Phasor measurement units (PMUs) can provide real-time measurement data to construct the ubiquitous electric of the Internet of Things. However, due to complex factors on site, PMU data can be easily compromised by interference or synchronization jitter. It will lead to various 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 proposed in this paper. The proposed algorithm does not require the system topology and parameters. Firstly, a data identification method based on a decision tree is proposed 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 developed. By analyzing the weighted relationships among all the data, this method can detect the bad data with a small deviation. Simulations and results of field recording data test 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.

中文翻译：

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基于谱聚类的PMU不良数据检测算法

相量测量单元（PMU）可以提供实时测量数据，以构建物联网的无处不在的电力。但是，由于现场的复杂因素，PMU数据很容易受到干扰或同步抖动的影响。这将导致各种级别的PMU数据质量问题，这些问题可能直接影响基于PMU的应用，甚至威胁到电力系统的安全。为了提高PMU的数据质量，提出了一种基于频谱聚类的PMU单数据不良数据检测算法。所提出的算法不需要系统拓扑和参数。首先，提出了一种基于决策树的数据识别方法，利用每个数据的斜率特征来区分事件数据和不良数据。然后，提出了一种基于谱聚类的不良数据检测方法。通过分析所有数据之间的加权关系，该方法可以检测出偏差很小的不良数据。现场记录数据测试的仿真和结果表明，该数据驱动方法可以有效地实现不良数据的识别和检测。该技术可以提高PMU数据质量，以保证其在电力系统中的应用。