A new technique for optimal estimation of the circuit-based PEMFCs using developed Sunflower Optimization Algorithm

Abstract This paper proposes a new methodology for the optimal selection of the parameters for proton exchange membrane fuel cell (PEMFC) models. The proposed method is to optimal parameter selection of the circuit-based model of the PEMFC model to minimize the sum of squared error (SSE) value between the estimated and the actual output voltage of the PEMFC stack. For minimizing the SSE, a newly developed model of the Sunflower Optimization Algorithm (DSFO) is proposed. Performance analysis is performed based on two practical models including NedSstack PS6 PEMFC and Horizon 500-W PEMFCs from the literature and the results have been compared with the empirical data and also some state of art methods including Seagull Optimization Algorithm (SOA), Multi-verse optimizer (MVO), and Shuffled Frog-Leaping Algorithm (SFLA). Final results indicate 2.18 and 0.014 SSE value for NedSstack PS6 PEMFC and Horizon 500-W open cathode PEMFC, respectively which are the minimum values compared with the other compared methods.

[1]  Noradin Ghadimi,et al.  Multi-objective energy management in a micro-grid , 2018, Energy Reports.

[2]  G. Cheng,et al.  On the efficiency of chaos optimization algorithms for global optimization , 2007 .

[3]  Mohsen Mohammadi,et al.  Optimal location and optimized parameters for robust power system stabilizer using honeybee mating optimization , 2015, Complex..

[4]  Attia A. El-Fergany,et al.  Electrical characterisation of proton exchange membrane fuel cells stack using grasshopper optimiser , 2018 .

[5]  Nan Wang,et al.  Application of co-evolution RNA genetic algorithm for obtaining optimal parameters of SOFC model , 2020 .

[6]  Do-Young Kim,et al.  In-plane 2-D patterning of microporous layer by inkjet printing for water management of polymer electrolyte fuel cell , 2020 .

[7]  Yong Wang,et al.  System identification of PEM fuel cells using an improved Elman neural network and a new hybrid optimization algorithm , 2019, Energy Reports.

[8]  Ning Wang,et al.  Cuckoo search algorithm with onlooker bee search for modeling PEMFCs using T2FNN , 2019, Eng. Appl. Artif. Intell..

[9]  Noradin Ghadimi,et al.  High step-up interleaved dc/dc converter with high efficiency , 2020 .

[10]  Jun Liu,et al.  An IGDT-based risk-involved optimal bidding strategy for hydrogen storage-based intelligent parking lot of electric vehicles , 2020 .

[11]  Qing Meng,et al.  A Single-Phase Transformer-Less Grid-Tied Inverter Based on Switched Capacitor for PV Application , 2020, Journal of Control, Automation and Electrical Systems.

[12]  Hany M. Hasanien,et al.  Parameters extraction of PEMFC's model using manta rays foraging optimizer , 2020, International Journal of Energy Research.

[13]  Noradin Ghadimi,et al.  Optimal preventive maintenance policy for electric power distribution systems based on the fuzzy AHP methods , 2016, Complex..

[14]  Guo Li,et al.  A niching chaos optimization algorithm for multimodal optimization , 2018, Soft Comput..

[15]  Oguz Emrah Turgut,et al.  Optimal proton exchange membrane fuel cell modelling based on hybrid Teaching Learning Based Optimization – Differential Evolution algorithm , 2016 .

[16]  Ning Wang,et al.  Cuckoo search algorithm with explosion operator for modeling proton exchange membrane fuel cells , 2019, International Journal of Hydrogen Energy.

[17]  Satvir Singh,et al.  Butterfly optimization algorithm: a novel approach for global optimization , 2019, Soft Comput..

[18]  Ahmed Fathy,et al.  Multi-Verse Optimizer for Identifying the Optimal Parameters of PEMFC Model , 2018 .

[19]  Yugal Kumar,et al.  Improved cat swarm optimization algorithm for solving global optimization problems and its application to clustering , 2017, Applied Intelligence.

[20]  Navid Razmjooy,et al.  Experimental modeling of PEM fuel cells using a new improved seagull optimization algorithm , 2019, Energy Reports.

[21]  Carlos Sánchez,et al.  Thermal and Electrical Parameter Identification of a Proton Exchange Membrane Fuel Cell Using Genetic Algorithm , 2018, Energies.

[22]  Wenbin Li,et al.  Thermodynamic and economic assessment of a PEMFC-based micro-CCHP system integrated with geothermal-assisted methanol reforming , 2020 .

[23]  Alireza Nouri,et al.  Planning in Microgrids With Conservation of Voltage Reduction , 2018, IEEE Systems Journal.

[24]  Vijander Singh,et al.  Owl search algorithm: A novel nature-inspired heuristic paradigm for global optimization , 2018, J. Intell. Fuzzy Syst..

[25]  N. Rajasekar,et al.  Application of flower pollination algorithm for enhanced proton exchange membrane fuel cell modelling , 2019, International Journal of Hydrogen Energy.

