References
- [1] W. R. Sultana, S. K. Sahoo, S. Sukchai, S. Yamuna, and D. Venkatesh, “A review on state of art development of model predictive control for renewable energy applications”, Renewable and Sustainable Energy Reviews, pp. 391-406, 2017.10.1016/j.rser.2017.03.058
- [2] M. Santolo and S. Vincenzo, “Discrete–time integral sliding mode control with disturbances compensation and reduced chattering for PV grid–connected inverter”, Journal of Electrical Engineering, no. 2, pp. 61–69, 2015.10.1515/jee-2015-0010
- [3] M. N. Hamidi, D. Ishak, and M. A. A. M. Zainuri, “Comparative evaluation of multilevel DC link inverter using symmetrical and asymmetrical DC sources”, Journal of Electrical Engineering, no. 2, pp. 122–129, 2019.10.2478/jee-2019-0018
- [4] S. Saha, M. E. Haque, C. P. Tan, M. A. Mahmud, M. T. Arif, S. Lyden, and N. Mendis, “Diagnosis and mitigation of voltage and current sensors malfunctioning in a grid connected PV system”, International Journal of Electrical Power and Energy Systems, 115, 2020.10.1016/j.ijepes.2019.105381
- [5] V. R. Segovia, T. H¨agglund, and K. J. Åström, “Measurement noise filtering for PID controllers”, Journal of Process Control, pp. 299-313, 2014.10.1016/j.jprocont.2014.01.017
- [6] I. S. Mohamed, S. Rovetta, T. D. Do, T. Dragicevic, and A. A. Z. Diab, “A neural-network-based model predictive control of three-phase inverter with an output LC Filter”, IEEE Access, pp. 124737-124749, 2019.
- [7] V. Blahnik, J. Talla, and Z. Peroutka, “Current source based on H–bridge inverter with output LCL filter”, Journal of Electrical Engineering, no. 5, pp. 287–291, 2015.10.2478/jee-2015-0047
- [8] T. R. Biyanto, N. Sehamat, N. A. Sordi, and H. Zabiri, “Optimization of PID controller tuning parameters for multivariable system using Duelist algorithm”, IOP Conference Series: Materials Science and Engineering, no. 1, 2018.10.1088/1757-899X/458/1/012053
- [9] L. Hassaine, E. Olias, J. Quintero, and V. Salas, “Overview of power inverter topologies and control structures for grid connected photovoltaic systems”, Renewable and Sustainable Energy Reviews, pp. 796–807, 2014.10.1016/j.rser.2013.11.005
- [10] M. Parvez, M. F. M. Elias, N. A. Rahim, and N. Osman, “Current control techniques for three-phase grid interconnection of renewable power generation systems: A review”, Solar Energy, pp. 29–42, 2016.10.1016/j.solener.2016.05.029
- [11] S. J. Pinto and G. Panda, “Wavelet technique based islanding detection and improved repetitive current control for reliable operation of grid-connected PV systems”, International Journal of Electrical Power and Energy Systems, pp. 39–51, 2015.10.1016/j.ijepes.2014.11.008
- [12] A. Youssef, E.-T. Mohammed, and Z. Abdelhalim, “Design and simulation of fuzzy controller for three phase grid connected PV systems”, International Journal of Engineering Technology, no. 4, 2018.
