An Improved Method for Parameter Identification and Performance Estimation of PV Modules From Manufacturer Datasheet Based On Temperature-Dependent Single-Diode Model

Hao, Peng; Zhang, Yunpeng

doi:10.1109/jphotov.2021.3114592

Cited by 16 publications

(4 citation statements)

References 45 publications

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“…The parameters in the model are current source which represents photon current (𝐼 𝑝ℎ ), diode current (𝐼 𝑑 ) passing through the diode which is connected across the source terminals, a shunt resistor (𝑅 𝑠ℎ ) which is connected across the current source terminals and represents leakages in form of leakage current (𝐼 𝑠ℎ ) experienced by the cell, and a series resistor (𝑅 𝑠 ) representing resistances due to solder bonds, metallization of cell, terminations of junction box, and emitter and base regions [2,8,9]. Considering expressions of different electrical parameters of the model the value of output current 𝐼 𝑝𝑣 expressed in mathematical form is presented in (1) [10,11,12,13,14,15,16].…”

Section: Establishing Maximum Power Profiles For Winter and Summer Se...mentioning

confidence: 99%

Performance Adjustment Factor for Fixed Solar PV Module

Tsamaase¹,

Sakala²,

Motshidisi³

et al. 2022

Adv. sci. technol. eng. syst. j.

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There are different factors which contribute to the amount of output power which can be delivered by solar photovoltaic (PV) module at any time of the year. The factors include but not limited to solar irradiation, ambient temperature, relative humidity, wind velocity, position of sun in the sky, geographical position of installed solar PV module and others. Apparent position of the sun in the sky contribute to the amount of electromagnetic radiation from the sun reaching the module's surface area. With the sun further away from the module and irradiance reaching the module surface area at an angle non perpendicular to the surface leads to low output power delivered by the module. In southern hemisphere the PV module experience high output power around November/December which are summer months and low output power around June/July which are winter months. This paper develops performance adjustment factor of fixed solar PV module to adjust PV module output power such that the PV system can deliver required amount of power during winter season. The results show that the value of performance adjustment factor for fixed solar PV module or system was established and can be used to adjust performance or output power for winter periods.

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Section: Establishing Maximum Power Profiles For Winter and Summer Se...mentioning

confidence: 99%

Performance Adjustment Factor for Fixed Solar PV Module

Tsamaase¹,

Sakala²,

Motshidisi³

et al. 2022

Adv. sci. technol. eng. syst. j.

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show abstract

“…When using analytical approaches, some assumptions need to be made in advance, which reduces the accuracy of the solutions [9]. Several methods have been proposed, including Newton-Raphson methods [10,11], iterative curve fitting [12], Lambert W-functions [13], and more. Although deterministic methods are simple and quick, these methods are sensitive to initial values.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Leader Multi-Verse Optimizer (DLMVO): A New Algorithm for Parameter Identification of Solar PV Models

Dang

Xia

et al. 2023

Applied Sciences

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To efficiently extract the model parameters of photovoltaic (PV) modules, this paper proposed an identification method based on the Dynamic Elite-Leader Multi-Verse Optimizer (DLMVO) algorithm. An adaptive strategy was used to control parameters based on population evolution rate and aggregation rate to balance the exploitation and exploration to avoid the search falling into local optimization. In addition, this paper proposed a dynamic elite-leader-based variation strategy to enhance the probability of variation success and improve merit search speed. This proposed algorithm was applied to the parameter identification of two different PV modules and validated using six existing methods in the literature for comparison. The experimental results show that the DLMVO algorithm significantly reduced the standard deviation of the three models compared with the standard deviation of the MVO algorithm, the single diode decreased by nearly 40%, the single-component model decreased by about 28%, and the double diode exhibited the best effect, which decreased by 83%.

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“…Analytical modeling determines the current–voltage relationship of PV modules, usually based on a single‐diode model [16–19], which contains five parameters. These parameters were evaluated in De Soto's model [2] using data from the manufacturer datasheet, and the result of the predicted I–V curves under different operating conditions offers better accuracy than that of the King's model [1].…”

Section: Introductionmentioning

confidence: 99%

“…Analytical modeling determines the current-voltage relationship of PV modules, usually based on a single-diode model [16][17][18][19], which contains five parameters. These parameters were evaluated in De Soto's model [2] using data from the manufacturer datasheet, a Correspondence to: Yunpeng Zhang.…”

Section: Introductionmentioning

confidence: 99%

Improved ANN Method Based on Explicit Model for Characterization and Power Prediction of Photovoltaic Module

Zhang

Wang

2022

IEEJ Transactions Elec Engng

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With the continuous increase of solar penetration rate, the volatility and randomness of solar irradiance have brought difficulties to grid management. Therefore, accurate characterization and maximum power point prediction of photovoltaic (PV) modules is important for maximum power point tracking and ensuring grid stability. The single-diode model is that traditional characterization method commonly used but is inconvenient and complex because it employs an implicit equation. This paper proposes a novel model, based on an artificial neural network (ANN) with 4-input and an explicit analytical model, for estimating the maximum power point and current-voltage (I-V) characteristics of PV modules under different operating conditions. Based on irradiance and temperature, the model considers the solar zenith angle and the solar azimuth angle, reducing the loss of accuracy of the solar spectrum and the solar incident angle. The input layer of the neural network has four neurons, which are solar radiation, module temperature, zenith and azimuth. The outputs are the four shape parameters in the explicit analytical model. Once the neural network is trained using historical measured data, it can build a shape model based on the temperature, solar radiation, zenith angle and azimuth angle at a certain moment, and estimate the maximum power point and I-V characteristics without solving any nonlinearity formula equation. Through experimental verification, it can be concluded that the 4-input neural network after increasing the zenith angle and the azimuth angle is more accurate than the 2-input neural network. Finally, the experimental data of four types of photovoltaic modules are used to verify the reliability and accuracy of the proposed model. Since the zenith angle and azimuth angle are considered, the accuracy of the model is improved and the calculation time is reduced.

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An Improved Method for Parameter Identification and Performance Estimation of PV Modules From Manufacturer Datasheet Based On Temperature-Dependent Single-Diode Model

Cited by 16 publications

References 45 publications

Performance Adjustment Factor for Fixed Solar PV Module

Performance Adjustment Factor for Fixed Solar PV Module

Dynamic Leader Multi-Verse Optimizer (DLMVO): A New Algorithm for Parameter Identification of Solar PV Models

Improved ANN Method Based on Explicit Model for Characterization and Power Prediction of Photovoltaic Module

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