|Title||Synchrophasor Measurements-based Events Detection Using Deep Learning|
|Publication Type||Conference Paper|
|Year of Publication||2020|
|Authors||Huiying Ren, Z. Jason Hou, Heng Wang, Pavel Etingov|
|Conference Name||e-Energy '20: The Eleventh ACM International Conference on Future Energy SystemsProceedings of the Eleventh ACM International Conference on Future Energy Systems|
|Conference Location||Virtual Event AustraliaNew York, NY, USA|
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 malfunction), 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.