The manufacturers of photovoltaic (PV) panel give the data of three major points on I-V characteristics. However, this information alone is not sufficient to derive the five parameter and seven parameter models, i.e.,...
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The manufacturers of photovoltaic (PV) panel give the data of three major points on I-V characteristics. However, this information alone is not sufficient to derive the five parameter and seven parameter models, i.e., single-diode and double-diode models. Hence, several population-based metaheuristic techniques are proposed in the literature. However, there is a need of well-balanced algorithm which is used for extracting the parameters of the diode model of PV panels from the datasheet. In this paper, a novel hybrid algorithm is proposed by combing the best features of a recently developed Marine Predators algorithm (MPA) and Success History based Adaptive Differential Evolution (shade) algorithm. The principal algorithm for obtaining the optimum solution is MPA. However, during the search process to enhance the best solution region, self-adaptive DE based on the successive history of parameters is used. The derived objective function ensures the zero error at three important points of the I-V characteristics. Hence, the parameters extracted by using proposed method results in the I-V curves which are passing through the all three important points. Only three parameters out of five in single-diode model, and five parameters out of seven are optimized with the proposed algorithm and remaining are calculated analytically to reduce the burden on metaheuristic algorithm. MATLAB programming is used to test the proposed parameter extraction by using hybrid Marine Predators - Success History based Adaptive Differential Evolution (MP-shade) algorithm, and the results are compared with the other state-of-the-art metaheuristic techniques. The single-diode and double-diode models of three types of panels (monocrystalline, polycrystalline, and thin-film) are derived by using MP-shade algorithm.
In this study, a new method for identifying and characterizing straight cracks in plate-like structures is presented. The method combines the finite element method (FEM) using the software Abaqus and the success histo...
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In this study, a new method for identifying and characterizing straight cracks in plate-like structures is presented. The method combines the finite element method (FEM) using the software Abaqus and the success history-based adaptive differential evolution algorithm (shade). The objective of the method is to minimize the mean relative error between the measured experimental frequencies of a plate with an unknown crack identity and the numerical frequencies obtained using the shade-FEM approach. The crack identity is defined by its length, orientation, and centre *** validate the effectiveness of the proposed approach, two strategies are applied. In the first strategy, the inverse problem is solved using the natural frequencies of a plate with a known crack identity obtained through modal simulation in Abaqus. In the second strategy, the experimental frequencies of a cracked plate are *** results of the study demonstrate that the proposed approach achieves promising results with just a population size of 25 and 150 iterations. The outcomes show high accuracy, as indicated by a relative error of the objective function below 0.8%. Overall, the study demonstrates the effectiveness of using the shade-FEM approach for identifying and characterizing straight cracks in plate-like structures, offering potential applications in various engineering and structural integrity fields.
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