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

TL;DR

This paper introduces a data-driven dynamic state estimation method for photovoltaic systems using sparse regression and an unscented Kalman filter, improving real-time monitoring under noise and incomplete data conditions.

Contribution

It develops a novel two-phase framework combining sparse regression for model identification and an unscented Kalman filter for state estimation in PV systems.

Findings

Effective in noisy environments

Outperforms physics-based DSE in simulations

Handles incomplete measurements robustly

Abstract

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.