Kalman-filter Based Maximum Power Point Tracking for a Single-Stage Grid-Connected Photovoltaic System

Farrokhi, Ehsan; Ghoreishy, Hoda; Ahmadiahangar, Roya; Rosin, Argo

doi:10.1109/iecon48115.2021.9589653

Cited by 1 publication

(2 citation statements)

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“…In fact, the purpose of this estimator is to find the PDF of the states assuming measurements y1, y2, …, yk, and the initial conditions x0. The Bayesian state estimator has two steps: the calculation of the priori and posteriori PDF, respectively, and in accordance with Equations ( 16) and (17).…”

Section: Standard Particle Filtermentioning

confidence: 99%

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Irradiation and Temperature Estimation with a New Extended Kalman Particle Filter for Maximum Power Point Tracking in Photovoltaic Systems

Hooshmand

Yaghobi

Jazaeri

2023

IJE

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In this paper, a new method, based on the estimation of irradiation and temperature values, was proposed for Maximum Power Point Tracking (MPPT) in photovoltaic systems. The proposed estimation method is based on a new Extended Kalman Particle Filter (EKPF). Given that the basis of the proposed method is a particle filter, firstly, the estimation is performed with high accuracy, although the target system has severe nonlinearity; secondly, there is no limitation for the probability density functions of the measurement and process noise. This method works for Gaussian and non-Gaussian noises. To show the estimation accuracy, the proposed method will be compared with the common method based on extended Kalman filter (EKF) and both methods will be evaluated due to the root means square error criterion. Due to the accurate estimation, MPPT is performed with good performance. For validation, the proposed MPPT method was compared with the EKF method and the conventional incremental conductance (InC) method. The simulations show that the efficiency is improved from 0.1% to 1% compared to the EKF, and from 0.8% to 8.65% compared to the InC method, which shows the performance of the proposed MPPT method in noisy environments.

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Section: Standard Particle Filtermentioning

confidence: 99%

“…The most common of these methods is the Kalman filter, which is widely used to estimate PV systems states. MPP tracking using the Kalman filter has been developed by Boutabba et al [15], Motahhir et al [16] and Farrokhi et al [17]. Because of using Kalman filter, these methods are robust against noise.…”

Section: Introductionmentioning

confidence: 99%