Artificial ecosystem‐based optimiser to electrically characterise PV generating systems under various operating conditions reinforced by experimental validations

Elkholy, Mahmoud M.; El‐Hameed, Mohamed A.; El‐Fergany, Attia A.

doi:10.1049/rpg2.12059

Cited by 18 publications

(11 citation statements)

References 41 publications

(94 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These parameters should be modified according to the new operating conditions and this will be presented in the following Eqs. ( 7)-( 11) [17], [49]- [51].…”

Section: Figure 1 Solar Cell Pv Modelmentioning

confidence: 99%

“…The coot's leaders are considered as percentage of the total assumed coots "populations, N pop " and the remaining are coot's followers. The positions of followers (Pos coots0 ) and leaders (Pos leader ) are randomly initialized as following (15), and ( 16 The fitness of all coot's followers Fit coots can be estimated using the OF (F obj ) as per (17). The best global coots score Gbest score and its position Gbest pos is identified by (18).…”

Section: Cbmo Search Algorithm Proceduresmentioning

confidence: 99%

See 1 more Smart Citation

Performance Assessment of Solar Generating Units Based on Coot Bird Metaheuristic Optimizer

et al. 2021

Self Cite

View full text Add to dashboard Cite

In this paper, a comprehensive methodology based on coot bird metahurestic optimizer called CBMO is presented aiming to demonstrate the performance of solar units under various operating and loading scenarios. The CBMO is capable to deal with different complicated behavior in both harmonized or disorderly phases and can define its major parameters with proofed indicators. It can convey from exploration state to exploitation state to realize a fast and better convergence without trapping in local minima. At first stage of this effort, the optimal values of uncertain parameters of well-known benchmarking PV units under study are cropped with compulsory validations and comparisons. It can be mentioned here both single-and doublediode models of such units are utilized, and their unknown parameters are emphasized. Then, a Simulink detailed model is built-up employing the obtained parameters with various types of connected loads such as static resistive load and dynamic DC motor to appraise the operating performance of such units. It is worth mentioning that the performance of solar units under varied sun radiations and cell temperatures are made along the presented work. To sum up, it can be empathized that the CBMO performs well, and its results are very competitive to those available in the literature.

show abstract

“…These parameters should be modified according to the new operating conditions and this will be presented in the following Eqs. ( 7)-( 11) [17], [49]- [51].…”

Section: Figure 1 Solar Cell Pv Modelmentioning

confidence: 99%

Section: Cbmo Search Algorithm Proceduresmentioning

confidence: 99%

Performance Assessment of Solar Generating Units Based on Coot Bird Metaheuristic Optimizer

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…The mathematical models and equivalent circuits of the photovoltaic cell and panel are well known and explained in many papers and books [12,43,44]. The mathematical model for two diode model of the photovoltaic panel is following:…”

Section: Mathematical Model Of Photovoltaic Cell and Panelmentioning

confidence: 99%

Hybrid successive discretisation algorithm used to calculate parameters of the photovoltaic cells and panels for existing datasets

Cotfas

Deaconu

Cotfas

2021

IET Renewable Power Gen

View full text Add to dashboard Cite

This paper presents a new hybrid successive discretisation algorithm, used to calculate the parameters of the photovoltaic cells and panels, by the one diode model and the two diode model. Nine known datasets from the specialised literature were used to validate the new algorithm and then it was firstly applied for two new datasets. For the first time only one algorithm is applied to extract the parameters, in both cases-one and two diode models. The new datasets are for commercial monocrystalline silicon and amorphous silicon photovoltaic cells. The main test used to prove the performance of HDSA is the root mean square error. Other four tests were used for comparison: the mean absolute error, the mean bias error, t-statistic, and the coefficient of determination. The hybrid successive discretisation algorithm proved its accuracy and reliability for parameter extraction of different types of photovoltaic cells and panels for all datasets used. Comparing the hybrid successive discretisation algorithm with the best algorithms from the specialised literature shows an improvement of the root mean square error by up to10.4% for the one diode model and by up to 7.5% for the two diode model, respectively.

show abstract

“…The Photowatt-PWP201 module has Ns = 36 cells connected in series and the measured I-V test points are measured at G = 1000 W/m 2 and T = 45 • C [44]. The mono-crystalline STM6 [45], and Ultra 85-P [46] modules consist of 36 monocrystalline PV cells connected in series and are measured under temperature levels of 51 • C, and 25 • C, respectively. The L min and L max of each known parameter according to the type of the PV cell and PV modules used in this study are given in Table 2, as described in the literature [13,16].…”

Section: Datasets Descriptionmentioning

confidence: 99%

“…In this section, a new commercial module, called the Ultra 85-P module, was used to check the performance of the algorithms. The typical nameplate of this module is shown in [46], where it consists of 36 PV cells connected in series and could generate a maximum power of 85 W at standard conditions. This module has an efficiency of 13.4% and 70.3% as a fill factor.…”

Section: Ultra 85-p Modulementioning

confidence: 99%

Recent Meta-Heuristic Algorithms with a Novel Premature Covergence Method for Determining the Parameters of PV Cells and Modules

et al. 2021

View full text Add to dashboard Cite

Currently, the incorporation of solar panels in many applications is a booming trend, which necessitates accurate simulations and analysis of their performance under different operating conditions for further decision making. In this paper, various optimization algorithms are addressed comprehensively through a comparative study and further discussions for extracting the unknown parameters. Efficient use of the iterations within the optimization process may help meta-heuristic algorithms in accelerating convergence plus attaining better accuracy for the final outcome. In this paper, a method, namely, the premature convergence method (PCM), is proposed to boost the convergence of meta-heuristic algorithms with significant improvement in their accuracies. PCM is based on updating the current position around the best-so-far solution with two-step sizes: the first is based on the distance between two individuals selected randomly from the population to encourage the exploration capability, and the second is based on the distance between the current position and the best-so-far solution to promote exploitation. In addition, PCM uses a weight variable, known also as a controlling factor, as a trade-off between the two-step sizes. The proposed method is integrated with three well-known meta-heuristic algorithms to observe its efficacy for estimating efficiently and effectively the unknown parameters of the single diode model (SDM). In addition, an RTC France Si solar cell, and three PV modules, namely, Photowatt-PWP201, Ultra 85-P, and STM6-40/36, are investigated with the improved algorithms and selected standard approaches to compare their performances in estimating the unknown parameters for those different types of PV cells and modules. The experimental results point out the efficacy of the PCM in accelerating the convergence speed with improved final outcomes.

show abstract

Artificial ecosystem‐based optimiser to electrically characterise PV generating systems under various operating conditions reinforced by experimental validations

Cited by 18 publications

References 41 publications

Performance Assessment of Solar Generating Units Based on Coot Bird Metaheuristic Optimizer

Performance Assessment of Solar Generating Units Based on Coot Bird Metaheuristic Optimizer

Hybrid successive discretisation algorithm used to calculate parameters of the photovoltaic cells and panels for existing datasets

Recent Meta-Heuristic Algorithms with a Novel Premature Covergence Method for Determining the Parameters of PV Cells and Modules

Contact Info

Product

Resources

About