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

Abstract

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.