Particle swarm optimization algorithm for dynamic synchronization of smart grid

References

• Abu-Taha, R. 2011. Multi-criteria applications in renewable energy analysis: A literature review. PICMET Portland International Conference on Management of Engineering and Technology 1–8.
   Google Scholar
• Barnes, M., T. Green, R. Lasseter, and N. Hatziargyriou. 2007. Real-world microgrids- an overview. 2007 IEEE International Conference on System of Systems Engineering 1–8. doi:10.1109/SYSOSE.2007.4304255.
   Google Scholar
• Blaabjerg, F., R. Teodorescu, M. Liserre, and A. V. Timbus. 2006. Overview of control and grid synchronization for distributed power generation systems. IEEE Transactions on Industrial Electronics 53 (5):1398–409. doi:10.1109/TIE.2006.881997.
   Web of Science ®Google Scholar
• Cho, C., J. H. Jeon, J. Y. Kim, S. Kwon, K. Park, and S. Kim. 2011. Active synchronizing control of a microgrid. IEEE Transactions on Power Electronics 26 (12):3707–19. doi:10.1109/TPEL.2011.2162532.
   Web of Science ®Google Scholar
• Dranka, G. G., and P. Ferreira. 2020. Towards a smart grid power system in Brazil: Challenges and opportunities. Energy Policy 136 (January):111033. doi:10.1016/J.ENPOL.2019.111033.
   Web of Science ®Google Scholar
• Eberhart, R. C., and Y. Shi. 2001. Particle swarm optimization: Developments, applications and resources. Proceedings IEEE Conference Evolution Computing ICEC 1:81–86. doi:10.1109/cec.2001.934374.
   Google Scholar
• Espina, E., J. Llanos, C. Burgos-Mellado, R. Cárdenas-Dobson, M. Martínez-Gómez, and D. Sáez. 2020. Distributed control strategies for microgrids: An overview. IEEE Access 8:193412–48. doi:10.1109/ACCESS.2020.3032378.
   Web of Science ®Google Scholar
• Fan, D., Y. Ren, Q. Feng, Y. Liu, Z. Wang, and J. Lin. June. 01, 2021. Restoration of smart grids: Current status, challenges, and opportunities. In Renewable and Sustainable Energy Reviews, Vol. 143, Elsevier Ltd. doi:10.1016/j.rser.2021.110909.
   Google Scholar
• Farhangi, H. 2010. The path of the smart grid. IEEE Power and Energy Magazine 8 (1):18–28. doi:10.1109/MPE.2009.934876.
   Web of Science ®Google Scholar
• Guerrero, J. M., J. C. Vasquez, J. Matas, L. G. De Vicuña, and M. Castilla. 2011. Hierarchical control of droop-controlled AC and DC microgrids - A general approach toward standardization. IEEE Transactions on Industrial Electronics 58 (1):158–72. doi:10.1109/TIE.2010.2066534.
   Web of Science ®Google Scholar
• Heppner, F. H., and U. Grenander. 1990. A stochastic non-linear model for bird flocking. The Ubiquity of Chaos 233–38.
   Google Scholar
• Hossain, M. A., H. R. Pota, M. J. Hossain, and F. Blaabjerg. 2019. Evolution of microgrids with converter-interfaced generations: Challenges and opportunities. International Journal of Electrical Power & Energy Systems 109 (February):160–86. doi:10.1016/j.ijepes.2019.01.038.
   Web of Science ®Google Scholar
• Jadeja, R., A. Ved, T. Trivedi, and G. Khanduja. 2020. Control of power electronic converters in AC microgrid. Power Systems 27 (11):329–55. doi:10.1007/978-3-030-23723-3_13.
   Google Scholar
• Justo, J. J., F. Mwasilu, J. Lee, and J. W. Jung. 2013. AC-microgrids versus DC-microgrids with distributed energy resources: A review. Renewable and Sustainable Energy Reviews 24:387–405. doi:10.1016/j.rser.2013.03.067.
   Web of Science ®Google Scholar
• Kennedy, J., and R. C. Eberhart. 1995. Particle swarm optimisation. Studies in Computational Intelligence 927:5–13. doi:10.1007/978-3-030-61111-8_2.
