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Comparative Study of Parameter Extraction from a Solar Cell or a Photovoltaic Module by Combining metaheuristic algorithms with Different Simulation Current Calculation Methods

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ENERGIES 2024年第10期17卷 2284页

作者： Qin, Cheng Li, Jianing Yang, Chen Ai, Bin Zhou, Yecheng Sun Yat Sen Univ Sch Mat Sci & Engn Guangzhou 510006 Peoples R China Sun Yat Sen Univ Guangdong Prov Key Lab Photovolta Technol Guangzhou 510006 Peoples R China

In this paper, single-diode model (SDM) and double-diode model (DDM) parameters of the French RTC solar cell and the Photowatt PWP 201 photovoltaic (PV) module were extracted by combining five metaheuristic algorithms with three simulation current calculation methods (i.e., approximation method, Lambert W method and Newton-Raphson method), respectively. It was found that the parameter-extraction accuracies of the Lambert W (LW) method and the Newton-Raphson (NR) method are always approximately equal and higher than that of the approximation method. The best RMSEs (root mean square error) obtained by using the LW or the NR method on the solar cell and the PV module are 7.72986 x 10-4 and 2.05296 x 10-3 for SDM parameter extraction and 6.93709 x 10-4 and 1.99051 x 10-3 for DDM parameter extraction, respectively. The latter may be the highest parameter-extraction accuracy reported on the solar cell and the PV module so far, which is due to the adoption of more reasonable DDM parameter boundaries. Furthermore, the convergence curves of the LW and the NR method basically coincide, with a convergence speed faster than that of the approximation method. The robustness of a parameter-extraction method is mainly determined by the metaheuristic algorithm, but it is also affected by the simulation current calculation method and the parameter-extraction object. In a word, the approximation method is not suitable for application in PV-model parameter extraction because of incorrect estimation of the simulation current and the RMSE, while the LW and NR methods are suitable for the application for accurately calculating the simulation current and RMSE. In terms of saving computation resources and time, the NR method is superior to the LW method.

关键词： parameter estimation Lambert W function Newton-Raphson method metaheuristic algorithms

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2-D DOA Estimation in a Cuboid Array Based on metaheuristic algorithms and Maximum Likelihood

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International Journal of Communications, Network and System Sciences 2020年第8期13卷 121-137页

作者： Gilberto Lopes Filho Ana Cláudia Barbosa Rezende Lucas Fiorini Cruz Flávio Henrique Teles Vieira Rodrigo Pinto Lemos Universidade Federal de Goiás (UFG) Goiánia Brazil

This paper proposes to apply the genetic algorithm and the firefly algorithm to enhance the estimation of the direction of arrival (DOA) angle of electromagnetic signals of a smart antenna array. This estimation is essential for beamforming, where the antenna array radiating pattern is steered to provide faster and reliable data transmission with increased coverage. This work proposes using metaheuristics to improve a maximum likelihood DOA estimator for an antenna array arranged in a uniform cuboidal geometry. The DOA estimation performance of the proposed algorithm was compared to that of MUSIC on different two dimensions scenarios. The metaheuristic algorithms present better performance than the well-known MUSIC algorithm.

关键词： metaheuristic algorithms Genetic Algorithm Firefly Algorithm DOA Estimation Maximum Likelihood

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BIM-based Applications of metaheuristic algorithms to Support the Decision-making Process: Uses in the Planning of Construction Site Layout

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Procedia Engineering 2017年 196卷 558-564页

作者： Roya Amiri Javad Majrouhi Sardroud Borja García de Soto Ph.D. Researcher Department of Civil & Construction Engineering Central Tehran Branch Islamic Azad University Tehran Iran Assistant Professor Department of Civil & Construction Engineering Central Tehran Branch Islamic Azad University Tehran Iran Senior Research Associate and Lecturer Institute of Construction and Infrastructure Management ETH Zurich Switzerland

The use of Building Information Modeling (BIM) is affecting the way in which construction projects are planned, designed, executed and operated. One of the main goals of BIM is to provide, based on a 3D model, an accurate information model in a digital format to give different project participants better tools when evaluating different options to support their decisions about the project at a given phase. With this in mind, BIM provides a suitable framework to support the decision-makings process by aggregating the necessary information at the right time, and clarifying details and existing conditions; however, the different elements required to make an optimized decision need additional consideration. To address that, this paper explores the value of metaheuristic algorithms for reaching an optimized solution. The use of metaheuristic algorithms is well known in various aspects of knowledge optimization. This paper provides an overall review of the applications of metaheuristic algorithms in BIM-based optimized decisions in the construction industry and focuses on applications to the planning of construction site layout. Based on the findings from this paper, research gaps in this area have been identified and suggestions for future research to address those gaps are suggested.

