Aiding Prosumers by Solar Cell Parameter Optimization Using a Hybrid Technique for Achieving Near Realistic P-V Characteristics

Zain‐ul‐Abdin,; Mahmood, Tahir; Xiong, Naixue; Mehmood, Raja Majid

doi:10.1109/access.2020.3043941

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…Cervellini et al [23]- [24], introduced a novel Genetic Algorithm (GA) to transform the conventional data into designed data, applies to various Kelvins and irradiation (G) zones [25]- [26]. The proposed GA approach is very simple and eliminates the complexity of evaluation, i.e., describes I-V curve and other related equations in a transparent and uncomplicated way [27]- [28].…”

Section: Introductionmentioning

confidence: 99%

Analysis and Implementation of Robust Metaheuristic Algorithm to Extract Essential Parameters of Solar Cell

Arandhakar

Chaudhary²,

Depuru³

et al. 2022

IEEE Access

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Optimization complications are solved using meta-heuristic methods, which transforms the complex data into simplest way and computational process is quite attractive because of their intensification, diversification, and accurate evaluating/computational behavior on the nonlinear data. Among different existing meta-heuristic algorithms, Tabu Search Optimization (TSO) algorithms have robust performance due to escaping strategy from local optimum and extensive extraction ability. Hence, this paper delineates the TSO searching technique for extracting the parasitic parameters of solar Photovoltaic (PV) modules under different climatic stipulations. Double diode design is developed and implemented in the operational aspect based on the extraction issue. The six parameters of the solar cell, i.e., I Ph , I 01 , I 02 , R S , R P , a 1 are emulated and the obtained data is investigated using TSO approach, at the same time the extracted data is compared with the effective selected metaheuristic algorithms such as Gravitational Search Algorithm (GSA), Lightning Search Algorithm (LSA), Pattern Search (PS), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Compared to the existing algorithms, TSO has very less computational time to extract all the six parameters.

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Section: Introductionmentioning

confidence: 99%

Analysis and Implementation of Robust Metaheuristic Algorithm to Extract Essential Parameters of Solar Cell

Arandhakar

Chaudhary²,

Depuru³

et al. 2022

IEEE Access

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Personalized PV system recommendation for enhanced solar energy harvesting using deep learning and collaborative filtering

Jbene,

Saadane,

Tigani

et al. 2023

Sustainable Energy Technologies and Assessments

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Performance Assessment of Heuristic Genetic Algorithm (HGA) for Electrochemical Impedance Spectroscopy Parameter Estimation

Pech-Rodríguez¹,

Suárez-Velázquez²,

Armendáriz-Mireles³

et al. 2023

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Due to the importance of cutting-edge nanomaterials applications in energy generation and storage devices, electrochemical impedance spectroscopy (EIS) has been adopted to fully understand the electronic and chemical reactions occurring inside these emerging technologies. Electronic behavior can be correlated with electrochemical properties such as electron transfer resistance, rate of mass diffusion, and the number of electrons in the electrochemical reaction. Although there is a lot of information about the electronic diagrams and methods for parameter estimation, some readers have difficulty analyzing and interpreting EIS curves. Thus, this work proposed using a heuristic approach and genetic algorithms to successfully estimate the resistance and capacitance value of a previously defined circuit model. To assess the potential of the genetic algorithm in electrochemical parameters estimation, we carried out practical measurements with known elements, and then the experimental and theoretical values were compared. Furthermore, the versatility and effectiveness of the algorithm were validated by determining the parameters in an Li-ion battery. The results revealed that the heuristic genetic algorithm (HGA) is a powerful tool for EIS parameters estimation because it can handle large below and upper limits with more pragmatic results in a shorter computational time.

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Aiding Prosumers by Solar Cell Parameter Optimization Using a Hybrid Technique for Achieving Near Realistic P-V Characteristics

Cited by 3 publications

References 28 publications

Analysis and Implementation of Robust Metaheuristic Algorithm to Extract Essential Parameters of Solar Cell

Analysis and Implementation of Robust Metaheuristic Algorithm to Extract Essential Parameters of Solar Cell

Personalized PV system recommendation for enhanced solar energy harvesting using deep learning and collaborative filtering

Performance Assessment of Heuristic Genetic Algorithm (HGA) for Electrochemical Impedance Spectroscopy Parameter Estimation

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