TY - GEN
T1 - Neural Electromagnetic Transients Program
AU - Zhou, Yifan
AU - Zhang, Peng
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - This paper devises a neural electromagnetic transients program (NeuEMTP), an unsupervised, physics-informed learning approach to numerical-integration-free EMTP solutions. The main contributions lie in: (1) a learning-based NeuEMTP architecture to simultaneously generate the electromagnetic states at all desired time steps, making the step-by-step integration unnecessary; (2) an unsupervised, physics-informed training procedure to realize the NeuEMTP functionality without requiring any EMTP trajectories beforehand; (3) an EMTP-oriented-neural-network (EMTPNet) accompanied with a novel activation function Act_mix to enable efficient extrapolations of diverse oscillation modes under arbitrary frequencies. Case studies sys-tematically verify that NeuEMTP generates high-fidelity EMTP trajectories without involving any numerical integration before or during the training process, and is promising to achieve faster-than-real-time EMTP simulations on the off-the-shelf computers.
AB - This paper devises a neural electromagnetic transients program (NeuEMTP), an unsupervised, physics-informed learning approach to numerical-integration-free EMTP solutions. The main contributions lie in: (1) a learning-based NeuEMTP architecture to simultaneously generate the electromagnetic states at all desired time steps, making the step-by-step integration unnecessary; (2) an unsupervised, physics-informed training procedure to realize the NeuEMTP functionality without requiring any EMTP trajectories beforehand; (3) an EMTP-oriented-neural-network (EMTPNet) accompanied with a novel activation function Act_mix to enable efficient extrapolations of diverse oscillation modes under arbitrary frequencies. Case studies sys-tematically verify that NeuEMTP generates high-fidelity EMTP trajectories without involving any numerical integration before or during the training process, and is promising to achieve faster-than-real-time EMTP simulations on the off-the-shelf computers.
KW - data-driven computing
KW - deep learning
KW - Electromagnetic transients program (EMTP)
KW - physics-informed deep learning
KW - trapezoidal rule
UR - https://www.scopus.com/pages/publications/85141470343
U2 - 10.1109/PESGM48719.2022.9916869
DO - 10.1109/PESGM48719.2022.9916869
M3 - Conference contribution
AN - SCOPUS:85141470343
T3 - IEEE Power and Energy Society General Meeting
BT - 2022 IEEE Power and Energy Society General Meeting, PESGM 2022
PB - IEEE Computer Society
T2 - 2022 IEEE Power and Energy Society General Meeting, PESGM 2022
Y2 - 17 July 2022 through 21 July 2022
ER -