Parameter identification of solar photovoltaic cell and module models via supply demand optimizer

Shaheen, Abdullah M.; El-Seheimy, Ragab A.; Xiong, Guojiang; Elattar, Ehab E.; Ginidi, Ahmed

doi:10.1016/j.asej.2022.101705

Cited by 38 publications

(16 citation statements)

References 65 publications

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“…In studies [ 35 , 36 ], the ML parameter identification models for SDM provided good performance. There are many heuristic search algorithms, including bioinspired, that were adapted to solve the parameter identification task of the different solar cell models [ 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ]. Table 4 displays a brief comparison of the parameter identification models from studies [ 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ].…”

Section: Machine Learning Applications For a Solar Plant Systemmentioning

confidence: 99%

A Review on Machine Learning Applications for Solar Plants

Engel

2022

Sensors

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A solar plant system has complex nonlinear dynamics with uncertainties due to variations in system parameters and insolation. Thereby, it is difficult to approximate these complex dynamics with conventional algorithms whereas Machine Learning (ML) methods yield the essential performance required. ML models are key units in recent sensor systems for solar plant design, forecasting, maintenance, and control to provide the best safety, reliability, robustness, and performance as compared to classical methods which are usually employed in the hardware and software of solar plants. Considering this, the goal of our paper is to explore and analyze ML technologies and their advantages and shortcomings as compared to classical methods for the design, forecasting, maintenance, and control of solar plants. In contrast with other review articles, our research briefly summarizes our intelligent, self-adaptive models for sizing, forecasting, maintenance, and control of a solar plant; sets benchmarks for performance comparison of the reviewed ML models for a solar plant’s system; proposes a simple but effective integration scheme of an ML sensor solar plant system’s implementation and outlines its future digital transformation into a smart solar plant based on the integrated cutting-edge technologies; and estimates the impact of ML technologies based on the proposed scheme on a solar plant value chain.

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Section: Machine Learning Applications For a Solar Plant Systemmentioning

confidence: 99%

A Review on Machine Learning Applications for Solar Plants

Engel

2022

Sensors

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“…Meantime, improved Rao-1 (IRao-1) algorithm [50], comprehensive learning Rao-1 optimization (CLRao-1) [51], and Rao-2 (R-II) and Rao-3 (R-III) algorithm [52] were proposed to estimate the PV cell parameters. Moreover, the supply-demand-based optimization algorithm (SDOA) [53], reinforcement learning technique [54], teachinglearning-based optimization (STLBO) [55], and honey badger algorithm (HBA) [56] were also employed to extract the unknown parameters of PV models.…”

Section: Introductionmentioning

confidence: 99%

A Level‐Based Learning Swarm Optimizer with Stochastic Fractal Search for Parameters Identification of Solar Photovoltaic Models

Zhang

Shu

et al. 2023

Mathematical Problems in Engineering

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As the most popular renewable energy, solar energy could be converted into electricity by photovoltaic (PV) systems directly. To maximize the effectiveness of the conversion, it is critical to find the precise and accurate parameters of the PV model. In this paper, we propose a level-based learning swarm optimizer with stochastic fractal search (LLSOF) to tackle the parameter estimation of several kinds of solar PV models. The population is separated into multiple levels according to their fitness at first. The individuals at the lower levels evolve through learning from the individuals at the higher levels. Benefiting from the interactive learning among levels, the population could approach the multiple optimal regions rapidly. To enhance the local search ability, stochastic fractal search is introduced to locate the optima accurately. Combination of both, the proposed LLSOF could achieve a good balance on both exploration and exploitation. To evaluate the performance of LLSOF, it is used to obtain the parameters of three PV models and compared with nine well-established algorithms. Comparative results validate the excellent performance of LLSOF. Moreover, the application manufactory’s data sheets report the superior efficiency and effectiveness of LLSOF for the parameter estimation of PV systems.

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“…The analytical computation technique has computed four parameters using equations and the manufacturers data sheet of PV modules, whereas the remaining five parameters have been identified using the HHO algorithm. In addition to this, the supply demand optimizer (SDO) has been demonstrated in [43] to assess the PV parameters of SDM, DDM and TDM of PV modules and some other modules have been tested by SDO in [44] to extract the PV parameters under real outdoor climatic conditions. Moreover, triple-phase teaching-learning-based optimization [45], coyote optimization algorithm [46], evolutionary shuffled frog leaping [47] have been used to assess the PV parameters of SDM, DDM and TDM of PV modules.…”

Section: Introductionmentioning

confidence: 99%

Estimation of electrical parameters of photovoltaic panels using heap‐based algorithm

Ginidi

Shaheen

El‐Sehiemy

et al. 2022

IET Renewable Power Gen

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Parameter estimation for solar photovoltaic (PV) models is a challenging issue due to the complex nonlinear multivariable of the current-voltage and power-voltage characteristics. In this article, an improved heap-based algorithm (IHBA) is proposed to improve the performance of a recently published algorithm called heap-based algorithm (HBA). The HBA's performance is enhanced by applying an effective exploitation feature to boost the searching around the leader position with the goal of enhancing its global search capabilities and avoiding becoming trapped in a local optimum. The proposed IHBA and the standard HBA are developed considering the practical limits of the electrical parameters of PV models to minimize the root mean square error (RMSE) between the experimental and simulated results. The numerical analyses for the PVM-752GaAs PV module including single-diode model (SDM), double-diode model (DDM) and triple-diode model (TDM) are investigated to estimate five, seven, and nine electrical parameters of these models, respectively. Besides, different recent optimization techniques are simulated with fair comparisons, which are forensic-based investigation (FBI), equilibrium optimizer (EO), Jellyfish Search (JFS), HBA, Marine Predator Algorithm (MPA) and Enhanced MPA (EMPA), and are compared with the proposed IHBA. Several separate runs are illustrated for the proposed IHBA in comparison with others, whereas the whisker box plot and T-tests are activated to evaluate their effectiveness metrics. The simulation results derive higher superiority of the proposed IHBA with the minimum RMSE objective and standard deviation of (0.000228 and 3.7 × 10 6 ), (0.000184 and 1.93 × 10 5 ) and (0.000017 and 2.11 × 10 5 ) for SDM, DDM, and TDM, respectively, with respect to the standard HBA, other recent and reported optimization techniques. Additionally, the P-indicator is applied in this study to illustrate the evidence for the alternative hypothesis. Small values of the Pindicator for the proposed IHBA are obtained compared to the standard HBA and other recent optimization techniques, where IHBA achieves p-value of 1.0336 × 10 17 , 4.037 7× 10 11 and 2.1490 × 10 10 for SDM, DDM and TDM, respectively. The H-value is always one which elucidates that the hypothesis probability is not more than 5% for all algorithms for SDM, DDM and TDM. Moreover, the proposed IHBA demonstrates higher efficiency as it provides the first ranks in the confidence interval values compared with other algorithms. Furthermore, the proposed IHBA is significantly employed for the SQ_150 PV module considering the TDM with diverse solar irradiance and temperatures, where significant closeness between the emulated and experimental P-V curves is evidently demonstrated.

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Parameter identification of solar photovoltaic cell and module models via supply demand optimizer

Cited by 38 publications

References 65 publications

A Review on Machine Learning Applications for Solar Plants

A Review on Machine Learning Applications for Solar Plants

A Level‐Based Learning Swarm Optimizer with Stochastic Fractal Search for Parameters Identification of Solar Photovoltaic Models

Estimation of electrical parameters of photovoltaic panels using heap‐based algorithm

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