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Jyoti Gupta, P. Nijhawan, Souvik Ganguli (2021)
Parameter Estimation of Fuel Cell Using Chaotic Mayflies Optimization AlgorithmAdvanced Theory and Simulations, 4
T. Easwarakhanthan, J. Bottin, I. Bouhouch, C. Boutrit (1986)
Nonlinear Minimization Algorithm for Determining the Solar Cell Parameters with MicrocomputersInternational Journal of Solar Energy, 4
Jyoti Gupta, P. Nijhawan, Souvik Ganguli (2021)
Optimal parameter estimation of PEM fuel cell using slime mould algorithmInternational Journal of Energy Research, 45
M. Khan, M. Iqbal (2005)
Modelling and Analysis of Electro‐chemical, Thermal, and Reactant Flow Dynamics for a PEM Fuel Cell SystemFuel Cells, 5
G. Tina, C. Ventura (2014)
Simulation tool for energy management of photovoltaic systems in electric vehiclesEnergy Conversion and Management, 78
A. Vos, Aleksandra Szymańska, V. Badescu (2009)
Modelling of solar cells with down-conversion of high energy photons, anti-reflection coatings and light trappingEnergy Conversion and Management, 50
M. Singla, P. Nijhawan, A. Oberoi (2021)
Parameter estimation of proton exchange membrane fuel cell using a novel meta-heuristic algorithmEnvironmental Science and Pollution Research, 28
M. Singla, P. Nijhawan, A. Oberoi (2019)
Solar-PV & Fuel Cell Based Hybrid Power Solution for Remote LocationsInternational Journal of Engineering and Advanced Technology
Gaurav Dhiman, M. Garg, A. Nagar, Vijay Kumar, M. Dehghani (2020)
A novel algorithm for global optimization: Rat Swarm OptimizerJournal of Ambient Intelligence and Humanized Computing, 12
International Transactions on Electrical Energy Systems, 31
M. Singla, P. Nijhawan (2021)
Triple diode parameter estimation of solar PV cell using hybrid algorithmInternational Journal of Environmental Science and Technology, 19
Qamar Askari, M. Saeed, I. Younas (2020)
Heap-based optimizer inspired by corporate rank hierarchy for global optimizationExpert Syst. Appl., 161
S. Shamshirband, Mahdis Fathi, Anthony Chronopoulos, Antonio Montieri, Fabio Palumbo, A. Pescapé (2020)
Computational intelligence intrusion detection techniques in mobile cloud computing environments: Review, taxonomy, and open research issuesJ. Inf. Secur. Appl., 55
K. Ishaque, Z. Salam, S. Mekhilef, Amir Shamsudin (2012)
Parameter extraction of solar photovoltaic modules using penalty-based differential evolutionApplied Energy, 99
J. Tavoosi, A. Mohammadzadeh, B. Pahlevanzadeh, M. Kasmani, S. Band, Rabia Safdar, A. Mosavi (2021)
A machine learning approach for active/reactive power control of grid-connected doubly-fed induction generatorsAin Shams Engineering Journal
M. Singla (2019)
Trends so far in Hydrogen Fuel Cell Technology: State of the artInternational Journal of Advanced Trends in Computer Science and Engineering
H. Hemi, J. Ghouili, A. Chériti (2014)
A real time fuzzy logic power management strategy for a fuel cell vehicleEnergy Conversion and Management, 80
L. Sandrolini, M. Artioli, U. Reggiani (2010)
Numerical method for the extraction of photovoltaic module double-diode model parameters through cluster analysisApplied Energy, 87
Jyoti Gupta, P. Nijhawan, Souvik Ganguli (2021)
Parameter extraction of solar PV cell models using novel metaheuristic chaotic tunicate swarm algorithmInternational Transactions on Electrical Energy Systems
Jyoti Gupta, P. Nijhawan, Souvik Ganguli (2021)
Parameter estimation of different solar cells using a novel swarm intelligence techniqueSoft Computing, 26
Qamar Askari, I. Younas, M. Saeed (2020)
Political Optimizer: A novel socio-inspired meta-heuristic for global optimizationKnowl. Based Syst., 195
Amitap Jain, A. Kapoor (2004)
Exact analytical solutions of the parameters of real solar cells using Lambert W-functionSolar Energy Materials and Solar Cells, 81
