The Application of Adaptive Algorithm in the Maximum Power Tracking of Power Photovoltaic System

Huang, Feng; Wang, Yunpeng

doi:10.1007/978-3-031-02097-1_9

Cited by 1 publication

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“…Increasing demand for electricity and increasing attention paid to the environmental impacts of the conventional electricity generation, have led the world to focus on using renewable energy sources such as fuel cells, wind power, and photovoltaic systems. Due to the many advantages of photovoltaic (PV) systems, the demand for electricity generation by them, both on-grid and off-grid, is increasing [1,2]. The power generated in a PV module depends on the amount of irradiation and its temperature.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%