Parameter extraction for dynamic PV thermal model using particle swarm optimisation

Chopde, Abhay; Magare, Dhiraj; Patil, Mukesh D.; Gupta, Rajesh; Sastry, O.S.

doi:10.1016/j.applthermaleng.2016.01.164

Cited by 27 publications

(10 citation statements)

References 10 publications

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“…A wide range of MAs have been used to many applications such as expensive optimization problems [38,39], multi-objective or many optimization problems [40][41][42][43], gate resource allocation [44,45], wind speed prediction [46], and scheduling problems [47]. They can also effectively solve unknown parameter of solar cells, including but not limited to, genetic algorithm (GA) [48], particle swarm optimizer (PSO) [49], differential evolution (DE) [50], grey wolf optimization (GWO) [51], Harris hawk optimizer (HHO) [12,52], slime mould algorithm (SMA) [53], cat swarm optimization (CSO) [54], sunflower optimization algorithm (SFO) [55], multi-verse optimizer (MVO) [56], demand response algorithm (DRA) [57], ant lion optimizer (ALO) [58], and firework algorithm (FWA) [59].…”

Section: Authors Methods Remarksmentioning

confidence: 99%

Parameter identification of photovoltaic models using a sine cosine differential gradient based optimizer

Chen

Heidari

et al. 2022

IET Renewable Power Gen

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In this paper, an efficient sine cosine differential gradient‐based optimization method is proposed for identifying unknown parameters of photovoltaic models. In the simulation, parameter identification is formulated as an objective function to be minimized based on the error between the estimated and experimental data. Based on the original gradient‐based optimization method, the proposed method combines the sine cosine method and the mutation crossover of the differential evolution algorithm. Specifically, the crossover operator enables the algorithm to avoid local optima; and meanwhile, the sine cosine strategy encourages the new individual to calculate the worst position. The simulation results demonstrate that the new optimization method can achieve the minimal root mean square error and obtain better optima relative to other algorithms in different photovoltaic cells. Therefore, the proposed optimization method has great potential to be used for estimating photovoltaic model parameters.

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Section: Authors Methods Remarksmentioning

confidence: 99%

Parameter identification of photovoltaic models using a sine cosine differential gradient based optimizer

Chen

Heidari

et al. 2022

IET Renewable Power Gen

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“…That is, neglecting the material thermal capacity effect and discarding the lag in temperature variation with respect to one or more of the affecting parameters [6]. Based on this concept, the main classification of the PV temperature modelling is whether it is a static (steady-state) [12,[15][16][17]22,25,27,28] or dynamic model [2,[5][6][7]18,[29][30][31][32][33]. Although the static model requires lower computational cost, its accuracy level could be affected in case of rapid changing of the controlling parameters.…”

Section: Classification Of Pv Modules Thermal Modelling Conceptsmentioning

confidence: 99%

“…This is due to several reasons such as the abundance of the solar irradiance, the photovoltaic (PV) phenomenon, by which a direct conversion is achieved from solar radiation to electricity, employable at both small and large scale, non-polluting, clean and reliable energy sources. The increase in the temperature of the silicon-based technology PV modules has direct effect on the current-voltage (I-V) characteristics of the device, that is, adversely affecting the power production and causes a significant drop in efficiency [2,3]. Therefore, it is insufficient to rely only on the rated efficiency to estimate the output power.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Method for Thermal Modelling of Photovoltaic Modules/Cells under Varying Environmental Conditions

2020

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Temperature has a significant effect on the photovoltaic module output power and mechanical properties. Measuring the temperature for such a stacked layers structure is impractical to be carried out, especially when we talk about a high number of modules in power plants. This paper introduces a novel thermal model to estimate the temperature of the embedded electronic junction in modules/cells as well as their front and back surface temperatures. The novelty of this paper can be realized through different aspects. First, the model includes a novel coefficient, which we define as the forced convection adjustment coefficient to imitate the module tilt angle effect on the forced convection heat transfer mechanism. Second, the new combination of effective sub-models found in literature producing a unique and reliable method for estimating the temperature of the PV modules/cells by incorporating the new coefficient. In addition, the paper presents a comprehensive review of the existing PV thermal sub-models and the determination expressions of the related parameters, which all have been tested to find the best combination. The heat balance equation has been employed to construct the thermal model. The validation phase shows that the estimation of the module temperature has significantly improved by introducing the novel forced convection adjustment coefficient. Measurements of polycrystalline and amorphous modules have been used to verify the proposed model. Multiple error indication parameters have been used to validate the model and verify it by comparing the obtained results to those reported in recent and most accurate literature.

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“…In Nižetić et al [5], the power yield is presented as a heat loss, but no detailed analysis was performed to define the exact heat loss and its influence on panel's temperature. In addition, heat flow was modelled in Chopde et al [6], where the power yield is also presented as a heat loss in global heat equation. When modelling a PhotoVoltaic/Thermal (PV/T) system, Gang et al [7] proposed a novel system which can simultaneously produce thermal and electrical energy.…”

Section: Introductionmentioning

confidence: 99%

Influence of Electrical Yield on Temperature Drop of the Photovoltaic Panel: Numerical and Experimental Findings

Grubišić‐Čabo¹,

Nižetić

Marinić-Kragić

et al. 2020

J. sustain. dev. energy water environ. syst.

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This manuscript deals in confirmation of drop in photovoltaic panel temperature under load. Two panels are compared, one with the load and other without the load. The panel under load should have larger heat dissipation (i.e. be cooler), when electrical yield is taken as an outgoing heat flow. During the experiment, a thermovision of the panels under solar irradiance was made with the thermal camera. Results of the first case show only minor change in panel temperature. Results of the second case, where one panel is under load, show reduced temperature of loaded panel, by up to 4 °C. Previously developed numerical model is updated with the measured data in the experiment. A measured electrical yield is taken into the account when the solar irradiance was modelled. Numerical model confirms the temperature drop and corresponds with infrared thermography data. Gained results can help in design of the photovoltaic systems, where more variants of operating conditions can be used in order to keep the panel on desired operating temperature.

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Parameter extraction for dynamic PV thermal model using particle swarm optimisation

Cited by 27 publications

References 10 publications

Parameter identification of photovoltaic models using a sine cosine differential gradient based optimizer

Parameter identification of photovoltaic models using a sine cosine differential gradient based optimizer

A Novel Method for Thermal Modelling of Photovoltaic Modules/Cells under Varying Environmental Conditions

Influence of Electrical Yield on Temperature Drop of the Photovoltaic Panel: Numerical and Experimental Findings

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