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Learning Model of Generator from Terminal Data

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Assuming that a generator is monitored by the system operator via a PMU device positioned at the generator’s terminal bus, we pose and resolve the question of the real-time, data-driven and automatic monitoring of the generator’s performance. We establish regimes of optimal performance for four complementary techniques ranging from the computationally light (a) Vector Auto-Regressive Model, suitable for normal, linear or almost linear regime, via (b) Long-Short-Term-Memory and (c) Neural ODE Deep Learning models, appropriate to monitor mildly nonlinear regimes, and finally to the (d) physics-informed model. For example, the physics-informed model is capable of fast identification of nonlinear transients and providing interpretable results, suitable, in particular, for corrective actions. The conclusions are reached in the result of validating the models on synthetic data generated in a realistic setting from an open-source, state-of-the-art modeling software. Advanced analysis is followed by a summary and conclusion suitable for the next step - validation of the hierarchy of the suggested data-driven schemes in the industry setting.

Author(s):

Nikolay Stulov    
Skolkovo Institute of Science and Technology
Russian Federation

Dejan Sobajic    
Grid Consulting LLC
United States

Yury Maximov    
Theoretical Division and Center for Nonlinear Studies, Los Alamos National Laboratory
United States

Deepjyoti Deka    
Theoretical Division and Center for Nonlinear Studies, Los Alamos National Laboratory
United States

Michael Chertkov    
Program in Applied Mathematics, University of Arizona
United States

 

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