MULTILAYER ADAPTIVE NEURAL NETWORK FOR DC LINK VOLTAGE REGULATION IN GRID CONNECTED HYBRID SYSTEMS

Masood I. Nazir, Ikhlaq Hussain, and Aijaz Ahmad

References

  1. [1] A. Talwariya, P. Singh, and M. Kolhe, A stepwise power tariff model with game theory based on Monte-Carlo simulation and its applications for household, agricultural, commercial and industrial consumers, International Journal of Electrical Power & Energy Systems, 111, 2019, 14–24.
  2. [2] J. Hwang, L. Lai, W. Wu, and W. Chang, Dynamic modeling of a photovoltaic hydrogen fuel cell hybrid system, International Journal of Hydrogen Energy, 34(23), 2009, 9531–9542.
  3. [3] R.K. Agarwal, I. Hussain, and B. Singh, 3-phase single-stage grid tied solar PV ECS using PLL-less fast CTF control technique, IET Power Electronics, 10(2), 2017, 178–188.
  4. [4] K. Javed, H. Ashfaq, and R. Singh, Analysis and sizing of hybrid energy storage system (HESS) topologies for solar photovoltaic applications, International Journal of Power and Energy Systems, 39, 2019. DOI: 10.2316/J.2019.203-0100
  5. [5] B. Vetrivelan and P. Kareem, Hybrid algorithm for tracking maximum power in solar PV array under partially shaded condition, International Journal of Power & Energy Systems, 39, 2019. DOI: 10.2316/J.2019.203-0167
  6. [6] M. J. Beevi, Shanifa and V. George, A novel high-performance instantaneous resistance MPPT algorithm for PV systems, International Journal of Power and Energy Systems, 39, 2019. DOI: 10.2316/J.2019.203-0141
  7. [7] G. Suvire and P. Mercado, Active power control of a flywheel energy storage system for wind energy applications, IET Renewable Power Generation, 6(1), 2012, 9–16.
  8. [8] B. Singh, A. Chandra, and K. Al-Haddad, Power quality: Problems and mitigation techniques (Hoboken, New Jersey: Wiley, 2014).
  9. [9] M. Jawarneh, A. Domijan, and W. Luo, The performance of maximum power point tracking (MPPT) algorithms for PV systems, International Journal of Power and Energy Systems, 35, 2015. DOI: 10.2316/Journal.203.2015.4.203-6153
  10. [10] T. Takagi and M. Sugeno, Fuzzy identification of systems and its applications to modeling and control, IEEE Transactions on Systems, Man, and Cybernetics, SMC-15(1), 1985, 116–132.
  11. [11] S. Yellagoud, P. Talluri, and G. Sreenivas, An ANFIS based fault location in power distribution networks, International Journal of Power and Energy Systems, 36, 2016. DOI: 10.2316/Journal.203.2016.3.203-6245
  12. [12] S. Gupta, R. Garg, and A. Singh, Wind/fuel cell hybrid system based on ANFIS-HLMS control algorithm for VSC, International Journal of Power and Energy Systems, 40, 2020. DOI: 10.2316/J.2020.203-0192
  13. [13] E.-M. Boufounas, J. Boumhidi, M. Ouriagli, and I. Boumhidi, A robust power control of the DFIG wind turbine based on general regression neural network and APSO algorithm, International Journal of Power & Energy Systems, 35, 2015. DOI: 10.2316/Journal.203.2015.2.203-6132
  14. [14] M. Nazir, I. Hussain, and A. Ahmad, Improved adaptive control algorithm of a grid-connected PMSG-based wind energy conversion system, in M.N. Favorskaya, S. Mekhilef, R.K. Pandey, and N. Singh (eds.), Innovations in electrical and electronic engineering (Singapore: Springer, 2020), pp. 167–180.
  15. [15] S. Mishra, I. Hussain, G. Pathak, and B. Singh, DPLL-based control of a hybrid wind–solar grid connected inverter in the distribution system, IET Power Elecectronic, 11(5), 2018, 952–960.
  16. [16] R.K. Agarwal, I. Hussain, and B. Singh, LMF-based control algorithm for single stage three-phase grid integrated solar PV system, IEEE Tranactions on Sustainable Energy, 7(4), 2016, 1379–1387.
  17. [17] D. Jin, J. Chen, C. Richard, and J. Chen, Model-driven online parameter adjustment for zero-attracting LMS, Signal Processing, 152, 2018, 373–383.
  18. [18] J.R. Jang, ANFIS: Adaptive-Network-Based Fuzzy Inference System, IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 1993, 665–685.

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