A hybrid global maximum power point tracking of partially shaded PV system under load variation by using adaptive salp swarm and differential evolution – perturb & observe technique

References

• Aygül, K., M. Cikan, T. Demirdelen, and M. Tumay. 2019. Butterfly optimization algorithm based maximum power point tracking of photovoltaic systems under partial shading condition. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects :1–19. doi:10.1080/15567036.2019.1677818.
   Web of Science ®Google Scholar
• Ayop, R., and C. W. Tan. 2018. Design of boost converter based on maximum power point resistance for photovoltaic applications. Solar Energy 160:322–35. doi:10.1016/j.solener.2017.12.016.
   Web of Science ®Google Scholar
• Balat, M. 2006. Solar technological progress and use of solar energy in the world. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 28 (10):979–94. doi:10.1080/009083190953409.
   Web of Science ®Google Scholar
• Cubas, J., S. Pindado, and C. D. Manuel. 2014. Explicit expressions for solar panel equivalent circuit parameters based on analytical formulation and the lambert w-function. Energies 7 (7):1–18. doi:10.3390/en7074098.
   Web of Science ®Google Scholar
• Dadj, A., N. Djongyang, J. D. Kana, and R. Tchinda. 2016. Maximum power point tracking methods for photovoltaic systems operating under partially shaded or rapidly variable insolation conditions: A review paper. International Journal of Sustainable Engineering 9 (4):224–39. doi:10.1080/19397038.2016.1149525.
   Web of Science ®Google Scholar
• Elgendy, M. A., B. Zahawi, and D. J. Atkinson. 2013. Assessment of the incremental conductance maximum power point tracking algorithm. IEEE Transactions on Sustainable Energy 4 (1):108–17. doi:10.1109/TSTE.2012.2202698.
   Web of Science ®Google Scholar
• Gandoman, F. H., S. H. Abdel Aleem, N. Omar, A. Ahmadi, and F. Q. Alenezi. 2018. Short-term solar power forecasting considering cloud coverage and ambient temperature variation effects. Renewable Energy 123:793–805. doi:10.1016/j.renene.2018.02.102.
   Web of Science ®Google Scholar
• Ishaque, K., Z. Salam, M. Amjad, and S. Mekhilef. 2012. An improved particle swarm optimization (pso)based mppt for pv with reduced steady-state oscillation. IEEE Transactions on Power Electronics 27 (8):3627–38. doi:10.1109/TPEL.2012.2185713.
   Web of Science ®Google Scholar
• Izadbakhsh, M., A. Rezvani, and M. Gandomkar. 2014. Improvement of microgrid dynamic performance under fault circumstances using ANFIS for fast varying solar radiation and fuzzy logic controller for wind system. Archives of Electrical Engineering 63 (4):551–78. doi:10.2478/aee-2014-0038.
   Google Scholar
• Izadbakhsh, M., A. Rezvani, and M. Gandomkar. 2015. Dynamic response improvement of hybrid system by implementing ANN-GA for fast variation of photovoltaic irradiation and FLC for wind turbine. Archives of Electrical Engineering 64 (2):291–314. doi:10.1515/aee-2015-0024.
   Web of Science ®Google Scholar
• Koutroulis, E., and F. Blaabjerg. 2015. Overview of maximum power point tracking techniques for photovoltaic energy production systems. Electric Power Components and Systems 43 (12):1329–51. doi:10.1080/15325008.2015.1030517.
   Web of Science ®Google Scholar
• Krishnan, S., and K. Sathiyasekar. 2020. A novel salp swarm optimization MPP tracking algorithm for the solar photovoltaic systems under partial shading conditions. Journal of Circuits, Systems and Computers :2050017. doi:10.1142/S0218126620500176.
   Web of Science ®Google Scholar
• Kumar, N., I. Hussain, B. Singh, and B. K. Panigrahi. 2017. MPPT in dynamic condition of partially shaded PV system by using WODE technique. IEEE Transactions on Sustainable Energy 8 (3):1204–14. doi:10.1109/TSTE.2017.2669525.
   Web of Science ®Google Scholar
• Li, X., Q. Wang, H. Wen, and W. Xiao. 2019. Comprehensive studies on operational principles for maximum power point tracking in photovoltaic systems. IEEE Access 7:121407–20. doi:10.1109/ACCESS.2019.2937100.
