Evolutionary shuffled frog leaping with memory pool for parameter optimization

Liu, Yun; Heidari, Ali Asghar; Ye, Xiaojia; Chen, Chi; Zhao, Xuehua; Ma, Chao; Turabieh, Hamza; Chen, Huiling; Le, Rongrong

doi:10.1016/j.egyr.2021.01.001

Cited by 37 publications

(10 citation statements)

References 145 publications

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“…Few studies employed Lambert W function [87]- [89], NR [61], [90], f-solve [91], Taylor series [92], Levenberg Marquardt [17], Bezier Curve [93], and least square nonlinear curve fitting method (lsqcurvefit function) [94]. On the other hand, the nonlinear and multi-variable PV model equation is generally solved linearly [10], [29], [34], [37], [50], [83], [95], [96], showing a theoretical gap in this field. The downsides of these methods are that some of them need a considerable execution time and cannot properly imitate the experimental current especially when the number of the experimental data contains a large number of data points, hard edges, and noises.…”

Section: Introductionmentioning

confidence: 99%

A Novel Theoretical and Practical Methodology for Extracting the Parameters of the Single and Double Diode Photovoltaic Models

et al. 2022

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Section: Introductionmentioning

confidence: 99%

A Novel Theoretical and Practical Methodology for Extracting the Parameters of the Single and Double Diode Photovoltaic Models

et al. 2022

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“…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. In [48], the performance of an off‐grid PV‐micro wind‐battery hybrid system has been experimentally assessed under real outdoor climatic conditions.…”

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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“…Recently, various attempts have been made to improve the parameters of PV models using modified evolutionary algorithms (EAs) and hybrid meta-heuristic approaches. For instance, a chaotic whale optimization algorithm (CWOA) 43 ; a hybrid of firefly and pattern search algorithm 44 ; a logistic chaotic JAYA algorithm (LCJAYA) 7 ; a novel hybrid algorithm, namely, fractional chaotic ensemble particle swarm optimizer (FC-EPSO) 45 ; random reselection particle swarm optimization (PSOCS) 46 ; shuffled frog leading algorithms (SFLA) [47][48][49] ; artificial electric field algorithm (AEF) 50 ; marine predators algorithm (MPA) 51 ; slime mould algorithm (SMA) 52 ; spherical evolution algorithm (SE) 53,54 ; Harris hawks optimization (HHO) 4,[55][56][57] ; coyote optimization algorithm (COA) 58 ; and a novel hybrid biogeography-based optimization (BBO) and cuckoo search (CS) 59 was provided to identify the parameters of PV cells and panels. In recent years, Chen et al 60 proposed a hybridized teaching-learning-based optimization (TLBO) with the artificial bee colony (ABC) for accurately estimating the parameters of different PV systems.…”

Section: Introductionmentioning

confidence: 99%

Adaptive slime mould algorithm for optimal design of photovoltaic models

Lin

Ahmadianfar²,

Golilarz

et al. 2022

Energy Science & Engineering

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Solar energy is becoming more popular as it is a clean source of electricity. The design of photovoltaic (PV) cells has therefore captivated experts worldwide.The two key issues are the lack of an excellent model to define solar cells and the lack of data regarding PV cells. This scenario even impacts solar module performance (panels). The behavior of solar cells is described by the current versus voltage. Considering these values, the design challenge entails solving complicated nonlinear multimodal objectives. Different methods to figure out the parameters of the PV cells and panels have been suggested. They do not come up with the best solutions most of the time. Hence, a powerful and reliable optimizer is needed to derive the optimal parameters of these models.To this end, this study has developed an adaptive slime mould algorithm (ASMA) as a robust and precise optimization method. To implement the ASMA, four improvements are proposed: (1) a trigonometric-based mutation and a double-based best mutation are introduced to promote the global and local search; (2) a suitable mechanism to adaptively select the control parameters of the SMA; (3) a local escaping strategy; (4) an opposition-based learning operator to improve the best solution. The ASMA is employed to derive optimal parameters of PV models and assessed utilizing a total number of eight well-known optimization algorithms. The findings show that the ASMA is very competitive in terms of accuracy and convergence speed and that this is supported by a wealth of evidence. As a result, when assessing the parameters of the PV model, ASMA is a very efficient and robust optimizer.

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Evolutionary shuffled frog leaping with memory pool for parameter optimization

Cited by 37 publications

References 145 publications

A Novel Theoretical and Practical Methodology for Extracting the Parameters of the Single and Double Diode Photovoltaic Models

A Novel Theoretical and Practical Methodology for Extracting the Parameters of the Single and Double Diode Photovoltaic Models

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

Adaptive slime mould algorithm for optimal design of photovoltaic models

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