核心概念
This paper proposes a data-driven dynamic state estimation approach for photovoltaic (PV) energy conversion systems that utilizes sparse regression and an unscented Kalman filter to accurately estimate the system states without relying on a known physical model.
摘要
The paper presents a two-phase methodology for data-driven dynamic state estimation of PV systems.
In the initial model identification phase, the authors use a nonlinear sparse regression technique to elucidate the dynamics of the PV systems based on state feedback data. This data-driven model identification approach can adaptively capture changes in the PV system dynamics without the need for a priori knowledge of the physical model.
Following the identification of the PV dynamics, the authors employ an unscented Kalman filter (UKF) to estimate the states of the PV system for monitoring and protection purposes. The UKF is designed to account for incomplete measurements, inherent uncertainties, and noise in the system.
The proposed sparse regression-based UKF approach is evaluated through simulation results and compared to a physics-based dynamic state estimation method. The results demonstrate the efficacy of the data-driven technique in accurately estimating the states of both single-stage and two-stage PV systems, even in the presence of parameter variations and system-level faults.
Key highlights:
Data-driven model identification using sparse regression to capture PV system dynamics
Adaptive unscented Kalman filter for state estimation to handle uncertainties and noise
Resilience to parameter variations and system-level faults
Comparative analysis with physics-based dynamic state estimation
統計資料
The paper does not provide any explicit numerical data or statistics. The focus is on the methodology and simulation-based evaluation of the proposed data-driven dynamic state estimation approach.
引述
The paper does not contain any direct quotes that are particularly striking or supportive of the key logics.