Data-Driven Dynamic State Estimation of Photovoltaic Systems via Sparse Regression Unscented Kalman Filter

Abstract

This article proposes a data-driven dynamic state-estimation (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 PV systems using a 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 the state estimation by processing the noisy output data. Ultimately, this article substantiates the efficacy of the proposed sparse regression-based unscented Kalman filter through simulation results, providing a comparative analysis with a physics-based DSE.