和其他监视设备将生成大量数据。尽管已经进行了研究以找到合适的压缩技术来存储电力系统干扰和PMU数据;缺乏对SCADA数据存储的研究,SCADA数据基本上是网格的稳态运行数据。当前,实用程序会在有限的时间内存储一些重要的SCADA数据,然后要么删除它们,要么以不可靠的方式存储它们(CD / DVD等)。本文介绍的研究探索了基于主成分分析的有损压缩技术在稳态运行数据归档中的应用。研究中考虑了四个重要的操作数据-电压,线流量,MW和MVAr的产生。考虑到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实用性。

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