Document Type : Original Article
1 College of Engineering, University of Miami, Coral Gables, FL 33146, USA
2 Department of Electrical Engineering, University of North Texas, USA
A new Blackbox technique has been presented in the current paper for model estimation of the solid oxide fuel cells (SOFCs) for providing better results. The proposed method is based on a Hierarchical Radial Basis Function (HRBF). The presented method is then developed by a new modified metaheuristic, called Developed Coronavirus Herd Immunity Algorithm. The suggested model has been named DCHIA-HRBF. The proposed model is then trained by some data and prepared for the identification and prediction. The model is then analyzed and were put in comparison with several latest techniques for validation of the efficiency of the technique. It is also verified by the empirical data to prove its validation with the real data. Simulation results specified that the suggested DCHIA-HRBF delivers high effectiveness as an identifier and prediction tool for the SOFCs.
2. Akbary, P., et al., Extracting appropriate nodal marginal prices for all types of committed reserve. Computational Economics, 2019. 53(1): p. 1-26.
3. Bagheri, M., et al. A novel wind power forecasting based feature selection and hybrid forecast engine bundled with honey bee mating optimization. in 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe). 2018. IEEE.
4. Cai, W., et al., Optimal bidding and offering strategies of compressed air energy storage: A hybrid robust-stochastic approach. Renewable Energy, 2019. 143: p. 1-8.
5. Ye, H., et al., High step-up interleaved dc/dc converter with high efficiency. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2020: p. 1-20.
6. Yu, D. and N. Ghadimi, Reliability constraint stochastic UC by considering the correlation of random variables with Copula theory. IET Renewable Power Generation, 2019. 13(14): p. 2587-2593.
7. Dehghani, M., et al., Blockchain-based securing of data exchange in a power transmission system considering congestion management and social welfare. Sustainability, 2021. 13(1): p. 90.
8. Ebrahimian, H., et al., The price prediction for the energy market based on a new method. Economic research-Ekonomska istraživanja, 2018. 31(1): p. 313-337.
9. Cao, Y., et al., Multi-objective optimization of a PEMFC based CCHP system by meta-heuristics. Energy Reports, 2019.
10. Eslami, M., et al., A New Formulation to Reduce the Number of Variables and Constraints to Expedite SCUC in Bulky Power Systems. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, 2018: p. 1-11.
11. Fan, X., et al., High voltage gain DC/DC converter using coupled inductor and VM techniques. IEEE Access, 2020. 8: p. 131975-131987.
12. Firouz, M.H. and N. Ghadimi, Concordant controllers based on FACTS and FPSS for solving wide-area in multi-machine power system. Journal of Intelligent & Fuzzy Systems, 2016. 30(2): p. 845-859.
13. Guo, Y., et al., An optimal configuration for a battery and PEM fuel cell-based hybrid energy system using developed Krill herd optimization algorithm for locomotive application. Energy Reports, 2020. 6: p. 885-894.
14. Ghadimi, N., An adaptive neuro‐fuzzy inference system for islanding detection in wind turbine as distributed generation. Complexity, 2015. 21(1): p. 10-20.
15. Yuan, Z., et al., Probabilistic decomposition-based security constrained transmission expansion planning incorporating distributed series reactor. IET Generation, Transmission & Distribution, 2020. 14(17): p. 3478-3487.
16. Meng, Q., et al., A single-phase transformer-less grid-tied inverter based on switched capacitor for PV application. Journal of Control, Automation and Electrical Systems, 2020. 31(1): p. 257-270.
17. Gheydi, et al., Planning in microgrids with conservation of voltage reduction. IEEE Systems Journal, 2016. 12(3): p. 2782-2790.
18. Hosseini Firouz, et al., Optimal preventive maintenance policy for electric power distribution systems based on the fuzzy AHP methods. Complexity, 2016. 21(6): p. 70-88.
19. Yuan, Z., et al., A new technique for optimal estimation of the circuit-based PEMFCs using developed Sunflower Optimization Algorithm. Energy Reports, 2020. 6: p. 662-671.
20. Yu, D., et al., System identification of PEM fuel cells using an improved Elman neural network and a new hybrid optimization algorithm. Energy Reports, 2019. 5: p. 1365-1374.
21. Xiong, G., et al., A simplified competitive swarm optimizer for parameter identification of solid oxide fuel cells. Energy Conversion and Management, 2020. 203: p. 112204.