关键词： Building Information Modeling metaheuristic algorithms Optimization Site Layout

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Forecasting urban water demand using different hybrid-based metaheuristic algorithms' inspire for extracting artificial neural network hyperparameters

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SCIENTIFIC REPORTS 2024年第1期14卷 1-17页

作者： Zubaidi, Salah L. Al-Bugharbee, Hussein Alattabi, Ali W. Ridha, Hussein Mohammed Hashim, Khalid Al-Ansari, Nadhir Yaseen, Zaher Mundher Wasit Univ Dept Civil Engn Wasit 52001 Iraq Univ Warith Al Anbiyaa Coll Engn Karbala 56001 Iraq Wasit Univ Dept Mech Engn Wasit 52001 Iraq Univ Putra Malaysia Fac Engn Dept Elect & Elect Engn Adv Lightning Power & Energy Res ALPER Serdang 43400 Malaysia Mustansiriyah Univ Dept Comp Engn Baghdad Iraq Univ Babylon Dept Environm Engn Al Hillah 51001 Iraq Liverpool John Moores Univ Sch Civil Engn & Built Environm Liverpool England Lulea Univ Technol Dept Civil Environm & Nat Resources Engn S-97187 Lulea Sweden King Fahd Univ Petr & Minerals Civil & Environm Engn Dept Dhahran 31261 Saudi Arabia

This research offers a novel methodology for quantifying water needs by assessing weather variables, applying a combination of data preprocessing approaches, and an artificial neural network (ANN) that integrates using a genetic algorithm enabled particle swarm optimisation (PSOGA) algorithm. The PSOGA performance was compared with different hybrid-based metaheuristic algorithms' behaviour, modified PSO, and PSO as benchmarking techniques. Based on the findings, it is possible to enhance the standard of initial data and select optimal predictions that drive urban water demand through effective data processing. Each model performed adequately in simulating the fundamental dynamics of monthly urban water demand as it relates to meteorological variables, proving that they were all successful. Statistical fitness measures showed that PSOGA-ANN outperformed competing algorithms.

关键词： Urban water management ANN PSOGA metaheuristic algorithms Water demand prediction

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Reliability assessment of truss structures with natural frequency constraints using metaheuristic algorithms

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JOURNAL OF BUILDING ENGINEERING 2020年 28卷

作者： Vaez, Seyed Rohollah Hoseini Mehanpour, Hashem Fathali, Mohammad Ali Univ Qom Fac Engn Dept Civil Engn Qom Iran

The failure probability (reliability index) and evaluation of safety of structures is a subject of interest in structural engineering. In order to calculate the reliability index, moment methods require an explicit mathematical form of the limit state function and gradient of this function, and simulation methods are dependent on a large number of evaluations of this function. In this study, in order to reduce computational efforts, the optimization problem is formulated so it can calculate the reliability index for structural problems with an implicit limit-state function. For this purpose, the objective function is formulated based on the Hasofer and Lind method, and the limit state function is defined based the first mode frequency. The random variables consist of modulus of elasticity, material density, non-structural mass, and cross-sectional area. To evaluate accuracy of the proposed approach in estimating the reliability index, four truss structures are selected and their reliability index is calculated by using six meta-heuristic algorithms including WEO, AWEO, CBO, ECBO, VPS, and EVPS. Compared with the Monte Carlo simulation method, the proposed approach shows acceptable performance.

