Physics-Informed Neural Networks for Non-linear System Identification for Power System Dynamics
This addresses challenges for power system operators in dynamic security assessment and real-time control due to varying power-infeed, representing an incremental improvement by applying an existing method (PINN) to a new domain-specific problem.
The paper tackles the problem of identifying non-linear system dynamics in power systems with uncertain parameters like inertia and damping, using Physics-Informed Neural Networks (PINN) to estimate frequency dynamics, and demonstrates its performance in test cases compared to state-of-the-art algorithms like the Unscented Kaluk Filter.
Varying power-infeed from converter-based generation units introduces great uncertainty on system parameters such as inertia and damping. As a consequence, system operators face increasing challenges in performing dynamic security assessment and taking real-time control actions. Exploiting the widespread deployment of phasor measurement units (PMUs) and aiming at developing a fast dynamic state and parameter estimation tool, this paper investigates the performance of Physics-Informed Neural Networks (PINN) for discovering the frequency dynamics of future power systems. PINNs have the potential to address challenges such as the stronger non-linearities of low-inertia systems, increased measurement noise, and limited availability of data. The estimator is demonstrated in several test cases using a 4-bus system, and compared with state of the art algorithms, such as the Unscented Kalman Filter (UKF), to assess its performance.