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

Abstract EN

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.