Developed teamwork optimizer for model parameter estimation of the proton exchange membrane fuel cell
This paper proposes a new optimal methodology for model parameters estimation of the Proton Exchange Membrane Fuel Cell. The main purpose here is to design a newly developed metaheuristic technique to deliver a model with higher accuracy. In this study, we utilized two modifications for the Teamwork...
- Autores:
-
Syah, Rahmad
Grimaldo Guerrero, John William
Leonidovich Poltarykhin, Andrey
Suksatan, Wanich
Ravindhan, Surendar
Bokov, Dmitry O.
Abdelbasset, Walid Kamal
Al-Janabi, Samaher
Alkaim, Ayad F.
Yu. Tumanovj, Dmitriy
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2022
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/9549
- Acceso en línea:
- https://hdl.handle.net/11323/9549
https://repositorio.cuc.edu.co/
- Palabra clave:
- System estimation
PEMFC
Improved Teamwork Optimizer
Voltage profile
- Rights
- openAccess
- License
- Atribución 4.0 Internacional (CC BY 4.0)
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| dc.title.eng.fl_str_mv |
Developed teamwork optimizer for model parameter estimation of the proton exchange membrane fuel cell |
| title |
Developed teamwork optimizer for model parameter estimation of the proton exchange membrane fuel cell |
| spellingShingle |
Developed teamwork optimizer for model parameter estimation of the proton exchange membrane fuel cell System estimation PEMFC Improved Teamwork Optimizer Voltage profile |
| title_short |
Developed teamwork optimizer for model parameter estimation of the proton exchange membrane fuel cell |
| title_full |
Developed teamwork optimizer for model parameter estimation of the proton exchange membrane fuel cell |
| title_fullStr |
Developed teamwork optimizer for model parameter estimation of the proton exchange membrane fuel cell |
| title_full_unstemmed |
Developed teamwork optimizer for model parameter estimation of the proton exchange membrane fuel cell |
| title_sort |
Developed teamwork optimizer for model parameter estimation of the proton exchange membrane fuel cell |
| dc.creator.fl_str_mv |
Syah, Rahmad Grimaldo Guerrero, John William Leonidovich Poltarykhin, Andrey Suksatan, Wanich Ravindhan, Surendar Bokov, Dmitry O. Abdelbasset, Walid Kamal Al-Janabi, Samaher Alkaim, Ayad F. Yu. Tumanovj, Dmitriy |
| dc.contributor.author.none.fl_str_mv |
Syah, Rahmad Grimaldo Guerrero, John William Leonidovich Poltarykhin, Andrey Suksatan, Wanich Ravindhan, Surendar Bokov, Dmitry O. Abdelbasset, Walid Kamal Al-Janabi, Samaher Alkaim, Ayad F. Yu. Tumanovj, Dmitriy |
| dc.subject.proposal.eng.fl_str_mv |
System estimation PEMFC Improved Teamwork Optimizer Voltage profile |
| topic |
System estimation PEMFC Improved Teamwork Optimizer Voltage profile |
| description |
This paper proposes a new optimal methodology for model parameters estimation of the Proton Exchange Membrane Fuel Cell. The main purpose here is to design a newly developed metaheuristic technique to deliver a model with higher accuracy. In this study, we utilized two modifications for the Teamwork Optimizer to get higher accuracy. The two modifiers are opposition-based learning and chaotic mechanism. The results show that using the opposition-based learning, the population diversity has been kept, owing to the greater population size due to the solution space, and using the Chaos theory, the population diversity has been increased. This is proved by applying the Improved Teamwork Optimizer to minimize the Root Mean Square Error and Integral Absolute Error between the suggested model and empirical data. The validation has been done by applying the proposed Improved Teamwork Optimizer to two studied cases, which are