Applications of Physics-Informed Neural Networks in Power Systems - A Review

The advances of deep learning (DL) techniques bring new opportunities to numerous intractable tasks in power systems (PSs). Nevertheless, the extension of the application of DL in the domain of PSs has encountered challenges, e.g., high requirement for the quality and quantity of training data, production of physically infeasible/inconsistent solutions, and low generalizability and interpretability. There is a growing consensus that physics-informed neural networks (PINNs) can address these concerns by integrating physics-informed (PI) rules or laws into state-of-the-art DL methodology. This survey presents a systematic overview of the PINN in the domain of PSs. Specifically, several paradigms of PINN (e.g.,…

• UD
  U.S. Department of Energy
• OO
  Office of Energy Efficiency and Renewable Energy
• SE
  Solar Energy Technologies Office

  Award: DE-EE0009337