Sequential Detection of Microgrid Bad Data via a Data-Driven Approach Combining Online Machine Learning With Statistical Analysis

Abstract

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Bad data is required to be detected and removed from the microgrid data stream because it misleads the decision-making of the Energy Management Systems (EMS) and puts the microgrid at risk of instability. In this paper, the authors propose a sequential detection method that combines three data mining algorithms, that is the Online Sequential Extreme Learning Machine (OSELM), statistical analysis within a sliding time window, and the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). After sequential data training, OSELM is used to construct an online updated error-filtering map to extract the electrical feature of the microgrid data sequence. Meanwhile, the statistical features, i.e. the surge of the variance and the corresponding correlation coefficients under a sliding time window are first proposed as another two complementary feature dimensions. The three-dimensional features are finally analyzed by DBSCAN to discriminate the bad data. The detection performance of this approach is verified by the data sequence collected from a four-terminal ring-shaped DC microgrid prototype. Compared with bad data detection using a single electrical feature or only statistical features, this approach shows the best performance. Moreover, it can be further applied to the online detection of microgrid bad data in the future.

Keywords

• bad data detection
• microgrid anomaly
• OSELM
• DBSCAN
• statistical analysis