关键词： Reliability index Truss structures Frequency constraints Failure probability metaheuristic algorithms Optimization

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Predicting longitudinal dispersion coefficient using ANN with metaheuristic training algorithms

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INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY 2017年第11期14卷 2399-2410页

作者： Alizadeh, M. J. Shabani, A. Kavianpour, M. R. KN Toosi Univ Technol Fac Civil Engn Tehran Iran

A reliable prediction of dispersion coefficient can provide valuable information for environmental scientists and river engineers as well. The main objective of this study is to apply intelligence techniques for predicting longitudinal dispersion coefficient in rivers. In this regard, artificial neural network (ANN) models were developed. Four different metaheuristic algorithms including genetic algorithm (GA), imperialist competitive algorithm (ICA), bee algorithm (BA) and cuckoo search (CS) algorithm were employed to train the ANN models. The results obtained through the optimization algorithms were compared with the Levenberg-Marquardt (LM) algorithm (conventional algorithm for training ANN). Overall, a relatively high correlation between measured and predicted values of dispersion coefficient was observed when the ANN models trained with the optimization algorithms. This study demonstrates that the metaheuristic algorithms can be successfully applied to make an improvement on the performance of the conventional ANN models. Also, the CS, ICA and BA algorithms remarkably outperform the GA and LM algorithms to train the ANN model. The results show superiority of the performance of the proposed model over the previous equations in terms of DR, R (2) and RMSE.

关键词： Cuckoo search Longitudinal dispersion Neural networks Rivers metaheuristic algorithms

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Comparison of metaheuristic optimization algorithms with a new modified deb feasibility constraint handling technique

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2021年第7期29卷 3270-3289页

作者： Cimen, Murat Erhan Garip, Zeynep Boz, Ali Fuat Sakarya Univ Appl Sci Fac Technol Dept Elect & Elect Engn Sakarya Turkey Sakarya Univ Appl Sci Fac Technol Dept Comp Engn Sakarya Turkey

In this study, the modification of the Deb feasibility method is considered to solve the constrained optimization problems. In the developed modified Deb feasibility constraint method, the third rule in its procedure was revised in order to increase the performance of the Deb feasibility constraint handling method. The innovation in the method is based on generating a new individual by using both possible solutions that violate the constraints in the method used for solving the problem. In detail, discussions were given about the application and usefulness of six constrained handling techniques. Furthermore, genetic algorithm, particle swarm optimization, Harris hawks optimization, whale optimization algorithm, grey wolf optimization and sine cosine algorithms were applied to both various benchmark functions and also different engineering application problems such as pressure vessel design, welded beam design, speed reducer design and active filter design. Overall the experimental results show that modified Deb feasibility constraint handling technique is more robust and efficient than Deb feasibility technique and most of the other constraint handling techniques.

关键词： Constraint handling methods metaheuristic algorithms Deb feasibility

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Benchmark of proton exchange membrane fuel cell parameters extraction with metaheuristic optimization algorithms

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ENERGY 2019年 183卷 912-925页

作者： Kandidayeni, M. Macias, A. Khalatbarisoltani, A. Boulon, L. Kelouwani, S. Univ Quebec Trois Rivieres Hydrogen Res Inst Dept Elect Engn & Comp Sci Trois Rivieres PQ GSA 5H7 Canada Canada Res Chair Energy Sources Vehicles Future Trois Rivieres PQ Canada Univ Quebec Trois Rivieres Dept Mech Engn Hydrogen Res Inst Trois Rivieres PQ GSA 5H7 Canada

Proton exchange membrane fuel cell (PEMFC) models are multivariate with different nonlinear elements which should be identified accurately to assure dependable modeling. metaheuristic algorithms are perfect candidates for this purpose since they do an informed search for finding the parameters. This paper utilizes three algorithms, namely shuffled frog-leaping algorithm (SFLA), firefly optimization algorithm (FOA), and imperialist competitive algorithm (ICA) for the PEMFC model calibration. In this regard, firstly, the algorithms are employed to find the parameters of a benchmark PEMFC model by minimizing the sum of squared errors (SSE) between the measured and estimated voltage for two available case studies in the literature. After conducting 100 independent runs, the algorithms are compared in terms of the best and the worst SSEs, the variance, and standard deviation. This comparison indicates that SFLA marginally outperforms ICA and FOA regarding the best SSE in both cases while it performs 20% and twofold better than other algorithms concerning the worst SSE. Furthermore, the obtained variance and standard deviation by SFLA are much less than the other algorithms showing the precision and repeatability of this method. Finally, SFLA is used to calibrate the model for a new case study (Horizon 500-W PEMFC) with variable temperature. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： metaheuristic algorithms Shuffled frog-leaping algorithm PEMFC Semi-empirical modeling

