North China Electric Power University

Zhiwei Yang

Hao Liu

Tianshu Bi

Ke Jia

A data-driven PMU bad data detection algorithm based on spectral clustering using single PMU data is described. The described algorithm does not require the system topology and parameters. First, a data identification method based on a decision tree is described to distinguish event data and bad data by using the slope feature of each set of data. Then, a bad data detection method based on spectral clustering is described. By analyzing the weighted relationships among all the data, this method can detect the bad data that has a small deviation.

PMU date correction using a recovery method

Bad data detection algorithm for PMU based on spectral clustering

A data recovery method framework is described, in which the data is classified as either ambient or disturbance data, and recovered by different methods to achieve good performance efficiently. An approach based on decision tree is described for identifying ambient and disturbance data. Then, an improved cubic spline interpolation based on the priority allocation strategy is described for ambient data loss, which can quickly and accurately recover ambient data. Simultaneously, a disturbance data recovery method based on singular value decomposition is described. It can achieve disturbance data recovery accurately by a single channel of measurement.

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Beijing, China

Zhiwei Yang