Nexa Proton Exchange Membrane Fuel Cell and NedSstack PS6 Proton Exchange Membrane Fuel Cell, and comparing it with other published works. Simulation results showed that the proposed method with 1.14 Integral Absolute Error and 0.21 Root Mean Square Error for NedSstack PS6 Proton Exchange Membrane Fuel Cells and with 12 Integral Absolute Error and 0.17 Root Mean Square Error for Nexa Proton Exchange Membrane Fuel Cells provides the minimum error value among the other optimization techniques. This shows the higher potential of the proposed method for use as the parameter estimator for Proton Exchange Membrane Fuel Cells. |
| publishDate |
2022 |
| dc.date.accessioned.none.fl_str_mv |
2022-09-30T00:48:45Z |
| dc.date.available.none.fl_str_mv |
2022-09-30T00:48:45Z |
| dc.date.issued.none.fl_str_mv |
2022 |
| dc.type.spa.fl_str_mv |
Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
| dc.type.content.spa.fl_str_mv |
Text |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/article |
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http://purl.org/redcol/resource_type/ART |
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info:eu-repo/semantics/publishedVersion |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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| dc.identifier.citation.spa.fl_str_mv |
ahmad Syah, John William Grimaldo Guerrero, Andrey Leonidovich Poltarykhin, Wanich Suksatan, Surendar Aravindhan, Dmitry O. Bokov, Walid Kamal Abdelbasset, Samaher Al-Janabi, Ayad F. Alkaim, Dmitriy Yu. Tumanov, Developed teamwork optimizer for model parameter estimation of the proton exchange membrane fuel cell, Energy Reports, Volume 8, 2022, Pages 10776-10785, ISSN 2352-4847, https://doi.org/10.1016/j.egyr.2022.08.177. |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/11323/9549 |
| dc.identifier.doi.none.fl_str_mv |
10.1016/j.egyr.2022.08.177 |
| dc.identifier.eissn.spa.fl_str_mv |
2352-4847 |
| dc.identifier.instname.spa.fl_str_mv |
Corporación Universidad de la Costa |
| dc.identifier.reponame.spa.fl_str_mv |
REDICUC - Repositorio CUC |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.cuc.edu.co/ |
| identifier_str_mv |
ahmad Syah, John William Grimaldo Guerrero, Andrey Leonidovich Poltarykhin, Wanich Suksatan, Surendar Aravindhan, Dmitry O. Bokov, Walid Kamal Abdelbasset, Samaher Al-Janabi, Ayad F. Alkaim, Dmitriy Yu. Tumanov, Developed teamwork optimizer for model parameter estimation of the proton exchange membrane fuel cell, Energy Reports, Volume 8, 2022, Pages 10776-10785, ISSN 2352-4847, https://doi.org/10.1016/j.egyr.2022.08.177. 10.1016/j.egyr.2022.08.177 2352-4847 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
| url |
https://hdl.handle.net/11323/9549 https://repositorio.cuc.edu.co/ |
| dc.language.iso.spa.fl_str_mv |
eng |
| language |
eng |
| dc.relation.ispartofjournal.spa.fl_str_mv |
Energy Reports |
| dc.relation.references.spa.fl_str_mv |
Akbary, P., et al., 2019. Extracting appropriate nodal marginal prices for all types of committed reserve. Comput. Econ. 53 (1), 1–26. Ariza, H.E., et al., 2018. Thermal and electrical parameter identification of a proton exchange membrane fuel cell using genetic algorithm. Energies 11 (8), 2099. Bao, S., et al., 2020. A new method for optimal parameters identification of a PEMFC using an improved version of Monarch Butterfly Optimization Algorithm. Int. J. Hydrogen Energy 45 (35), 17882–17892. Cao, Y., et al., 2020. An efficient terminal voltage control for PEMFC based on an improved version of whale optimization algorithm. Energy Rep. 6, 530–542. Chen, Liang, et al., 2022. Optimal modeling of combined cooling, heating, and power systems using developed african vulture optimization: a case study in watersport complex. Energy Sources A 44 (2), 4296–4317. Dehghani, M., Trojovský, P., 2021. Teamwork optimization algorithm: A new optimization approach for function minimization/maximization. Sensors 21 (13), 4567. Dehghani, M., et al., 2021. Blockchain-based securing of data exchange in a power transmission system considering congestion management and social welfare. Sustainability 13 (1), 90. Dhiman, G., Kumar, V., 2017. Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Adv. Eng. Softw. 114, 48–70. Dhiman, G., Kumar, V., 2018. Emperor penguin optimizer: A bio-inspired algorithm for engineering problems. Knowl.-Based Syst. 159, 20–50. Ebrahimian, H., et al., 2018. The price prediction for the energy market based on a new method. Econ. Res.-Ekonomska Istraživanja 31 (1), 313–337. Fan, X., et al., 2020a. High voltage gain DC/DC converter using coupled inductor and VM techniques. IEEE Access 8, 131975-131987. Fan, X., et al., 2020b. Multi-objective optimization for the proper selection of the best heat pump technology in a fuel cell-heat pump micro-CHP system. Energy Rep. 6, 325–335. Firouz, M.H., Ghadimi, N., 2016. Concordant controllers based on FACTS and FPSS for solving wide-area in multi-machine power system. J. Intell. Fuzzy Systems 30 (2), 845–859. Ghadimi, N., 2015. An adaptive neuro-fuzzy inference system for islanding detection in wind turbine as distributed generation. Complexity 21 (1), 10–20. Ghadimi, N., Afkousi-Paqaleh, A., Emamhosseini, A., 2014. A PSO-based fuzzy long-term multi-objective optimization approach for placement and parameter setting of UPFC. Arab. J. Sci. Eng. 39 (4), 2953–2963. Ghiasi, M., Ghadimi, N., Ahmadinia, E., 2019. An analytical methodology for reliability assessment and failure analysis in distributed power system. SN Appl. Sci. 1 (1), 44. Guo, Haibing, et al., 2022. Parameter extraction of the SOFC mathematical model based on fractional order version of dragonfly algorithm. Int. J. Hydrogen Energy. Hamian, M., et al., 2018. A framework to expedite joint energy-reserve payment cost minimization using a custom-designed method based on mixed integer genetic algorithm. Eng. Appl. Artif. Intell. 72, 203–212. Han, Erfeng, Ghadimi, Noradin, 2022. Model identification of proton-exchange membrane fuel cells based on a hybrid convolutional neural network and extreme learning machine optimized by improved honey badger algorithm. Sustain. Energy Technol. Assess. 52, 102005. Liu, Y., et al., 2017. Electricity load forecasting by an improved forecast engine for building level consumers. Energy 139, 18–30. Lu, X., et al., 2020. Optimal estimation of the Proton Exchange Membrane Fuel Cell model parameters based on extended version of Crow Search Algorithm. J. Cleaner Prod. 272, 122640. Madadi, A., et al., 2016. Robust control of power system stabilizer using world cup optimization algorithm. Int. J. Inf. Secur. Syst. Manage. 5 (1), 519–526. Mahdinia, Saeideh, et al., 2021. Optimization of PEMFC model parameters using meta-heuristics. Sustainability 13 (22), 12771. Mann, R., et al., 2006. Henry’s Law and the solubilities of reactant gases in the modelling of PEM fuel cells. J. Power Sources 161 (2), 768–774. Mehrpooya, Mehdi, et al., 2021. Numerical investigation of a new combined energy system includes parabolic dish solar collector, Stirling engine and thermoelectric device. Int. J. Energy Res. 45 (11), 16436–16455. Meng, Q., et al., 2020. A single-phase transformer-less grid-tied inverter based on switched capacitor for PV application. J. Control Autom. Electr. Syst. 31 (1), 257–270. Miao, D., et al., 2020. Parameter estimation of PEM fuel cells employing the hybrid grey wolf optimization method. Energy 193, 