   Web of Science ®Google Scholar
• Liu, F., S. Duan, F. Liu, B. Liu, and Y. Kang. 2008. A variable step size INC MPPT method for PV systems. IEEE Transactions on Industrial Electronics 55 (7):2622–28. doi:10.1109/TIE.2008.920550.
   Web of Science ®Google Scholar
• Ma, B., H. Ni, X. Zhu, and R. Zhao. 2019. A comprehensive improved salp swarm algorithm on redundant container deployment problem. IEEE Access 7:136452–70. doi:10.1109/ACCESS.2019.2933265.
   Web of Science ®Google Scholar
• Mäki, A., and S. Valkealahti. 2012. Power losses in long string and parallel-connected short strings of series-connected silicon- based photovoltaic modules due to partial shading conditions. IEEE Transactions on Energy Conversion 27 (1):173–83. doi:10.1109/TEC.2011.2175928.
   Web of Science ®Google Scholar
• Manganiello, P., M. Ricco, G. Petrone, E. Monmasson, and G. Spagnuolo. 2014. Optimization of perturbative PV MPPT methods through online system identification. IEEE Transactions on Industrial Electronics 61 (12):6812–21. doi:10.1109/TIE.2014.2317143.
   Web of Science ®Google Scholar
• Mathi, D. K., and C. H. Ramulu. 2019. Enhanced leader adaptive velocity particle swarm optimization based global maximum power point tracking technique for a pv string under partially shaded conditions. IET Renewable Power Generation (2):1–8. doi:10.1049/iet-rpg.2019.0575.
   Web of Science ®Google Scholar
• Mathi, D. K., and R. Chinthamalla. 2020. Global maximum power point tracking technique based on adaptive salp swarm algorithm and P&O techniques for a PV string under partially shaded conditions. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects :1–18. doi:10.1080/15567036.2020.1755391.
   Web of Science ®Google Scholar
• Mirjalili, S., A. H. Gandomi, S. Z. Mirjalili, S. Saremi, H. Faris, and S. M. Mirjalili. 2017. Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software 114:163–91. doi:10.1016/j.advengsoft.2017.07.002.
   Web of Science ®Google Scholar
• Miveh, M. R., M. F. Rahmat, M. W. Mustafa, A. A. Ghadimi, and A. Rezvani. 2016. An improved control strategy for a four-leg grid- forming power converter under unbalanced load conditions. Advances in Power Electronics 2016:1–14. doi:10.1155/2016/9123747.
   Google Scholar
• Mohamed, M. A., A. A. Z. Diab, and H. Rezk. 2019. Partial shading mitigation of PV systems via different meta-heuristic techniques. Renewable Energy 130:1159–75. doi:10.1016/j.renene.2018.08.077.
   Web of Science ®Google Scholar
• Mohanty, S., B. Subudhi, and P. K. Ray. 2016. A new MPPT design using grey wolf optimization technique for photovoltaic system under partial shading conditions. IEEE Transactions on Sustainable Energy 7 (1):181–88. doi:10.1109/TSTE.2015.2482120.
   Web of Science ®Google Scholar
• Mohanty, S., B. Subudhi, and P. K. Ray. 2017. A grey wolf-assisted perturb observe mppt algorithm for a PV system. IEEE Transactions on Energy Conversion 32 (1):340–47. doi:10.1109/TEC.2016.2633722.
   Web of Science ®Google Scholar
• Mousa, A., S. A. Aleem, and A. Ibrahim. 2016. Mathematical analysis of maximum power points and currents based maximum power point tracking in solar photovoltaic system: A solar powered water pump application. International Review of Electrical Engineering (IREE) 11:1. doi:10.15866/iree.v11i1.8137.
   Google Scholar
• Pandey, A., N. Dasgupta, and A. K. Mukerjee. 2008. High-performance algorithms for drift avoidance and fast tracking in solar MPPT System. IEEE Transactions on Energy Conversion 23 (2):681–89. doi:10.1109/TEC.2007.914201.
   Web of Science ®Google Scholar
• Patel, N., A. Kumar, N. Gupta, and B. C. Babu. 2020. Experimental investigations on voltage sourced inverter interfaced photovoltaic based distributed generation system. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects :1–23. doi:10.1080/15567036.2020.1781302.
   Web of Science ®Google Scholar
• Patel, N., N. Gupta, and B. C. Babu. 2019. Design, development, and implementation of grid-connected solar photovoltaic power conversion system. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects :1–20. doi:10.1080/15567036.2019.1668506.
