A simple iterative method to determine the electrical parameters of photovoltaic cell

Chaibi, Y.; Allouhi, A.; Salhi, Mohamed Salah

doi:10.1016/j.jclepro.2020.122363

Cited by 32 publications

(9 citation statements)

References 19 publications

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“…Not only that, the series resistor Rs, plays a critical role in PV modeling as it determines the slope of I-V curve near the maximum power point. Therefore, the work in [29] and [35] proposed to decrease the value of Rs as the irradiance decreases. Nevertheless, the accuracy of Rs given by this equation at much lower irradiances can still be improved.…”

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confidence: 99%

Simple PV Modeling Under Variable Operating Conditions

et al. 2021

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Photovoltaic (PV) panels are increasingly used to convert sunlight into electricity, as a source of sustainable energy. It can be used in a wide variety of applications ranging from the well-known power generation to the emerging energy harvesting in Internet of Things (IoT). Hence, an accurate model is required to evaluate and predict the performance of the PV panel. However, the non-linear characteristics of PV panels make the modeling of their electrical response a challenging task. In the literature, most of the previous PV models have been developed for large wattage PVs under high irradiance, or for small wattage PVs under lower irradiance. Those that can model both usually require more information, including the I-V curves data at different irradiances, which is not always provided by the manufacturers. Therefore, this paper presents a simple PV modeling that can be applied for different wattage panels at different operating levels of irradiance, using only the commonly provided datasheet values at standard test condition (STC). The model uses the characteristic points translation technique to translate the short circuit current, open circuit voltage and maximum power voltage points, at STC to other operating conditions. These translated values are then used by the parameter extraction technique to extract the model's parameters. The proposed model's techniques can model the losses across the resistors at low irradiance, which reduces the error. The accuracy of the proposed model is validated using two representative commercial PV panels. Results are generated for the proposed model and other comparative works. The results show that the proposed model can improve the accuracy over the other compared works, with a consistent percentage difference of below 5% across all levels of irradiances for both panels.

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confidence: 99%

Simple PV Modeling Under Variable Operating Conditions

et al. 2021

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“…An analytical method based on the ideality factor variation was used to identify the electrical parameters and the absolute error of output current was found below 0.25 A for a solar module [14]. A simple iterative method was adopted to obtain the initial values of shunt resistance and its relative error did not exceed 4.38% [15]. The Newton-Raphson method was utilized to estimate the reverse saturation current and diode ideality factor, and the deviation in open circuit voltage was found 0.08 V for a solar cell [16].…”

Section: Literature Reviewmentioning

confidence: 99%

“…In the movement step, in addition to Y i−GWO (k + 1), calculated by Equations ( 10)-( 13), Y i−DLH (k + 1) is generated as another candidate for the new position of Y i (k). To do this, initially, a radius R i (k) is computed utilizing Euclidean distance among Y i (k) and Y i−GWO (k + 1) with Equation (15). Following from this, N i (k), which indicates the neighbors of Y i (k), is constituted with Equation ( 16) and multi neighbors learning is realized with Equation (17).…”

Section: Grey Wolf Optimizer With Dimension Learning-based Hunting Search Strategymentioning

confidence: 99%

Parameter Extraction of Photovoltaic Cells and Modules Using Grey Wolf Optimizer with Dimension Learning-Based Hunting Search Strategy

Yeşilbudak

2021

Energies

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With the increase in the share of solar energy in the sustainable development, accurate parameter identification plays a significant role in designing optimal solar photovoltaic systems. For this purpose, this paper extensively implements and evaluates the grey wolf optimizer with a dimension learning-based hunting search strategy, an improved version of GWO named I-GWO, in the parameter extraction of photovoltaic cells and modules. According to the experimental results, the double-diode model leads to better fitness than the other diode models in representing the physical behaviors of both photovoltaic cells and photovoltaic modules. For further performance validation, firstly, the internal parameters extracted by the I-GWO algorithm and the corresponding output current data are compared with a number of widely-used parameter extraction methods in the literature. Then, the best goodness-of-fit results achieved by the I-GWO algorithm are evaluated considering many state-of-the-art metaheuristic algorithms in the literature. The accuracy measures including root mean squared error and sum of individual absolute errors show that I-GWO is fairly promising to be the efficient and valuable parameter extraction method for both photovoltaic cells and photovoltaic modules.

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“…The primary advantage of these methods are that they are fast and could be employed to determine the upper and lower boundary ranges of variables [9], [13]. These methods, however, face a number of challenges: To begin with, the manufacturer's datasheet is not always available, and if it is given, it is only represented at STC, which the nonlinearity and variability of the weather conditions are ignored [19]. Furthermore, the derived mathematical equations for the same PV model may differ from one research study to the next, and any small deviation will result in a little decline in accuracy [20], [21].…”

Section: Introductionmentioning

confidence: 99%