Experimental study of static and dynamic behaviours of cracked PV panels

Al-Soeidat, Mohammad; Tian, Cuihua; Lu, Dylan Dah-Chuan; Agelidis, Vassilios G.

doi:10.1049/iet-rpg.2019.0359

Cited by 6 publications

(4 citation statements)

References 23 publications

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“…where I ph is the photocurrent, I sat is the reverse saturation current of the diode, q is the electron charge, A is the diode ideality factor, K is the Boltzmann constant, T is the PV module temperature, R s is the series resistor, R sh is the shunt resistor, and N s is the number of connected PV cells in a series. I ph which is produced by the PV module is influenced by the solar irradiance falling on the PV module plane and the ambient temperature of the PV module [27,28] as follows:…”

Section: Static Pv Model Characteristicsmentioning

confidence: 99%

System identification of photovoltaic system based on fractional-order model

2022

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A fractional-order model of a photovoltaic (PV) system is proposed in this paper. The system identification approach is used to develop an effective dynamical model for a PV system. A real PV module and a boost converter are used to gather the experimental input–output data for the identification process. The black box modeling is applied to the system identification to obtain the transfer function, without the requirement to perform any mathematical analysis. The identification process is based on the least squares criteria for minimizing model output error, and the Levenberg–Marquardt algorithm is used for optimizing the model parameters. The proposed fractional-order model (FOM) is investigated using MATLAB to study the frequency response and the model's stability. Simulation results verify the effectiveness and advantages of the FOM in comparison to identified integer-order model.

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Section: Static Pv Model Characteristicsmentioning

confidence: 99%

System identification of photovoltaic system based on fractional-order model

2022

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“…Where I ph is the photocurrent, I sat is the reverse saturation current of the diode, q is the electron charge, A is the diode ideality factor, K is the Boltzmann constant, T is the PV module temperature, R s is the series resistor, R sh is the shunt resistor, and N s is the number of connected PV cells in series, the photocurrent ( I ph ) which is produced by the PV module is in uenced by the solar irradiance falling on the PV module plane and its in uenced by the ambient temperature of the PV module as shown below 26,27 :…”

Section: Static Pv Model Characteristicmentioning

confidence: 99%

System identification of photovoltaic system based on fractional-order model

Ebead

Abou-Zalam

Nabil

2022

Preprint

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A fractional-order model of a photovoltaic (PV) system is proposed in this paper. The system identification method is used to construct an accurate dynamical model for a PV system, a real PV module and boost converter are used to collect experimentally input-output data for the identification process. A black box modeling is considered for system identification to obtain a transfer function, without needing to make mathematical analysis. the input-output data which is measured is fitted by the least-square curve fitting method, and the parameters of the curve fitting are optimized by the Levenberg-Marquardt algorithm. the proposed fractional-order model is employed with the MATLAB and Simulink software to obtain a fractional order PID controller, which is used to extract maximum power from the PV system.

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“…Solar energy, as a clean and environmentally friendly renewable resource, has been favored by many countries in recent years. Due to the long-term outdoor exposure, solar panels are subject to the mutual impact of the temperature difference between daytime and night, wind, and snow erosion and other harsh environments, which will inevitably cause defects such as cracks, fragments, and grid breaks [1]. These defects will reduce the efficiency of photovoltaic conversion and the service life of solar panels [2,3].…”

Section: Introductionmentioning

confidence: 99%

Crack Extraction for Polycrystalline Solar Panels

Xue

Song

et al. 2021

Energies

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Crack extraction of solar panels has become a research focus in recent years. The cracks are small and hidden. In addition, there are particles of irregular shape and size on the surface of the polycrystalline solar panel, whose reflection position and direction are random. Therefore, there is a complex and uneven texture background on the solar panel image, which makes the crack extraction more difficult. In this paper, a crack extraction method combining image texture and morphological features is proposed. Firstly, the background texture and multi-scale details are suppressed by the linear filter and the Laplace pyramid decomposition method. Secondly, the edge can be extracted based on the modulus maximum method of the wavelet transform. Finally, cracks were extracted by using the improved Fuzzy C-means (FCM) clustering combining the morphological and texture features of the cracks. To make the extraction results more accurate and reasonable, an improved region growth algorithm is proposed to optimize the extraction results. All of the above research is closely centered on the accuracy and stability requirements of the solar cell crack detection, which is also the key point of this paper. The experimental results show that various improved or innovative algorithms proposed in this paper can accurately extract the position of cracks and obtain better extraction results. The detection results have good stability and can be faithful to the actual situation, which will promote the application of solar cells in more fields.

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Experimental study of static and dynamic behaviours of cracked PV panels

Cited by 6 publications

References 23 publications

System identification of photovoltaic system based on fractional-order model

System identification of photovoltaic system based on fractional-order model

System identification of photovoltaic system based on fractional-order model

Crack Extraction for Polycrystalline Solar Panels

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