Modeling and stability analysis methods of neutrosophic transfer functions

Ye, Jun; Cui, Wenhua

doi:10.1007/s00500-019-04434-0

Cited by 27 publications

(7 citation statements)

References 16 publications

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“…Among them, these data set suites were always used by the researchers to detect the efficiency of the evaluation of different unknown parameters for the PV models with more precise test implications. The above three PV modules were used by Alam et al 5 verify the accuracy of the parameters extracted by the evaluation, the I-V characteristic diagrams of the approximate currents simulated by ECPA under the three PV modules ST40, SM55 and KC200GT are plotted by Figures 23,24,and 25. It can be visually seen that the tapped PV parameters make the estimated and measured curves match highly and the ECPA demonstrates a high degree of stability performance. Next, the second set of experiments was conducted, when the irradiation of the control light condition was a constant 1000 W/m 2 , and the ambient temperature was set at 25°C, 40°C, 50°C, and 70°C for comparison experiments.…”

Section: Ecpa Results Through Manufacturer's Datasheetmentioning

confidence: 99%

Extremal Nelder–Mead colony predation algorithm for parameter estimation of solar photovoltaic models

Heidari

Zhang

et al. 2022

Energy Science & Engineering

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Measurement data based on current and voltage of photovoltaic (PV) systems and the establishment of more accurate and stable solar system models are of typical significance for the design, control, evaluation and optimization of PV systems. Accurate and stable parameter evaluation for PV systems needs to be based on more efficient optimization techniques to achieve efficient energy conversion from solar energy. Therefore, this paper proposes a novel and efficient optimization technique enhanced colony predation algorithm to solve the complex PV parameter identification problem named ECPA. By fusing extremal optimization strategy and Nelder-Mead simplex method enables ECPA to further develop in the neighborhood of potential optimal solutions while improving the position of inferior agent candidates, and finally has the ability to search globally beyond the local optimum. To verify the optimization efficiency of ECPA, the first part verifies the efficiency of ECPA in solving complex high-dimensional and multimodal problems by conducting competitive comparison experiments at the IEEE CEC 2020 benchmark case. In the second part, ECPA is compared with nine similar published state-of-the-art algorithms, and competitive tests for PV parameter identification under single diode model, double diode model, triple diode model and PV module model (PV) are conducted. Finally, we focused on three different commercial PV models (thin film ST40, monocrystalline SM55, and multicrystalline KC200GT) to test the accuracy of ECPA in evaluating PV parameters. The test results show that ECPA is able to maintain a high level of accuracy and stability when dealing with commercial PV models in complex environments.The experimental results demonstrate that ECPA outperforms other algorithms in terms of data fitting, stability, convergence speed and convergence accuracy. All the competitive experimental results show that ECPA can be a novel technique with the best performance for identifying the parameters to be determined in solar PV systems.

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Section: Ecpa Results Through Manufacturer's Datasheetmentioning

confidence: 99%

Extremal Nelder–Mead colony predation algorithm for parameter estimation of solar photovoltaic models

Heidari

Zhang

et al. 2022

Energy Science & Engineering

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“…Although SFO, DRM, and FWA explore well, they tend to fall into local optima. To date, multiple improved metaheuristics have been developed to efficiently tackle real-world optimization problems including performance optimization [61], neural network training [62], object tracking [63,64], engineering design problems [65][66][67][68], scheduling problem [69,70], image segmentation [71,72], traveling salesman problem [73], multi-attribute decision making [74][75][76][77], feature selection [78][79][80][81][82], medical data classification [83][84][85][86], bankruptcy prediction [87][88][89], PID optimization control [90][91][92], gate resource allocation [93,94], fault diagnosis of rolling bearings [95,96] and detection of foreign fiber in cotton [97,98].…”

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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“…The supply chain vendor selection problem [50], the dry port location selection challenge in China [51], the thermal power plant location selection problem [52], the solar power plant location selection problem [53], and the wind power plant location selection problem [54] have all recently been solved by utilizing the fuzzy AHP method. Recently, some researchers have utilized neutrosophic functions for applications like scheduling [55][56][57][58] and for security enhancement [59][60][61][62][63][64][65][66]. Pairwise comparisons in fuzzy AHP are made using linguistic variables, which are represented as triangular numbers.…”

Section: Methodsmentioning

confidence: 99%

Human Centered Decision-Making for COVID-19 Testing Center Location Selection: Tamil Nadu—A Case Study

Subbaraj¹,

Madavan²,

Haseena³

et al. 2022

Computational and Mathematical Methods in Medicine

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This paper proposes a blend of three techniques to select COVID-19 testing centers. The objective of the paper is to identify a suitable location to establish new COVID-19 testing centers. Establishment of the testing center in the needy locations will be beneficial to both public and government officials. Selection of the wrong location may lead to lose both health and wealth. In this paper, location selection is modelled as a decision-making problem. The paper uses fuzzy analytic hierarchy process (AHP) technique to generate the criteria weights, monkey search algorithm to optimize the weights, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method to rank the different locations. To illustrate the applicability of the proposed technique, a state named Tamil Nadu, located in India, is taken for a case study. The proposed structured algorithmic steps were applied for the input data obtained from the government of India website, and the results were analyzed and validated using the government of India website. The ranks assigned by the proposed technique to different locations are in aligning with the number of patients and death rate.

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Modeling and stability analysis methods of neutrosophic transfer functions

Cited by 27 publications

References 16 publications

Extremal Nelder–Mead colony predation algorithm for parameter estimation of solar photovoltaic models

Extremal Nelder–Mead colony predation algorithm for parameter estimation of solar photovoltaic models

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

Human Centered Decision-Making for COVID-19 Testing Center Location Selection: Tamil Nadu—A Case Study

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