Accurate Estimation of Maximum Power Point Tracking for Photovoltaic Systems Using an Adaptive Rao-Blackwellized CKF Scheme

Maximum power point tracking (MPPT) technology is well suited to photovoltaic (PV) system analysis, which can improve power generation efficiency and control effectiveness. However, the nonlinear intrinsic characteristics of the PV system cannot guarantee that the power generation system always work...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2024, Vol.12, p.135973-135984
Main Authors: Zhang, Wanhong, Lan, Tian, Zhang, Yan
Format: Article
Language:English
Subjects:
Accuracy
Adaptive
Adaptive control
Adaptive systems
CKF
Computational modeling
Dynamical systems
Electric power generation
Equations of state
Integrated circuit modeling
Kalman filters
Linear equations
LogWright function
Mathematical models
Maximum power point trackers
Maximum power tracking
MPPT
Noise measurement
Nonlinear control
Nonlinear equations
Nonlinear systems
Photovoltaic cells
PV system
Rao-Blackwellization
State estimation
Systems analysis
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Maximum power point tracking (MPPT) technology is well suited to photovoltaic (PV) system analysis, which can improve power generation efficiency and control effectiveness. However, the nonlinear intrinsic characteristics of the PV system cannot guarantee that the power generation system always works stably at the maximum power point (MPP). Additionally, the accuracy and complexity of the system modeling are crucial to the performance of the PV system. In this article, a novel adaptive Rao-Blackwellized cubature Kalman filter (ARBCKF) scheme is developed that can be efficiently applied to estimate MPPT for PV systems. At first, a comprehensive analysis of the cubature Kalman filter (CKF) is conducted in the context of MPPT. Second, the LogWright function is utilized to represent the current as an explicit function of the voltage, which achieves faster and more accurate execution. Finally, an adaptive-Rao-Blackwellized method is applied to dynamic systems with nonlinear equations of state and linear equations of measurement, which can capitalize on adapting the noise covariance and improving the performance. Compared with the widely adopted other Kalman filter methods, computation results show that MPP estimation accuracy can be significantly increased in diverse transient and steady-state scenarios.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3462947