   Google Scholar
• Kennedy, J., and W. M. Spears, “Matching algorithms to problems: An experimental test of the particle swarm and some genetic algorithms on the multimodal problem generator,” Proc. IEEE Conf. Evol. Comput. ICEC, Anchorage, AK, pp. 78–83, 1998. doi: 10.1109/icec.1998.699326.
   Google Scholar
• Kewat, S., and B. Singh. 2020 November. Grid synchronization of WEC-PV-BES based distributed generation system using robust control strategy. IEEE Transactions on Industry Applications 56(6):7088–98. doi:10.1109/TIA.2020.3021060.
   Web of Science ®Google Scholar
• Lasseter, R. H. 2002. MicroGrids. IEEE Power Engineering Society Winter Meeting 1:305–08. doi:10.1109/PESW.2002.985003.
   Google Scholar
• Lasseter, R. H., “Extended CERTS microgrid,” IEEE Power Energy Soc. 2008 Gen. Meet. Convers. Deliv. Electr. Energy 21st Century, PES, Pittsburgh, PA, 2008. doi: 10.1109/PES.2008.4596492.
   Google Scholar
• Lasseter, R. H., and P. Paigi. 2004. Microgrid: A conceptual solution. PESC Rec. - IEEE Annual Power Electronics Specialists Conference 6:4285–90. doi:10.1109/PESC.2004.1354758.
   Google Scholar
• Long, Q., H. Yu, F. Xie, N. Lu, and D. Lubkeman. 2021. Diesel generator model parameterization for microgrid simulation using hybrid box-constrained levenberg-marquardt algorithm. IEEE Transactions on Smart Grid 12 (2):943–52. doi:10.1109/TSG.2020.3026617.
   Web of Science ®Google Scholar
• Mariam, L., M. Basu, and M. F. Conlon. 2016. Microgrid: Architecture, policy and future trends. Renewable and Sustainable Energy Reviews 64:477–89. doi:10.1016/j.rser.2016.06.037.
   Web of Science ®Google Scholar
• Martínez-Filgueira, P., E. Zulueta, A. Sánchez-Chica, U. Fernández-Gámiz, and J. Soriano. 2019. Multi-objective particle swarm based optimization of an air jet impingement system. Energies 12 (9):1–16. doi:10.3390/en12091627.
   Web of Science ®Google Scholar
• Moghaddam, B. F., R. Ruiz, and S. J. Sadjadi. 2012. Vehicle routing problem with uncertain demands: An advanced particle swarm algorithm. Computers & Industrial Engineering 62 (1):306–17. doi:10.1016/j.cie.2011.10.001.
   Web of Science ®Google Scholar
• Mollah, M. B., J. Zhao, D. Niyato, K.-Y. Lam, X. Zhang, A. M. Y. M. Ghias, L. H. Koh, and L. Yang. 2021. Blockchain for future smart grid: A comprehensive survey. IEEE Internet of Things Journal 8 (1):18–43. doi:10.1109/JIOT.2020.2993601.
   Web of Science ®Google Scholar
• Moreno Escobar, J. J., O. Morales Matamoros, R. Tejeida Padilla, I. Lina Reyes, and H. Quintana Espinosa. 2021. A comprehensive review on smart grids: Challenges and opportunities. Sensors (Basel) 21 (21):1–41. doi:10.3390/s21216978.
   Web of Science ®Google Scholar
• Mu, X., A. Ottino, F. M. Ferreira, and G. Zervas. 2022. Optimization of 125-μm heterogeneous multi-core fibre design using artificial intelligence. IEEE Journal of Selected Topics in Quantum Electronics 28 (4):1–13. doi:10.1109/JSTQE.2021.3104821.
   Web of Science ®Google Scholar
• Nejabatkhah, F., and Y. W. Li. 2015. Overview of power management strategies of hybrid AC/DC microgrid. IEEE Transactions on Power Electronics 30 (12):7072–89. doi:10.1109/TPEL.2014.2384999.
   Web of Science ®Google Scholar
• Nickabadi, A., M. M. Ebadzadeh, and R. Safabakhsh. 2011. A novel particle swarm optimization algorithm with adaptive inertia weight. Applied Soft Computing Journal 11 (4):3658–70. doi:10.1016/j.asoc.2011.01.037.