[26]  Karzan Wakil,et al.  RETRACTED: Risk-assessment of photovoltaic-wind-battery-grid based large industrial consumer using information gap decision theory , 2018, Solar Energy.

[27]  Mehdi Hosseinzadeh,et al.  A framework to expedite joint energy-reserve payment cost minimization using a custom-designed method based on Mixed Integer Genetic Algorithm , 2018, Eng. Appl. Artif. Intell..

[28]  Haiguo Tang,et al.  A new wind power prediction method based on ridgelet transforms, hybrid feature selection and closed-loop forecasting , 2018, Adv. Eng. Informatics.

[29]  Navid Razmjooy,et al.  Optimal configuration and energy management for combined solar chimney, solid oxide electrolysis, and fuel cell: a case study in Iran , 2019, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects.

[30]  Saeed Tavakoli,et al.  Reliability based optimal allocation of distributed generations in transmission systems under demand response program , 2019, Electric Power Systems Research.

[31]  Liyan Zhang,et al.  Data driven NARMAX modeling for PEMFC air compressor , 2020 .

[32]  Leandro dos Santos Coelho,et al.  Coyote Optimization Algorithm: A New Metaheuristic for Global Optimization Problems , 2018, 2018 IEEE Congress on Evolutionary Computation (CEC).

[33]  Ayda Darvishan,et al.  Fuzzy-based heat and power hub models for cost-emission operation of an industrial consumer using compromise programming , 2018, Applied Thermal Engineering.

[34]  Z. M. Isa,et al.  Optimizing PEMFC model parameters using ant lion optimizer and dragonfly algorithm: A comparative study , 2019 .

[35]  Noradin Ghadimi,et al.  A new prediction model of battery and wind-solar output in hybrid power system , 2019, J. Ambient Intell. Humaniz. Comput..

[36]  Vijay Kumar,et al.  Emperor penguin optimizer: A bio-inspired algorithm for engineering problems , 2018, Knowl. Based Syst..

[37]  I. Saleh,et al.  Simplified mathematical model of proton exchange membrane fuel cell based on horizon fuel cell stack , 2016 .

[38]  Attia A. El-Fergany,et al.  Extracting optimal parameters of PEM fuel cells using Salp Swarm Optimizer , 2018 .

[39]  Noradin Ghadimi,et al.  An analytical methodology for assessment of smart monitoring impact on future electric power distribution system reliability , 2015, Complex..

[40]  Sebastiao Simões da Cunha,et al.  A sunflower optimization (SFO) algorithm applied to damage identification on laminated composite plates , 2019, Engineering with Computers.

[41]  Mohammad Ghiasi,et al.  Extracting Appropriate Nodal Marginal Prices for All Types of Committed Reserve , 2019 .

[42]  Shuhuai Lan,et al.  Performance evaluation of commercial-size proton exchange membrane fuel cell stacks considering air flow distribution in the manifold , 2020 .

[43]  Seyed Mohammad Mirjalili,et al.  Multi-Verse Optimizer: a nature-inspired algorithm for global optimization , 2015, Neural Computing and Applications.

[44]  Zhi Wang,et al.  Parameter estimation of proton exchange membrane fuel cells using eagle strategy based on JAYA algorithm and Nelder-Mead simplex method , 2019, Energy.

[45]  Wei Wang,et al.  Electricity load forecasting by an improved forecast engine for building level consumers , 2017 .

[46]  Noradin Ghadimi,et al.  The price prediction for the energy market based on a new method , 2018 .

[47]  Tulga Ersal,et al.  A Mathematical Model toward Real-Time Monitoring of Automotive PEM Fuel Cells , 2020, Journal of the Electrochemical Society.

[48]  M. A. Elhameed,et al.  Effective parameters’ identification for polymer electrolyte membrane fuel cell models using grey wolf optimizer , 2017 .

[49]  D. Binu,et al.  Deer Hunting Optimization Algorithm: A New Nature-Inspired Meta-heuristic Paradigm , 2019, The Computer Journal.

[50]  Noradin Ghadimi,et al.  Concordant controllers based on FACTS and FPSS for solving wide-area in multi-machine power system , 2016, J. Intell. Fuzzy Syst..

[51]  Hany M. Hasanien,et al.  Effective methodology based on neural network optimizer for extracting model parameters of PEM fuel cells , 2019, International Journal of Energy Research.

[52]  M. Karimi,et al.  Voltage Control of PEMFC Using A New Controller Based on Reinforcement Learning , 2012 .

[53]  Noradin Ghadimi,et al.  A new feature selection and hybrid forecast engine for day-ahead price forecasting of electricity markets , 2017, J. Intell. Fuzzy Syst..

[54]  Hadi Zayandehroodi,et al.  A New Formulation to Reduce the Number of Variables and Constraints to Expedite SCUC in Bulky Power Systems , 2019 .

[55]  Gaige Wang,et al.  Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems , 2016, Memetic Computing.

[56]  Hany M. Hasanien,et al.  Semi-empirical PEM fuel cells model using whale optimization algorithm , 2019, Energy Conversion and Management.