- [13] A. A. S. Mohamed, H. Metwally, A. El-Sayed, and S. I. Selem, “Predictive neural network based adaptive controller for grid-connected PV systems supplying pulse-load”, Solar Energy, pp. 139–147, 2019.10.1016/j.solener.2019.09.018
- [14] A. Merabet, L. Labib, A. M. Y. M. Ghias, A. Aldurra, and M. Debbouza, “Dual-mode operation based second-order sliding mode control for grid-connected solar photovoltaic energy system”, International Journal of Electrical Power and Energy Systems, pp. 459–474, 2019.10.1016/j.ijepes.2019.04.036
- [15] A. K. R. Dadinaboina, K. R. Pedakota, S. Chinnathambi, and R. R. Senige, “Improved power quality with an adaptive grid-forming inverter control scheme in solar PV system”, International Transactions on Electrical Energy, vol. 31, no. 9, pp. 1–20, 2021.10.1002/2050-7038.13009
- [16] R. Ravi and S. J. Mija, “Design of Brain Emotional Learning Based Intelligent Controller (BELBIC) for uncertain systems ’ IEEE International Conference on advanced communication, control and computing technologies (ICACCCT), 2014.10.1109/ICACCCT.2014.7019265
- [17] C. Lucas, D. Shahmirzadi, and N. Sheikholeslami, “Introducing belbic: Brain emotional learning based intelligent controller”, Intelligent Automation and Soft Computing, vol. 10, no. 1, pp. 11-21, 2004.10.1080/10798587.2004.10642862
- [18] C. Balkenius and J. Morén, “Emotional learning: A computational model of the amygdala”, Cybernetics Systems, no. 06, pp. 611-636, 2001.10.1080/019697201750361283
- [19] R. Sankarganesh and S. Thangavel, “Performance analysis of various DC-DC converters with optimum controllers for PV applications”, Research Journal of Applied Sciences, Engineering and Technology, no. 8, pp. 929–941, 2014.10.19026/rjaset.8.1055
- [20] J. P. Coelho, M. Braz-César, and J. Gonalves, “BELBIC Based Step-Down Controller Design Using PSO”, [20] J, P. Coelho, M. Braz-Csar and J. Gonalves, ¿—BELBIC Based Step-Down Controller Design Using PSO—¡, First international conference OL2A, 2021.10.1007/978-3-030-91885-9_25
- [21] S. K. Kumar and S. Chandramohan, “BELBIC control of DSTATCOM for voltage regulation”, Journal of Control Engineering and Applied Informatics, no. 02, 2016.
- [22] A. D. Falehi, “Optimal Fractional Order BELBIC to Ameliorate Small Signal Stability of Interconnected Hybrid Power System”, Environmental Progress and Sustainable Energy, vol. 38, no. 5, 2019.10.1002/ep.13208
- [23] C. Rizzi, C. G. Johnson, F. Fabris, and P. A. Vargas, “A Situation-Aware Fear Learning (SAFEL) model for robots”, Neuro-computing, pp. 32-47, 2017.10.1016/j.neucom.2016.09.035
- [24] F. Blaabjerg, R. Teodorescu, M. Liserre, and A. V. Timbus, “Overview of control and grid synchronization for distributed power generation systems”, IEEE Transactions on Industrial, vol. 53, no. 5, pp. 1398–1409, 2006.
- [25] A. Benrabah and D. Xu, “UDE-based current control of grid-connected photovoltaic inverters”, COMPEL - The international journal for computation and mathematics electrical and electronic engineering, no. 1, pp. 183-198, 2019.10.1108/COMPEL-02-2018-0092
- [26] N. Sheikholeslami, D. Shahmirzadi, E. Semsar, C. Lucas, and M. J. Yazdanpanah, “Applying brain emotional learning algorithm for multivariable control of HVAC systems”, Journal of Intelligent and Fuzzy Systems, no. 1, pp. 35-46, 2006.
- [27] C. Rizzi, C. G. Johnson, and P. A. Vargas, “Improving the Predictive Performance of SAFEL: A Situation-Aware Fear Learning Model”, [27] C, Rizzi, C. G. Johnson, and P. A. Vargas, ¿—Improving the Predictive Performance of SAFEL: A Situation-Aware Fear Learning Model—¡, IEEE International Workshop on Robot and Human Communication (ROMAN), 2016.
- [28] R. R. Rhinehart, “Automated steady and transient state identification in noisy processes”, IEEE American Control Conference (ACC), 2013.10.1109/ACC.2013.6580530
- [29] Z. Beheshti and S. Z. M. Hashim, “A review of emotional learning and it’s utilization in control engineering”, International Journal of Advances Soft Computing and its Applications, no. 2, 2010.
- [30] M. S. Mahmoud, Advanced Control Design with Application to Electromechanical Systems, Elsevier Inc, 2018.10.1016/B978-0-12-814543-2.00006-0
- [31] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press. 2016.