An efficient Manta Ray Foraging Optimization algorithm for parameter extraction of three-diode photovoltaic model

Houssein, Essam H.; Zaki, Gamela Nageh; Diab, Ahmed A. Zaki; Younis, Eman M. G.

doi:10.1016/j.compeleceng.2021.107304

Cited by 83 publications

(38 citation statements)

References 29 publications

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“…The DDM has two diodes, one series resistance, and one parallel resistance; the second diode represents the recombination effect in the P-N junction [11][12][13][14]. The TDM has three diodes, one series resistance, and one parallel resistance; the third diode represents the effect of leakage current and grain boundaries [15][16][17]. The total estimated parameters for the SDM, DDM, and TDM are five, seven, and nine parameters, respectively.…”

Section: Introductionmentioning

confidence: 99%

Accurate Photovoltaic Models Based on an Adaptive Opposition Artificial Hummingbird Algorithm

et al. 2022

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The greater the demand for energy, the more important it is to improve and develop permanent energy sources, because of their advantages over non-renewable energy sources. With the development of artificial intelligence algorithms and the presence of so many data, the evolution of simulation models has increased. In this research, an improvement to one recent optimization algorithm called the artificial hummingbird algorithm (AHA) is proposed. An adaptive opposition approach is suggested to select whether or not to use an opposition-based learning (OBL) method. This improvement is developed based on adding an adaptive updating mechanism to enable the original algorithm to obtain more accurate results with more complex problems, and is called the adaptive opposition artificial hummingbird algorithm (AOAHA). The proposed AOAHA was tested on 23 benchmark functions and compared with the original algorithm and other recent optimization algorithms such as supply–demand-based optimization (SDO), wild horse optimizer (WHO), and tunicate swarm algorithm (TSA). The proposed algorithm was applied to obtain accurate models for solar cell systems, which are the basis of solar power plants, in order to increase their efficiency, thus increasing the efficiency of the whole system. The experiments were carried out on two important models—the static and dynamic models—so that the proposed model would be more representative of real systems. Two applications for static models have been proposed: In the first application, the AOAHA satisfies the best root-mean-square values (0.0009825181). In the second application, the performance of the AOAHA is satisfied in all variable irradiance for the system. The results were evaluated in more than one way, taking into account the comparison with other modern and powerful optimization techniques. Improvement showed its potential through its satisfactory results in the tests that were applied to it.

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Section: Introductionmentioning

confidence: 99%

Accurate Photovoltaic Models Based on an Adaptive Opposition Artificial Hummingbird Algorithm

et al. 2022

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“…Manta ray foraging optimization (MRFO) is a new swarm intelligence optimization algorithm proposed in 2020. With excellent searchability, fewer parameters, simple model and easily understood, it is better than particle swarm optimization (PSO) [ 11 , 12 ], genetic algorithm (GA) [ 13 , 14 ], Differential Evolution (DE) [ 15 , 16 ], Cuckoo Search (CS) [ 17 ], gravitational search algorithm (GSA) [ 18 ], and ABC [ 19 ] in some function optimization [ 20 ], and it has been successfully applied to solar energy [ 21 , 22 ], ECG [ 23 ], generator [ 24 , 25 ], power system [ 26 ], cogeneration energy system [ 27 ], geophysical inversion problem [ 28 ], directional overcurrent relay [ 29 ], feature selection [ 30 ], hybrid energy system [ 31 ], and sewage treatment [ 32 ]. Although MRFO has good optimization ability, it still has its own defects.…”

Section: Introductionmentioning

confidence: 99%

K-Means Segmentation of Underwater Image Based on Improved Manta Ray Algorithm

Zhu

Xie

Zhou

2022

Computational Intelligence and Neuroscience

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Image segmentation plays an important role in daily life. The traditional K-means image segmentation has the shortcomings of randomness and is easy to fall into local optimum, which greatly reduces the quality of segmentation. To improve these phenomena, a K-means image segmentation method based on improved manta ray foraging optimization (IMRFO) is proposed. IMRFO uses Lévy flight to improve the flexibility of individual manta rays and then puts forward a random walk learning that prevents the algorithm from falling into the local optimal state. Finally, the learning idea of particle swarm optimization is introduced to enhance the convergence accuracy of the algorithm, which effectively improves the global and local optimization ability of the algorithm simultaneously. With the probability that K-means will fall into local optimum reducing, the optimized K-means hold stronger stability. In the 12 standard test functions, 7 basic algorithms and 4 variant algorithms are compared with IMRFO. The results of the optimization index and statistical test show that IMRFO has better optimization ability. Eight underwater images were selected for the experiment and compared with 11 algorithms. The results show that PSNR, SSIM, and FSIM of IMRFO in each image are better. Meanwhile, the optimized K-means image segmentation performance is better.

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“…Due to the cleanliness and availability of solar energy, it has been recognized as a capable renewable energy source [6]. Generation of electricity directly from solar energy is accomplished by photovoltaic (PV) systems [7] and their behaviour is successfully simulated based on several PV models [8,9]. They have applications such as heating and cooling [10], fuel cells [11], cost-effective emission dispatches [12] and water desalination [13].…”

Section: Introductionmentioning

confidence: 99%

Identification of Parameters in Photovoltaic Models through a Runge Kutta Optimizer

et al. 2021

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Recently, the resources of renewable energy have been in intensive use due to their environmental and technical merits. The identification of unknown parameters in photovoltaic (PV) models is one of the main issues in simulation and modeling of renewable energy sources. Due to the random behavior of weather, the change in output current from a PV model is nonlinear. In this regard, a new optimization algorithm called Runge–Kutta optimizer (RUN) is applied for estimating the parameters of three PV models. The RUN algorithm is applied for the R.T.C France solar cell, as a case study. Moreover, the root mean square error (RMSE) between the calculated and measured current is used as the objective function for identifying solar cell parameters. The proposed RUN algorithm is superior compared with the Hunger Games Search (HGS) algorithm, the Chameleon Swarm Algorithm (CSA), the Tunicate Swarm Algorithm (TSA), Harris Hawk’s Optimization (HHO), the Sine–Cosine Algorithm (SCA) and the Grey Wolf Optimization (GWO) algorithm. Three solar cell models—single diode, double diode and triple diode solar cell models (SDSCM, DDSCM and TDSCM)—are applied to check the performance of the RUN algorithm to extract the parameters. the best RMSE from the RUN algorithm is 0.00098624, 0.00098717 and 0.000989133 for SDSCM, DDSCM and TDSCM, respectively.

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An efficient Manta Ray Foraging Optimization algorithm for parameter extraction of three-diode photovoltaic model

Cited by 83 publications

References 29 publications

Accurate Photovoltaic Models Based on an Adaptive Opposition Artificial Hummingbird Algorithm

Accurate Photovoltaic Models Based on an Adaptive Opposition Artificial Hummingbird Algorithm

K-Means Segmentation of Underwater Image Based on Improved Manta Ray Algorithm

Identification of Parameters in Photovoltaic Models through a Runge Kutta Optimizer

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