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Event Detection From PMU Generated Big Data using R Programming

机译:从PMU生成的大数据使用R编程检测事件检测

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Recent advancement in Power System Analysis shows that implementation of PMU (Phasor Measurement Unit) in Smart Grid playing a significant role over SCADA. The main reasoning for that is more sampling data than traditional SCADA system. Every PMU data like voltage, current and Phase angle gives more samples in every second which is helpful for event detection. The enormous data send by each PMU in every second energies the big data issue. To find out and predict the transient situation and even small disturbances or anomalies from big data analysis within the specified short period of time is a challenge for near future. Because introduction of new smart electrical devices will boost up the big data issue. Processing of big data for post disturbance analysis is also an important task. This paper gives a scenario of PMU measurements received to PDC (Phasor Data Concentrator) from PMUs placed in distinct locations and detection of transient events for post disturbance analysis. In this analysis, the disturbances are evaluated with the R programming analysis and compare findings of chronological data from separate locations and also shows the relation between disturbances in a grid. For this analysis, the impacts of frequency and voltage data are also considered
机译:电力系统分析的最新进步表明,在智能电网中实施PMU(Phasor测量单位)在SCADA上发挥着重要作用。这是比传统SCADA系统更具抽样数据的主要原因。每个PMU数据如电压,电流和相位角在每秒提供更多样本,这有助于事件检测。每个PMU在每秒Energies中发送的巨大数据都是大数据问题。要了解和预测从指定短期内的大数据分析的瞬态情况,甚至小的扰动或异常是不久的将来的挑战。因为新的智能电气设备引入将提升大数据问题。后扰动分析的大数据处理也是一个重要的任务。本文给出了从PDC(Phasor数据集中器)的PMU测量的场景,从PMU放置在不同的位置和检测后扰动分析的瞬态事件。在该分析中,利用R编程分析评估了干扰,并从单独的位置比较时间数据的结果,并且还显示了网格中干扰之间的关系。对于该分析,还考虑了频率和电压数据的影响

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