  Dynamic state estimation (DSE) is vital in modern power systems with numerous
inverter-based distributed energy resources including solar and wind, ensuring
real-time accuracy for tracking system variables and optimizing grid stability.
This paper proposes a data-driven DSE approach designed for photovoltaic (PV)
energy conversion systems (single stage and two stage) that are subjected to
both process and measurement noise. The proposed framework follows a two-phase
methodology encompassing ``data-driven model identification" and
``state-estimation." In the initial model identification phase, state feedback
is gathered to elucidate the dynamics of the photovoltaic systems using
nonlinear sparse regression technique. Following the identification of the PV
dynamics, the nonlinear data-driven model will be utilized to estimate the
dynamics of the PV system for monitoring and protection purposes. To account
for incomplete measurements, inherent uncertainties, and noise, we employ an
``unscented Kalman filter," which facilitates state estimation by processing
the noisy output data. Ultimately, the paper substantiates the efficacy of the
proposed sparse regression-based unscented Kalman filter through simulation
results, providing a comparative analysis with a physics-based DSE.