S. Caux, W. Hankache, M. Fadel, D. Hissel (2010)
PEM fuel cell model suitable for energy optimization purposesEnergy Conversion and Management, 51
F. Fazelpour, Majid Vafaeipour, Omid Rahbari, M. Rosen (2014)
Intelligent optimization to integrate a plug-in hybrid electric vehicle smart parking lot with renewable energy resources and enhance grid characteristicsEnergy Conversion and Management, 77
(2021)
Hydrogen fuel and fuel cell technology for 61a cleaner future: 61a review
Yu Wang, Bin Li, T. Weise, Jianyu Wang, Bo Yuan, Qiongjie Tian (2011)
Self-adaptive learning based particle swarm optimizationInf. Sci., 181
A. Humada, M. Hojabri, S. Mekhilef, Hussein Hamada (2016)
Solar cell parameters extraction based on single and double-diode models: A reviewRenewable & Sustainable Energy Reviews, 56
A. Orioli, A. Gangi (2013)
A procedure to calculate the five-parameter model of crystalline silicon photovoltaic modules on the basis of the tabular performance dataApplied Energy, 102
B. Amrouche, A. Guessoum, M. Belhamel (2012)
A simple behavioural model for solar module electric characteristics based on the first order system step response for MPPT study and comparisonApplied Energy, 91
Yangdong Cao, A. Raise, A. Mohammadzadeh, Sakthivel Rathinasamy, S. Band, A. Mosavi (2021)
Deep learned recurrent type-3 fuzzy system: Application for renewable energy modeling/predictionEnergy Reports
D. Cheddie, N. Munroe (2005)
Review and comparison of approaches to proton exchange membrane fuel cell modelingJournal of Power Sources, 147
Shahabodin Shamshirband, T. Rabczuk, K. Chau (2019)
A Survey of Deep Learning Techniques: Application in Wind and Solar Energy ResourcesIEEE Access, 7
W. Mohamed, M. Remeli, A. Hamid, R. Atan (2011)
Thermal and Coolant Flow Computational Analysis of Cooling Channels for an Air-Cooled PEM Fuel CellApplied Mechanics and Materials, 110-116
S. Berrazouane, Kamal MOHAMMEDI (2014)
Parameter optimization via cuckoo optimization algorithm of fuzzy controller for energy management of a hybrid power systemEnergy Conversion and Management, 78
The purpose of the proposed hybrid method aims to increase population efficiency, and a local search is used to further improve the value of the global best solution. An experimental observation suggests that the model’s statistical outcomes are more aligned with the real-time experimental findings.Design/methodology/approachA novel metaheuristic efficient hybrid algorithm, i.e. hybrid particle swarm optimization rat search algorithm, is introduced and applied for parameter extraction of hybrid energy system. This proposed hybrid method rules out the chances of local minima, hence enhancing the precision of the parametric estimation. The parameter extraction and error is calculated for the solar photovoltaic (PV)–fuel cell system using the proposed algorithm.FindingsNonparametric statistical tests are also conducted to indicate the findings of the outcome parameters using various metaheuristic algorithms. The proposed algorithm is better than the rest of the compared algorithms in the study.Originality/valueThe authors proposed a novel algorithm, and this proposed algorithm is implemented on hybrid solar PV and fuel cell-based system for parameter extraction. The nonparametric test results clearly suggest that the proposed algorithm is far more effective for parameter estimation of the test system.
COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering – Emerald Publishing
Published: Aug 26, 2022
Keywords: Error analysis; Numerical analysis; Equivalent circuit model; Design optimization methodology; Particle swarm optimization; Rat search algorithm; Hybrid particle swarm optimization rat search algorithm; Solar photovoltaic cell; Polymer electrolyte membrane fuel cell; Nonparametric tests
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