Physics-Informed Neural Networks for Accelerating Power System State
  Estimation
Solon Falas Markos Asprou Charalambos Konstantinou Maria K. Michael
State estimation is the cornerstone of the power system control center since
it provides the operating condition of the system in consecutive time
intervals. This work investigates the application of physics-informed neural
networks (PINNs) for accelerating power systems state estimation in monitoring
the operation of power systems. Traditional state estimation techniques often
rely on iterative algorithms that can be computationally intensive,
particularly for large-scale power systems. In this paper, a novel approach
that leverages the inherent physical knowledge of power systems through the
integration of PINNs is proposed. By incorporating physical laws as prior
knowledge, the proposed method significantly reduces the computational
complexity associated with state estimation while maintaining high accuracy.
The proposed method achieves up to 11% increase in accuracy, 75% reduction in
standard deviation of results, and 30% faster convergence, as demonstrated by
comprehensive experiments on the IEEE 14-bus system.