BACKGROUND

Phasor measurement units (PMUs) can provide real-time measurement data to construct the ubiquitous power of the Internet of Things.

SUMMARY

Due to complex factors on-site, PMU data can be easily compromised by interference or synchronization jitter, resulting in varying levels of PMU data quality issues, which can directly affect the PMU-based application and even threaten the safety of power systems. In order to improve the PMU data quality, a data-driven PMU bad data detection algorithm based on spectral clustering using single PMU data is described in this disclosure. 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 data. Then, a bad data detection method based on spectral clustering is disclosed. By analyzing the weighted relationships among all the data, this method can detect the bad data that has a small deviation. Simulations and field recorded data testing results illustrate that this data-driven method can achieve bad data identification and detection effectively. This technique can improve PMU data quality to guarantee its applications in the power systems.

