Enhanced social network search algorithm with powerful exploitation strategy for PV parameters estimation

Shaheen, Abdullah M.; Elsayed, Abdallah M.; Ginidi, Ahmed; El‐Sehiemy, Ragab A.; Elattar, Ehab E.

doi:10.1002/ese3.1109

Cited by 23 publications

(6 citation statements)

References 61 publications

(125 reference statements)

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“…The suggested AHT demonstrates strong stability and great searching efficiency than previously reported algorithms, according to the statistical evaluation of the three metrics (Best, Average, Worst) presented in Table 10 . The thirty runs' results demonstrate the suggested AHT’s superior resilience over others as related to the comparison of the proposed AHT and other recently described approaches has been presented in Table 11 which are SNS 72 , FBI 44 , SA 49 , ISCE 53 , ImCSA 52 , HFAPS 69 , SMA 15 , CGBO 73 , PSO 64 , and RAO optimizer 13 for the SDM. The comparative assessment is conducted considering the DDM versus different reported state of the art such as SNS 72 , FBI 44 , PSO 74 , LAPO 75 , PSO 64 , MPA 73 and CGBO 73 .…”

Section: Simulation Resultsmentioning

confidence: 80%

Electrical parameters extraction of PV modules using artificial hummingbird optimizer

El‐Sehiemy¹,

Shaheen²,

El‐Fergany³

et al. 2023

Sci Rep

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The parameter extraction of PV models is a nonlinear and multi-model optimization problem. However, it is essential to correctly estimate the parameters of the PV units due to their impact on the PV system efficiency in terms of power and current production. As a result, this study introduces a developed Artificial Hummingbird Technique (AHT) to generate the best values of the ungiven parameters of these PV units. The AHT mimics hummingbirds' unique flying abilities and foraging methods in the wild. The AHT is compared with numerous recent inspired techniques which are tuna swarm optimizer, African vulture’s optimizer, teaching learning studying-based optimizer and other recent optimization techniques. The statistical studies and experimental findings show that AHT outperforms other methods in extracting the parameters of various PV models of STM6-40/36, KC200GT and PWP 201 polycrystalline. The AHT’s performance is evaluated using the datasheet provided by the manufacturer. To highlight the AHT dominance, its performance is compared to those of other competing techniques. The simulation outcomes demonstrate that the AHT algorithm features a quick processing time and steadily convergence in consort with keeping an elevated level of accuracy in the offered solution.

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Section: Simulation Resultsmentioning

confidence: 80%

Electrical parameters extraction of PV modules using artificial hummingbird optimizer

El‐Sehiemy¹,

Shaheen²,

El‐Fergany³

et al. 2023

Sci Rep

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“…Additionally, I PV and I express the cell photocurrent and the output current, respectively. Furthermore, V is the terminal voltage and V th represents the PV cell thermal voltage that can be calculated as follows [53]:…”

Section: Sdpvmmentioning

confidence: 99%

Dwarf Mongoose Optimizer for Optimal Modeling of Solar PV Systems and Parameter Extraction

Moustafa,

Smaili,

Almalawi

et al. 2023

Electronics

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This article presents a modified intelligent metaheuristic form of the Dwarf Mongoose Optimizer (MDMO) for optimal modeling and parameter extraction of solar photovoltaic (SPV) systems. The foraging manner of the dwarf mongoose animals (DMAs) motivated the DMO’s primary design. It makes use of distinct DMA societal groups, including the alpha category, scouts, and babysitters. The alpha female initiates foraging and chooses the foraging path, bedding places, and distance travelled for the group. The newly presented MDMO has an extra alpha-directed knowledge-gaining strategy to increase searching expertise, and its modifying approach has been led to some extent by the amended alpha. For two diverse SPV modules, Kyocera KC200GT and R.T.C. France SPV modules, the proposed MDMO is used as opposed to the DMO to efficiently estimate SPV characteristics. By employing the MDMO technique, the simulation results improve the electrical characteristics of SPV systems. The minimization of the root mean square error value (RMSE) has been used to compare the efficiency of the proposed algorithm and other reported methods. Based on that, the proposed MDMO outperforms the standard DMO. In terms of average efficiency, the MDMO outperforms the standard DMO approach for the KC200GT module by 91.7%, 84.63%, and 75.7% for the single-, double-, and triple-diode versions, respectively. The employed MDMO technique for the R.T.C France SPV system has success rates of 100%, 96.67%, and 66.67%, while the DMO’s success rates are 6.67%, 10%, and 0% for the single-, double-, and triple-diode models, respectively.

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“…Because it is gradient-free, simple to create, and performs well, this approach is perfect for dealing with difficult optimization issues. These approaches of problem resolution include SDBT [17,18], PSO [19], sunflower optimization technique [20], MPA [21], social networking searching method [22], butterfly optimizing approach [23], bonobo optimizer [24], HBM [25], etc. In [26], an IGA with a non-uniform mutation was presented for retrieving features from PV models in a trustworthy, exact, and time-efficient way.…”

Section: Introductionmentioning

confidence: 99%

Parameter Identification of Solar Photovoltaic Systems Using an Augmented Subtraction-Average-Based Optimizer

Moustafa

2023

Eng

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Solar photovoltaic system parameter identification is crucial for effective performance management, design, and modeling of solar panel systems. This work presents the Subtraction-Average-Based Algorithm (SABA), a unique, enhanced evolutionary approach for solving optimization problems. The conventional SABA works by subtracting the mean of searching solutions from the position of those in the population in the area of search. In order to increase the search capabilities, this work proposes an Augmented SABA (ASABA) that incorporates a method of collaborative learning based on the best solution. In accordance with manufacturing, the suggested ASABA is used to effectively estimate Photovoltaic (PV) characteristics for two distinct solar PV modules, RTC France and Kyocera KC200GT PV modules. Through the adoption of the ASABA approach, the simulation findings improve the electrical characteristics of PV systems. The suggested ASABA outperforms the regular SABA in terms of efficiency and effectiveness. For the R.T.C France PV system, the suggested ASABA approach outperforms the traditional SABA technique by 90.1% and 87.8 for the single- and double-diode models, respectively. Also, for the Kyocera KC200GT PV systems, the suggested ASABA approach outperforms the traditional SABA technique by 99.1% and 99.6 for the single- and double-diode models, respectively. Furthermore, the suggested ASABA method is quantitatively superior to different current optimization algorithms.

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Enhanced social network search algorithm with powerful exploitation strategy for PV parameters estimation

Cited by 23 publications

References 61 publications

Electrical parameters extraction of PV modules using artificial hummingbird optimizer

Electrical parameters extraction of PV modules using artificial hummingbird optimizer

Dwarf Mongoose Optimizer for Optimal Modeling of Solar PV Systems and Parameter Extraction

Parameter Identification of Solar Photovoltaic Systems Using an Augmented Subtraction-Average-Based Optimizer

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