PEMFC IDENTIFICATION USING DEEP LEARNING DEVELOPED BY IMPROVED DEER HUNTING OPTIMIZATION ALGORITHM

Zili Yin and Navid Razmjooy

References

  1. [1] C. Yan, J. Chen, H. Liu, and H. Lu, Model-based fault tolerant control for the thermal management of PEMFC systems, IEEE Transactions on Industrial Electronics, 67(4), 2020, 2875–2884.
  2. [2] A. Nouri, H. Khodaei, A. Darvishan, S. Sharifian, and N. Ghadimi, Optimal performance of fuel cell-CHP-battery based micro-grid under real-time energy management: An epsilon constraint method and fuzzy satisfying approach, Energy, 159, 2018, 121–133.
  3. [3] M.A. Cropper, S. Geiger, and D.M. Jollie, Fuel cells: A survey of current developments, Journal of Power Sources, 131(1–2), 2004, 57–61.
  4. [4] D.M. Bernardi and M.W. Verbrugge, A mathematical model of the solid-polymer-electrolyte fuel cell, Journal of the Elec- trochemical Society, 139(9), 1992, 2477–2491.
  5. [5] E. El-Hay, M. El-Hameed, and A. El-Fergany, Optimized parameters of SOFC for steady state and transient simulations using interior search algorithm, Energy, 166, 2019, 451–461.
  6. [6] P.N. Papadopoulos, M. Kandyla, P. Kourtza, T.A. Papadopoulos, and G.K. Papagiannis, Parametric analysis of the steady state and dynamic performance of proton exchange membrane fuel cell models, Renewable Energy, 71, 2014, 23–31.
  7. [7] P.A. Kumar, M. Geetha, K. Chandran, and P. Sanjeevikumar, PEM fuel cell system identification and control, in Advances in smart grid and renewable energy (Singapore: Springer, 2018), 449–457.
  8. [8] M. Solsona, C. Kunusch, and C. Ocampo-Martinez, Control- oriented model of a membrane humidifier for fuel cell applications, Energy Conversion and Management, 137, 2017, 121–129.
  9. [9] A. Shamel and N. Ghadimi, Hybrid PSOTVAC/BFA technique for tuning of robust PID controller of fuel cell voltage, IJCT 23(3), 2016, 171–178.
  10. [10] N. Razmjooy, F.R. Sheykhahmad, and N. Ghadimi, A hybrid neural network–world cup optimization algorithm for melanoma detection, Open Medicine, 13(1), 2018, 9–16.
  11. [11] M.T. Hagh, H. Ebrahimian, and N. Ghadimi, Hybrid intelligent water drop bundled wavelet neural network to solve the islanding detection by inverter-based DG, Frontiers in Energy, 9(1), 2015, 75–90.
  12. [12] H. Hosseini, B. Tousi, N. Razmjooy, and M. Khalilpour, Design robust controller for automatic generation control in restructured power system by imperialist competitive algorithm, IETE Journal of Research, 59(6), 2013, 745–752.
  13. [13] A. Jalili and N. Ghadimi, Hybrid harmony search algorithm and fuzzy mechanism for solving congestion management problem in an electricity market, Complexity, 21(S1), 2016, 90–98.
  14. [14] N. Razmjooy, M. Ramezani, and A. Namadchian, A new LQR optimal control for a single-link flexible joint robot manipulator based on grey wolf optimizer, Majlesi Journal of Electrical Engineering, 10(3), 2016, 53.
  15. [15] A. Rezazadeh, M. Sedighizadeh, and M. Karimi, Proton ex- change membrane fuel cell control using a predictive control based on neural network, International Journal of Computer and Electrical Engineering, 2(1), 2010, 81.
  16. [16] N. Razmjooy and M. Ramezani, Training wavelet neural net- works using hybrid particle swarm optimization and gravitational search algorithm for system identification, 6(21), 2016, 2987–2997.
  17. [17] N.Y. Steiner, D. Hissel, P. Mocoteguy, and D. Candusso, Diagnosis of polymer electrolyte fuel cells failure modes (flooding & drying out) by neural networks modeling, International Journal of Hydrogen Energy, 36(4), 2011, 3067–3075.
  18. [18] D.F. Pereira, F.C. Lopes, and E.H. Watanabe, Prediction of PEMFC stack efficiency using recurrent neural networks, 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE), IEEE, Edinburgh, UK, 2017, 1017–1022.
  19. [19] A. Namadchian, M. Ramezani, and N. Razmjooy, A new meta-heuristic algorithm for optimization based on variance reduction of Gaussian distribution, Majlesi Journal of Electrical Engineering, 10(4), 2016, 49.
  20. [20] G.E. Hinton, S. Osindero, and Y.-W. Teh, A fast learning algorithm for deep belief nets, Neural Computation, 18(7), 2006, 1527–1554.
  21. [21] R. Salakhutdinov and G. Hinton, Deep Boltzmann machines, in Proceedings of Machine Learning Research, 2009, 448–455.
  22. [22] N. Razmjooy, 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, 27(4), 2016, 419–440.
  23. [23] M. Bagheri, A. Sultanbek, O. Abedinia, M.S. Naderi, M.S. Naderi, and N. Ghadimi, Multi-objective shark smell optimization for solving the reactive power dispatch problem, 2018 IEEE International Conf. on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), IEEE, 2018, 1–6.
  24. [24] S. Arora and S. Singh, Butterfly optimization algorithm: A novel approach for global optimization, Soft Computing, 23(3), 2019, 715–734.
  25. [25] B.S. Mousavi and F. Soleymani, Semantic image classification by genetic algorithm using optimised fuzzy system based on Zernike moments, Signal, Image and Video Processing, 8(5), 2014, 831–842.
  26. [26] G. Brammya, S. Praveena, N. Ninu Preetha, R. Ramya, B. Rajakumar, and D. Binu, Deer hunting optimization algorithm: A new nature-inspired meta-heuristic paradigm, The Computer Journal, 2019, bxy133, https://doi.org/10.1093/comjnl/bxy133.
  27. [27] X. Li, P. Niu, and J. Liu, Combustion optimization of a boiler based on the chaos and Levy flight vortex search algorithm, Applied Mathematical Modelling, 58, 2018, 3–18.
  28. [28] C. Rim, S. Piao, G. Li, and U. Pak, A niching chaos optimization algorithm for multimodal optimization, Soft Computing, 22(2), 2018, 621–633.
  29. [29] J.C. Bansal, Particle swarm optimization, in Evolutionary and swarm intelligence algorithms (Singapore: Springer, 2019), 11–23.
  30. [30] J.H. Holland, Genetic algorithms, Scientific American, 267(1), 1992, 66–73.
  31. [31] O. Abedinia, N. Amjady, and N. Ghadimi, Solar energy forecasting based on hybrid neural network and improved metaheuristic algorithm, Computational Intelligence, 34(1), 2018, 241–260.
  32. [32] L. Zhang and N. Wang, An adaptive RNA genetic algorithm for modeling of proton exchange membrane fuel cells, International Journal of Hydrogen Energy, 38(1), 2013, 219–228.
  33. [33] Z.J. Mo, X.J. Zhu, L.Y. Wei, and G.Y. Cao, Parameter optimization for a PEMFC model with a hybrid genetic algorithm, International Journal of Energy Research, 30(8), 2006, 585–597.

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