Fast Extraction and Characterization of Fundamental Frequency Events from a Large PMU Dataset using Big Data Analytics

Baembitov, Rashid
Dokic, Tatjana
Kezunovic, Mladen
Hu, Yi
Obradovic, Zoran
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A novel method for fast extraction of fundamental frequency events (FFE) based on measurements of frequency and rate of change of frequency by Phasor Measurement Units (PMU) is introduced. The method is designed to work with exceptionally large historical PMU datasets. Statistical analysis was used to extract the features and train Random Forest and Catboost classifiers. The method is capable of fast extraction of FFE from a historical dataset containing measurements from hundreds of PMUs captured over multiple years. The reported accuracy of the best algorithm for classification expressed as Area Under the receiver operating Characteristic curve reaches 0.98, which was obtained in out-of-sample evaluations on 109 system-wide events over 2 years observed at 43 PMUs. Then Minimum Volume Enclosing Ellipsoid Algorithm was used to further analyze the events. 93.72% events were correctly characterized, where average duration of the event as seen by the PMU was 9.93 sec.
Monitoring, Control and Protection, big data, event detection, fundamental frequency event, machine learning, pmu
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