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DOE OSTI · 1765113

Synchrophasor Measurements-based Events Detection Using Deep Learning

Ren, Huiying·Hou, Zhangshuan·Wang, Heng·Etingov, Pavel V.
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Abstract

Deep learning algorithms have been developed for phasor measurement units (PMUs) analysis aiming at providing grid operators to observe and react to significant real-time changes in the grid associated with multiple factors (e.g., power generation and load variations, different type of faults, and equipment mailfunction), or for offline post-event system diagnostics. In this study, a Long Short-Term Memory (LSTM)-based deep neural network (DNN) is adopted and evaluated to identify the most appropriate model configurations for event detection and longer-term anomalous pattern extraction. The proposed DNN model shows the potential on long-term predictions with the ability to capture nonlinear and nonstationary mixture complex patterns in PMU datasets. Real-world PMU in the WECC system were used for model development and validation.

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Ren, Huiying, Hou, Zhangshuan, Wang, Heng, Etingov, Pavel V.. 2020-06-01. Synchrophasor Measurements-based Events Detection Using Deep Learning. https://doi.org/10.1145/3396851.3403513

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