1 research outputs found
Learning a Generator Model from Terminal Bus Data
In this work we investigate approaches to reconstruct generator models from
measurements available at the generator terminal bus using machine learning
(ML) techniques. The goal is to develop an emulator which is trained online and
is capable of fast predictive computations. The training is illustrated on
synthetic data generated based on available open-source dynamical generator
model. Two ML techniques were developed and tested: (a) standard vector
auto-regressive (VAR) model; and (b) novel customized long short-term memory
(LSTM) deep learning model. Trade-offs in reconstruction ability between
computationally light but linear AR model and powerful but computationally
demanding LSTM model are established and analyzed.Comment: 6 pages, 9 figure