   Web of Science ®Google Scholar
• Olivares, D. E., A. Mehrizi-Sani, A. H.Etemadi, C. A. Cañizares, R. Iravani, M. Kazerani, A. H. Hajimiragha, O. Gomis-Bellmunt, M. Saeedifard, R. Palma-Behnke, G. A. Jiménez-Estéve, and N. D. Hatziargyriou. 2014. Trends in microgrid control. IEEE Transactions on Smart Grid 5 (4):1905–19. doi:10.1109/TSG.2013.2295514.
   Web of Science ®Google Scholar
• Parhizi, S., H. Lotfi, A. Khodaei, and S. Bahramirad. 2015. State of the art in research on microgrids: A review. IEEE Access 3:890–925. doi:10.1109/ACCESS.2015.2443119.
   Web of Science ®Google Scholar
• Pepermans, G., J. Driesen, D. Haeseldonckx, R. Belmans, and W. D’haeseleer. 2005. Distributed generation: Definition, benefits and issues. Energy Policy 33 (6):787–98. doi:10.1016/j.enpol.2003.10.004.
   Web of Science ®Google Scholar
• Pontani, M., and B. A. Conway. 2010. Particle swarm optimization applied to space trajectories. Journal of Guidance, Control, and Dynamics 33 (5):1429–41. doi:10.2514/1.48475.
   Web of Science ®Google Scholar
• Sarker, E., P. Halder, M. Seyedmahmoudian, E. Jamei, B. Horan, S. Mekhilef, and A. Stojcevski. 2021. Progress on the demand side management in smart grid and optimization approaches. International Journal of Energy Research 45 (1):36–64. doi:10.1002/er.5631.
   Web of Science ®Google Scholar
• Sezi, T 1999. A new method for measuring power system frequency. IEEE/PES Transmission and Distribution Conference on A Vision Into the 21st Century, 11–16 April, NEW ORLEANS, LA, pp. 400–405. doi:10.1109/TDC.1999.755385.
   Google Scholar
• Shi, Y., and R. C. Eberhart. 1999. Empirical study of particle swarm optimization. Proceedings of the 1999 Congress on Evolutionary Computation 3:1945–50. doi:10.1109/CEC.1999.785511.
   Google Scholar
• Tuballa, M. L., and M. L. Abundo. 2016. A review of the development of Smart Grid technologies. Renewable and Sustainable Energy Reviews 59:710–25. doi:10.1016/j.rser.2016.01.011.
   Web of Science ®Google Scholar
• Tuppadung, Y., and W. Kurutach. 2000. Comparing nonlinear inertia weights and constriction factors in particle swarm optimization. International Journal of Knowledge-Based and Intelligent Engineering Systems 15 (2):65–70. doi:10.3233/KES-2010-0211.
   Google Scholar
• Wu, W. C., and M. S. Tsai. 2011. Application of enhanced integer coded particle swarm optimization for distribution system feeder reconfiguration. IEEE Transactions on Power Systems 26 (3):1591–99. doi:10.1109/TPWRS.2010.2094212.
   Web of Science ®Google Scholar
• Zhong, Q. C., P. L. Nguyen, Z. Ma, and W. Sheng. 2014. Self-synchronized synchronverters: Inverters without a dedicated synchronization unit. IEEE Transactions on Power Electronics 29 (2):617–30. doi:10.1109/TPEL.2013.2258684.
   Web of Science ®Google Scholar
• Zhou, Y. P., L. J. Tang, J. Jiao, D. D. Song, J. H. Jiang, and R. Q. Yu. 1998. A modified particle swarm optimizer. Journal of Chemical Information and Modeling 49 (5):1144–53. doi:10.1021/ci800374h.
   Web of Science ®Google Scholar
• Zhu, P., H. Saadati, and M. Khayatnezhad. 2021. Application of probability decision system and particle swarm optimization for improving soil moisture content. Water Supply 21 (8):4145–52. doi:10.2166/ws.2021.169.
   Google Scholar
• Zhu, Z., B. Tang, and J. Yuan. 2017. Multirobot task allocation based on an improved particle swarm optimization approach. International Journal of Advanced Robotic Systems 14 (3):1–22. doi:10.1177/1729881417710312.
   Web of Science ®Google Scholar