基于主成分分析的电力系统稳态运行数据压缩方案

Sarasij Das, P. Rao
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引用次数: 34

摘要

智能电网中越来越多地使用数字仪器,导致测量数据量迅速增加。在未来的SG中,大量的数据将由智能电表、基于PMU的WAMS、SCADA和其他监控设备产生。同时研究了合适的压缩技术来存储电力系统扰动和PMU数据;SCADA数据基本上是电网的稳态运行数据,对SCADA数据的存储研究较少。目前,公用事业公司将一些重要的SCADA数据存储一段有限的时间后,要么删除这些数据,要么以不可靠的方式(CD/DVD等)存储这些数据。本文探讨了基于主成分分析的有损压缩技术在稳态运行数据存档中的应用。研究中考虑了电压、线路流量、兆瓦和MVAr发电量这四个重要的运行数据。结合印度南部区域电网的实际数据,对该方法的有效性进行了评价。结果说明了该技术的有效性。
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Principal component analysis based compression scheme for power system steady state operational data
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.
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