116616. Mir, M., et al., 2020. Application of hybrid forecast engine based intelligent algorithm and feature selection for wind signal prediction. Evol. Syst. 11 (4), 559–573. Navid, R., Saeid, R., 2021. Skin melanoma segmentation using neural networks optimized by quantum invasive weed optimization algorithm. In: Metaheuristics and Optimization in Computer and Electrical Engineering. Springer, pp. 233–250. Razmjooy, N., Ashourian, M., Foroozandeh, Z., 2020. Metaheuristics and Optimization in Computer and Electrical Engineering. Springer. Razmjooy, N., Khalilpour, M., Ramezani, M., 2016. A new meta-heuristic optimization algorithm inspired by FIFA world cup competitions: theory and its application in PID designing for AVR system. J. Control Autom. Electr. Syst. 27 (4), 419–440. Restrepo, C., et al., 2014. Identification of a proton-exchange membrane fuel cell’s model parameters by means of an evolution strategy. IEEE Trans. Ind. Inf. 11 (2), 548–559. Rim, C., et al., 2018. A niching chaos optimization algorithm for multimodal optimization. Soft Comput. 22 (2), 621–633. Rizk-Allah, R.M., El-Fergany, A.A., 2021. Artificial ecosystem optimizer for parameters identification of proton exchange membrane fuel cells model. Int. J. Hydrogen Energy 46 (75), 37612–37627. Saeedi, M., et al., 2019. Robust optimization based optimal chiller loading under cooling demand uncertainty. Appl. Therm. Eng. 148, 1081–1091. San Martin, J., et al., 2010. Influence of the rated power in the performance of different proton exchange membrane (PEM) fuel cells. Energy 35 (5), 1898–1907. Song, Y., Tan, X., Mizzi, S., 2020. Optimal parameter extraction of the proton exchange membrane fuel cells based on a new Harris Hawks optimization algorithm. Energy Sources A 1–18. Spiegel, C., 2011. PEM Fuel Cell Modeling and Simulation using MATLAB. Elsevier. Tian, M.-W., et al., 2020. New optimal design for a hybrid solar chimney, solid oxide electrolysis and fuel cell based on improved deer hunting optimization algorithm. J. Cleaner Prod. 249, 119414. Tizhoosh, H.R., 2005. Opposition-based learning: a new scheme for machine intelligence. In: International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce. CIMCA-IAWTIC’06. IEEE. Wolpert, D.H., Macready, W.G., 1997. No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1 (1), 67–82. Xu, S., Wang, Y., Wang, Z., 2019. Parameter estimation of proton exchange membrane fuel cells using eagle strategy based on JAYA algorithm and Nelder–Mead simplex method. Energy 173, 457–467. Yang, D., Li, G., Cheng, G., 2007. On the efficiency of chaos optimization algorithms for global optimization. Chaos Solitons Fractals 34 (4), 1366–1375. Yang, Zaoli, et al., 2021. Robust multi-objective optimal design of islanded hybrid system with renewable and diesel sources/stationary and mobile energy storage systems. Renew. Sustain. Energy Rev. 148, 111295. Yazdani, M., Jolai, F., 2016. Lion optimization algorithm (LOA): a nature-inspired metaheuristic algorithm. J. Comput. Des. Eng. 3 (1), 24–36. Ye, H., et al., 2020. High step-up interleaved dc/dc converter with high efficiency. Energy Sources A 1–20. Yu, D., et al., 2019a. Reliability constraint stochastic UC by considering the correlation of random variables with Copula theory. IET Renew. Power Gener. 13 (14), 2587–2593. Yu, D., et al., 2019b. System identification of PEM fuel cells using an improved Elman neural network and a new hybrid optimization algorithm. Energy Rep. 5, 1365–1374. Yu, D., et al., 2020. Energy management of wind-PV-storage-grid based large electricity consumer using robust optimization technique. J. Energy Storage 27, 101054. Yuan, Z., et al., 2020a. Developed coyote optimization algorithm and its application to optimal parameters estimation of PEMFC model. Energy Rep. 6, 1106–1117. Yuan, Z., et al., 2020b. Probabilistic decomposition-based security constrained transmission expansion planning incorporating distributed series reactor. IET Gen. Transm. Distrib. 