  A significant increase in renewable energy production is necessary to achieve
the UN's net-zero emission targets for 2050. Using power-electronic
controllers, such as Phase Locked Loops (PLLs), to keep grid-tied renewable
resources in synchronism with the grid can cause fast transient behavior during
grid faults leading to instability. However, assessing all the probable
scenarios is impractical, so determining the stability boundary or region of
attraction (ROA) is necessary. However, using EMT simulations or Reduced-order
models (ROMs) to accurately determine the ROA is computationally expensive.
Alternatively, Machine Learning (ML) models have been proposed as an efficient
method to predict stability. However, traditional ML algorithms require large
amounts of labeled data for training, which is computationally expensive. This
paper proposes a Physics-Informed Neural Network (PINN) architecture that
accurately predicts the nonlinear transient dynamics of a PLL controller under
fault with less labeled training data. The proposed PINN algorithm can be
incorporated into conventional simulations, accelerating EMT simulations or
ROMs by over 100 times. The PINN algorithm's performance is compared against a
ROM and an EMT simulation in PSCAD for the CIGRE benchmark model C4.49,
demonstrating its ability to accurately approximate trajectories and ROAs of a
PLL controller under varying grid impedance.