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Detection ofSalmonella Typhimuriumcontamination levels in fresh pork samples using electronic nose smellprints in tandem with support vector machine regression and metaheuristic optimization algorithms

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JOURNAL OF FOOD SCIENCE AND TECHNOLOGY-MYSORE 2021年第10期58卷 3861-3870页

作者： Bonah, Ernest Huang, Xingyi Yang Hongying Aheto, Joshua Harrington Ren Yi Yu, Shanshan Tu, Hongyang Jiangsu Univ Sch Food & Biol Engn Xuefu Rd 301 Zhenjiang 212013 Jiangsu Peoples R China Food & Drugs Author Lab Serv Dept POB CT 2783 Cantonments Accra Ghana Suzhou Polytech Inst Agr Sch Smart Agr XiYuan Rd 279 Suzhou 215000 Peoples R China

Rapid detection and quantification of bacterial foodborne pathogens are crucial in reducing the incidence of diseases associated with meat products contaminated with pathogens. For the identification, discrimination and quantification ofSalmonella Typhimuriumcontamination in pork samples, a commercial electronic nose with ten (10) metal oxide semiconductor sensor array is applied. Principal component analysis was successfully applied for discrimination of inoculated samples and inoculated samples at different contaminant levels. Support vector machine regression (SVMR) together with a metaheuristic framework using genetic algorithm (GA), particle swarm optimization (PSO), and grid searching (GS) optimization algorithms were applied for ***. Although SVMR results were satisfactory, SVMR hyperparameter tuning (c and g) by PSO, GA and GS showed superior performance of the models. The order of the prediction accuracy based on the prediction set was GA-SVMR (RP2 = 0.989;RMSEP = 0.137;RPD = 14.93) > PSO-SVMR (RP2 = 0.986;RMSEP = 0.145;RPD = 14.11) > GS-SVMR (RP2 = 0.966;RMSEP = 0.148;RPD = 13.82) > SVMR (RP2 = 0.949;RMSEP = 0.162;RPD = 12.63). GA-SVMR's proposed approach was fairly more effective and retained an excellent prediction accuracy. A clear relationship was identified between odor analysis results, and reference traditional microbial test, indicating that the electronic nose is useful for accurate microbial volatile organic compound evaluation in the quantification of *** a food matrix.

关键词： Salmonella Foodborne pathogens Electronic nose Chemometric algorithms Longissimus pork muscle metaheuristic algorithms

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A Comparative Study on Accurate Parameter Estimation of Solar Photovoltaic Models Using metaheuristic Optimization algorithms

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2024年第7期52卷 1001-1021页

作者： Yesilbudak, Mehmet Nevsehir Haci Bektas Veli Univ Fac Engn & Architecture Dept Elect & Elect Engn Nevsehir Turkiye Nevsehir Haci Bektas Veli Univ Fac Engn & Architecture Dept Elect & Elect Engn TR-50300 Nevsehir Turkiye

Fossil fuel prices are increasing day by day due to growing demand and resource limitations. This situation shifts conventional electricity generation to renewable power generation. Among renewables, solar photovoltaic (PV) energy is very promising as the third fastest growing one. However, PV system modeling requires the estimation of unknown parameters of PV cells and modules. This problem remains a compelling task due to its multi-dimensional, multi-model and non-linear characteristics. In order to contribute to the solution of this optimization problem, this work makes a detailed comparison of artificial hummingbird algorithm, artificial rabbits optimization, enhanced Jaya algorithm, flow direction algorithm and artificial gorilla troops optimizer for determining the unknown parameters of PV models. Experimental results demonstrate that artificial hummingbird algorithm in single diode modeling of PV cell, flow direction algorithm in double diode modeling of PV cell and artificial rabbits optimization in single diode modeling of PV module are found to be capable of estimating accurate and efficient design coefficients for PV systems. In addition, the mentioned metaheuristic algorithms show reasonable convergence characteristics for the corresponding PV models.

关键词： Comparison metaheuristic algorithms parameter estimation photovoltaic system photovoltaic models

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