22. Alhumade, H., et al., Optimal Parameter Estimation Methodology of Solid Oxide Fuel Cell Using Modern Optimization. Mathematics, 2021. 9(9): p. 1066.
23. Yang, B., et al., Extreme learning machine based meta-heuristic algorithms for parameter extraction of solid oxide fuel cells. Applied Energy, 2021. 303: p. 117630.
24. Ba, S., D. Xia, and E.M. Gibbons, Model identification and strategy application for Solid Oxide Fuel Cell using Rotor Hopfield Neural Network based on a novel optimization method. International Journal of Hydrogen Energy, 2020. 45(51): p. 27694-27704.
25. Jia, H. and B. Taheri, Model identification of Solid Oxide Fuel Cell using hybrid Elman Neural Network/Quantum Pathfinder algorithm. Energy Reports, 2021. 7: p. 3328-3337.
26. Fei, X., et al., Optimal configuration and energy management for combined solar chimney, solid oxide electrolysis, and fuel cell: a case study in Iran. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2019: p. 1-21.
27. Tian, M.-W., et al., New optimal design for a hybrid solar chimney, solid oxide electrolysis and fuel cell based on improved deer hunting optimization algorithm. Journal of Cleaner Production, 2020. 249: p. 119414.
28. Zhi, Y., et al., New approaches for regulation of solid oxide fuel cell using dynamic condition approximation and STATCOM. International Transactions on Electrical Energy Systems: p. e12756.
29. Ramezani, M., et al., A new optimal energy management strategy based on improved multi-objective antlion optimization algorithm: applications in smart home. SN Applied Sciences, 2020. 2(12): p. 1-17.
30. Yang, Z., et al., Model parameter estimation of the PEMFCs using improved Barnacles Mating Optimization algorithm. Energy, 2020. 212: p. 118738.
31. Yanda, L., Z. Yuwei, and N. Razmjooy, Optimal arrangement of a micro-CHP system in the presence of fuel cell-heat pump based on metaheuristics. International Journal of Ambient Energy, 2020: p. 1-12.
32. Al-Betar, M.A., et al., Coronavirus herd immunity optimizer (CHIO). Neural Computing and Applications, 2021. 33(10): p. 5011-5042.
33. Tizhoosh, H.R. 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). 2005. IEEE.
34. Ramezani, M., D. Bahmanyar, and N. Razmjooy, A New Improved Model of Marine Predator Algorithm for Optimization Problems. Arabian Journal for Science and Engineering, 2021: p. 1-24.
35. Wang, Z., et al., A new configuration of autonomous CHP system based on improved version of marine predators algorithm: A case study. International Transactions on Electrical Energy Systems: p. e12806.
36. Razmjooy, N., M. Khalilpour, and M. Ramezani, A new meta-heuristic optimization algorithm inspired by FIFA world cup competitions: theory and its application in PID designing for AVR system. Journal of Control, Automation and Electrical Systems, 2016. 27(4): p. 419-440.
37. Yang, D., Z. Liu, and J. Zhou, Chaos optimization algorithms based on chaotic maps with different probability distribution and search speed for global optimization. Communications in Nonlinear Science and Numerical Simulation, 2014. 19(4): p. 1229-1246.
38. Khishe, M. and M.R. Mosavi, Chimp optimization algorithm. Expert Systems with Applications, 2020: p. 113338.
39. Cuevas, E., F. Fausto, and A. González, The Locust Swarm Optimization Algorithm, in New Advancements in Swarm Algorithms: Operators and Applications. 2020, Springer. p. 139-159.
40. Dhiman, G. and V. Kumar, Emperor penguin optimizer: A bio-inspired algorithm for engineering problems. Knowledge-Based Systems, 2018. 159: p. 20-50.
41. Wu, X.-J., et al., Modeling a SOFC stack based on GA-RBF neural networks identification. Journal of Power Sources, 2007. 167(1): p. 145-150.
42. Calise, F., et al., Simulation and exergy analysis of a hybrid solid oxide fuel cell (SOFC)–gas turbine system. Energy, 2006. 31(15): p. 3278-3299.
43. Liu, X., et al., Normalization methods for the analysis of unbalanced transcriptome data: a review. Frontiers in bioengineering and biotechnology, 2019. 7: p. 358.