14 (17), 3478–3487. Zhang, Jiali, Khayatnezhad, Majid, Ghadimi, Noradin, 2022. Optimal model evaluation of the proton-exchange membrane fuel cells based on deep learning and modified African Vulture Optimization Algorithm. Energy Sources A 44 (1), 287–305. Zhang, G., et al., 2020a. Optimal parameter extraction of PEM fuel cells by meta-heuristics. Int. J. Ambient Energy 1–22, (in press). Zhang, G., et al., 2020b. Optimal parameter extraction of PEM fuel cells by meta-heuristics. Int. J. Ambient Energy 1–10. Zhi, Y., et al., 2020. New approaches for regulation of solid oxide fuel cell using dynamic condition approximation and statcom. Internat. Trans. Electrical Energy Syst. e12756. |
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Atribución 4.0 Internacional (CC BY 4.0)© 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Syah, RahmadGrimaldo Guerrero, John WilliamLeonidovich Poltarykhin, AndreySuksatan, WanichRavindhan, SurendarBokov, Dmitry O.Abdelbasset, Walid KamalAl-Janabi, SamaherAlkaim, Ayad F.Yu. Tumanovj, Dmitriy2022-09-30T00:48:45Z2022-09-30T00:48:45Z2022ahmad Syah, John William Grimaldo Guerrero, Andrey Leonidovich Poltarykhin, Wanich Suksatan, Surendar Aravindhan, Dmitry O. Bokov, Walid Kamal Abdelbasset, Samaher Al-Janabi, Ayad F. Alkaim, Dmitriy Yu. Tumanov, Developed teamwork optimizer for model parameter estimation of the proton exchange membrane fuel cell, Energy Reports, Volume 8, 2022, Pages 10776-10785, ISSN 2352-4847, https://doi.org/10.1016/j.egyr.2022.08.177.https://hdl.handle.net/11323/954910.1016/j.egyr.2022.08.1772352-4847Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/This paper proposes a new optimal methodology for model parameters estimation of the Proton Exchange Membrane Fuel Cell. The main purpose here is to design a newly developed metaheuristic technique to deliver a model with higher accuracy. In this study, we utilized two modifications for the Teamwork Optimizer to get higher accuracy. The two modifiers are opposition-based learning and chaotic mechanism. The results show that using the opposition-based learning, the population diversity has been kept, owing to the greater population size due to the solution space, and using the Chaos theory, the population diversity has been increased. This is proved by applying the Improved Teamwork Optimizer to minimize the Root Mean Square Error and Integral Absolute Error between the suggested model and empirical data. The validation has been done by applying the proposed Improved Teamwork Optimizer to two studied cases, which are Nexa Proton Exchange Membrane Fuel Cell and NedSstack PS6 Proton Exchange Membrane Fuel Cell, and comparing it with other published works. Simulation results showed that the proposed method with 1.14 Integral Absolute Error and 0.21 Root Mean Square Error for NedSstack PS6 Proton Exchange Membrane Fuel Cells and with 12 Integral Absolute Error and 0.17 Root Mean Square Error for Nexa Proton Exchange Membrane Fuel Cells provides the minimum error value among the other optimization techniques. This shows the higher potential of the proposed method for use as the parameter estimator for Proton Exchange Membrane Fuel Cells.10 páginasapplication/pdfengElsevier Ltd.United Kingdomhttps://www.sciencedirect.com/science/article/pii/S2352484722016225?via%3DihubDeveloped teamwork optimizer for model parameter estimation of the proton exchange membrane fuel cellArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Energy