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Study of WAMS Big Data Elastic Store Model in Low-Frequency Oscillation Analysis

机译:WAMS大数据弹性存储模型在低频振荡分析中的应用研究

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Low-frequency oscillation (LFO) is among the key factors that threaten interconnected power grids' security and stability and restrict transfer capability. In particular, power systems incur now and then weak damping and forced oscillations. To monitor and control LFO, the principles of online calculation and analysis of two types of LFO are studied in this paper. The big data of wide area measurements is an important information source of LFO analysis. Hence, we should make sure it has access to online system continuously, accurately, and reliably. Nevertheless, the conventional linear data store model has difficulty to meet the processing requirements of high rate, multiple concurrency, and high reliability. To deal with it, a new model of double-set elastic store is proposed in this paper. It transforms the storage space linear model to plane model, realizes the management of power system substation group sets in vertical direction and the management of multiple Phase Measurement Units (PMU) uploading data sets in horizontal direction, and hence solves the problems in continuous and reliable access of the wide area measurements data, which is dense and of large scale and has quick update rate, providing technical support of accuracy and robustness of LFO analysis. The performance test and practical application of the proposed new model of double-set elastic store validate its accuracy.
机译:低频振荡(LFO)是威胁互联电网安全稳定、限制输电能力的关键因素之一。特别是,电力系统时不时地会出现弱阻尼和强迫振荡。为了监测和控制低频调频,本文研究了两种低频调低频的在线计算和分析原理。广域测量的大数据是LFO分析的重要信息来源。因此,我们应该确保它能够持续、准确、可靠地访问在线系统。然而,传统的线性数据存储模型难以满足高速率、多并发、高可靠性的处理需求。针对这一问题,该文提出一种新的双集弹性存储模型。将存储空间线性模型转换为平面模型,实现了电力系统变电站组组的垂直管理和水平方向的多个相位测量单元(PMU)上传数据集的管理,从而解决了广域测量数据密集、规模大、更新速度快的连续可靠访问问题。 为LFO分析的准确性和稳健性提供技术支持。所提出的双集弹性存储新模型的性能测试和实际应用验证了其准确性。

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