Detection and Classification of Anomalies in Power Distribution System Using Outlier Filtered Weighted Least Square

This work presents a new algorithm for detecting and classifying data anomalies in operational measurements using statistical, clustering, and outlier-based approaches. Base detectors explored in this work includes density-based spatial clustering of applications with noise, K-Means, local outlier factor, feature bagging, and robust random cut forests using real distribution system datasets. An ensemble approach is proposed to achieve high detection accuracy and precision compared with any of the base detector and with less dependency on hyperparameter tuning. Also, developed ensemble architecture can integrate additional base detectors. In addition, a simplistic anomaly classification approach is developed, utilizing the clustering concept, while considering the physics of the power distribution systems. The developed schemes are rigorously tested and validated using data from multiple distribution phasor measurement unit devices in the Bronzeville community microgrid, with a diverse set of events and distributed energy resources at dispersed locations. Performance analysis using three test cases are provided to showcase superiority of the proposed approaches.