ReportsAkbary, P., et al., 2019. Extracting appropriate nodal marginal prices for all types of committed reserve. Comput. Econ. 53 (1), 1–26.Ariza, H.E., et al., 2018. Thermal and electrical parameter identification of a proton exchange membrane fuel cell using genetic algorithm. Energies 11 (8), 2099.Bao, S., et al., 2020. A new method for optimal parameters identification of a PEMFC using an improved version of Monarch Butterfly Optimization Algorithm. Int. J. Hydrogen Energy 45 (35), 17882–17892.Cao, Y., et al., 2020. An efficient terminal voltage control for PEMFC based on an improved version of whale optimization algorithm. Energy Rep. 6, 530–542.Chen, Liang, et al., 2022. Optimal modeling of combined cooling, heating, and power systems using developed african vulture optimization: a case study in watersport complex. Energy Sources A 44 (2), 4296–4317.Dehghani, M., Trojovský, P., 2021. Teamwork optimization algorithm: A new optimization approach for function minimization/maximization. Sensors 21 (13), 4567.Dehghani, M., et al., 2021. Blockchain-based securing of data exchange in a power transmission system considering congestion management and social welfare. Sustainability 13 (1), 90.Dhiman, G., Kumar, V., 2017. Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Adv. Eng. Softw. 114, 48–70.Dhiman, G., Kumar, V., 2018. Emperor penguin optimizer: A bio-inspired algorithm for engineering problems. Knowl.-Based Syst. 159, 20–50.Ebrahimian, H., et al., 2018. The price prediction for the energy market based on a new method. Econ. Res.-Ekonomska Istraživanja 31 (1), 313–337.Fan, X., et al., 2020a. High voltage gain DC/DC converter using coupled inductor and VM techniques. IEEE Access 8, 131975-131987.Fan, X., et al., 2020b. Multi-objective optimization for the proper selection of the best heat pump technology in a fuel cell-heat pump micro-CHP system. Energy Rep. 6, 325–335.Firouz, M.H., Ghadimi, N., 2016. Concordant controllers based on FACTS and FPSS for solving wide-area in multi-machine power system. J. Intell. Fuzzy Systems 30 (2), 845–859.Ghadimi, N., 2015. An adaptive neuro-fuzzy inference system for islanding detection in wind turbine as distributed generation. Complexity 21 (1), 10–20.Ghadimi, N., Afkousi-Paqaleh, A., Emamhosseini, A., 2014. A PSO-based fuzzy long-term multi-objective optimization approach for placement and parameter setting of UPFC. Arab. J. Sci. Eng. 39 (4), 2953–2963.Ghiasi, M., Ghadimi, N., Ahmadinia, E., 2019. An analytical methodology for reliability assessment and failure analysis in distributed power system. SN Appl. Sci. 1 (1), 44.Guo, Haibing, et al., 2022. Parameter extraction of the SOFC mathematical model based on fractional order version of dragonfly algorithm. Int. J. Hydrogen Energy.Hamian, M., et al., 2018. A framework to expedite joint energy-reserve payment cost minimization using a custom-designed method based on mixed integer genetic algorithm. Eng. Appl. Artif. Intell. 72, 203–212.Han, Erfeng, Ghadimi, Noradin, 2022. Model identification of proton-exchange membrane fuel cells based on a hybrid convolutional neural network and extreme learning machine optimized by improved honey badger algorithm. Sustain. Energy Technol. Assess. 52, 102005.Liu, Y., et al., 2017. Electricity load forecasting by an improved forecast engine for building level consumers. Energy 139, 18–30.Lu, X., et al., 2020. Optimal estimation of the Proton Exchange Membrane Fuel Cell model parameters based on extended version of Crow Search Algorithm. J. Cleaner Prod. 272, 122640.Madadi, A., et al., 2016. Robust control of power system stabilizer using world cup optimization algorithm. Int. J. Inf. Secur. Syst. Manage. 5 (1), 519–526.Mahdinia, Saeideh, et al., 2021. Optimization of PEMFC model parameters using meta-heuristics. Sustainability 13 (22), 12771.Mann, R., et al., 2006. Henry’s Law and the solubilities of reactant gases in the modelling of PEM fuel cells. J. Power Sources 161 (2), 768–774.Mehrpooya, Mehdi, et al., 2021. Numerical investigation of a new combined energy system includes parabolic dish solar collector, Stirling engine and thermoelectric device. Int. J. Energy Res. 45 (11), 16436–16455.Meng, Q., et al., 2020. A single-phase transformer-less grid-tied inverter based on switched capacitor for PV application. J. Control Autom. Electr. Syst. 31 (1), 257–270.Miao, D., et al., 2020. Parameter estimation of PEM fuel cells employing the hybrid grey wolf optimization method. Energy 193, 116616.Mir, M., et al., 2020. Application of hybrid forecast engine based intelligent algorithm and feature selection for wind signal prediction. Evol. Syst. 11 (4), 559–573.Navid, R., Saeid, R., 2021. Skin melanoma segmentation using neural networks optimized by quantum invasive weed optimization algorithm. In: Metaheuristics and Optimization in Computer and Electrical Engineering. Springer, pp. 233–250.Razmjooy, N., Ashourian, M., Foroozandeh, Z., 2020. Metaheuristics and Optimization in Computer and Electrical Engineering. Springer.Razmjooy, N., Khalilpour, M., Ramezani, M., 2016. A new meta-heuristic optimization algorithm inspired by FIFA world cup competitions: theory and its application in PID designing for AVR system. J. Control Autom. Electr. Syst. 27 (4), 419–440.Restrepo, C., et al., 2014. Identification of a proton-exchange membrane fuel cell’s model parameters by means of an evolution strategy. IEEE Trans. Ind. Inf. 11 (2), 548–559.Rim, C., et al., 2018. A niching chaos optimization algorithm for multimodal optimization. Soft Comput. 22 (2), 621–633.Rizk-Allah, R.M., El-Fergany, A.A., 2021. 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Electrical Energy Syst. e12756.10785107768System estimationPEMFCImproved Teamwork OptimizerVoltage profilePublicationORIGINALDeveloped teamwork optimizer for model parameter estimation of the proton exchange membrane fuel cell.pdfDeveloped teamwork optimizer for model parameter estimation of the proton exchange membrane fuel cell.pdfArtículoapplication/pdf806327https://repositorio.cuc.edu.co/bitstreams/2632c36b-c3a8-4526-b2ff-057d5e133a2b/download027a3bf2deba7ec649eb7c83820b4bd0MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://repositorio.cuc.edu.co/bitstreams/2408d7ec-5d75-4f2a-8a6b-057cddffb4d2/download2f9959eaf5b71fae44bbf9ec84150c7aMD52TEXTDeveloped teamwork optimizer for model parameter estimation of the proton exchange membrane fuel cell.pdf.txtDeveloped teamwork optimizer for model parameter estimation of the proton exchange membrane fuel cell.pdf.txtExtracted texttext/plain51422https://repositorio.cuc.edu.co/bitstreams/e0fe369d-8116-4e9d-8371-19595c81c98f/download88bb32cd7cb38b10096de1285b01f542MD53THUMBNAILDeveloped teamwork optimizer for model parameter estimation of the proton exchange membrane fuel cell.pdf.jpgDeveloped teamwork optimizer for model parameter estimation of the proton exchange membrane fuel cell.pdf.jpgGenerated Thumbnailimage/jpeg15568https://repositorio.cuc.edu.co/bitstreams/66e02d91-cce1-4dcc-a382-fb1fd2b16af0/downloadfa9c410a6e888fb72c97134c40d755e9MD5411323/9549oai:repositorio.cuc.edu.co:11323/95492024-09-17 10:59:07.161https://creativecommons.org/licenses/by/4.0/© 2022 The Author(s). 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This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).open.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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