   Web of Science ®Google Scholar
• Rezvani, A., A. Esmaeily, H. Etaati, and M. Mohammadinodoushan. 2019. Intelligent hybrid power generation system using new hybrid fuzzy-neural for photovoltaic system and RBFNSM for wind turbine in the grid connected mode. Frontiers in Energy 13 (1):131–48. doi:10.1007/s11708-017-0446-x.
   Web of Science ®Google Scholar
• Rezvani, A., and M. Gandomkar. 2017. Simulation and control of intelligent photovoltaic system using new hybrid fuzzy-neural method. Neural Computing & Applications 28 (9):2501–18. doi:10.1007/s00521-016-2210-2.
   Web of Science ®Google Scholar
• Selvamuthukumaran, R., Y. S. Kumar, and R. Gupta. 2016. Global maximum power point tracking of multiple pv modules under partially shaded condition using stepped comparison search. Electric Power Components and Systems 44 (12):1384–95. doi:10.1080/15325008.2016.1157106.
   Web of Science ®Google Scholar
• Seyedmahmoudian, M., R. Rahmani, S. Mekhilef, A. Maung Than Oo,  A. Stojcevski , T. K. Soon, and A. S. Ghandhari. 2015. Simulation and hardware implementation of new maximum power point tracking technique for partially shaded PV system using hybrid DEPSO method. IEEE Transactions on Sustainable Energy 6 (3):850–62. doi:10.1109/TSTE.2015.2413359.
   Web of Science ®Google Scholar
• Shi, J., W. Zhang, Y. Zhang, F. Xue, and T. Yang. 2015. MPPT for PV systems based on a dormant PSO algorithm. Electric Power Systems Research 123:100–07. doi:10.1016/j.epsr.2015.02.001.
   Web of Science ®Google Scholar
• Shi, J. Y., D. Y. Zhang, L. T. Ling, F. Xue , Ya-Jing Li , Zi-Jian Qin, and T. Yang. 2018. Dual-algorithm maximum power point tracking control method for photovoltaic systems based on grey wolf optimization and golden-section optimization. Journal of Power Electronics 18 (3):841–52. doi:10.6113/JPE.2018.18.3.841.
   Web of Science ®Google Scholar
• Soon, T. K., and S. Mekhilef. 2015. A fast-converging MPPT technique for photovoltaic system under fast-varying solar irradiation and load resistance. IEEE Transactions on Industrial Informatics 11 (1):176–86. doi:10.1109/TII.2014.2378231.
   Web of Science ®Google Scholar
• Sridhar, R., S. Jeevananthan, and P. Vishnuram. 2017. Particle swarm optimisation maximum power-tracking approach based on irradiation and temperature measurements for a partially shaded photovoltaic system. International Journal of Ambient Energy 38 (7):685–93. doi:10.1080/01430750.2016.1181570.
   Web of Science ®Google Scholar
• Subudhi, B., and R. Pradhan. 2013. A comparative study on maximum power point tracking techniques for photovoltaic power systems. IEEE Transactions on Sustainable Energy 4 (1):89–98. doi:10.1109/TSTE.2012.2202294.
   Web of Science ®Google Scholar
• Sundareswaran, K., V. V. kumar, and S. Palani. 2015. Application of a combined particle swarm optimization and perturb and observe method for MPPT in PV systems under partial shading conditions. Renewable Energy 75:308–17. doi:10.1016/j.renene.2014.09.044.
   Web of Science ®Google Scholar
• Sweelem, E. A., F. H. Fahmy, M. M. A. E. Aziz, P. Zacharias, and A. Mahmoudi. 1999. Increased efficiency in the conversion of solar energy to electric power. Energy Sources 21 (5):367–77. doi:10.1080/00908319950014696.
   Google Scholar
• Tey, K. S., S. Mekhilef, M. Seyedmahmoudian, B. Horan, A. T. Oo, and A. Stojcevski. 2018. Improved differential evolution-based MPPT algorithm using SEPIC for PV systems under partial shading conditions and load variation. IEEE Transactions on Industrial Informatics 14 (10):4322–33. doi:10.1109/TII.2018.2793210.
   Web of Science ®Google Scholar
• Vafaei, S., M. Gandomkar, A. Rezvani, and M. Izadbakhsh. 2015. Enhancement of grid-connected photovoltaic system using ANFIS-GA under different circumstances. Frontiers in Energy 9 (3):322–34. doi:10.1007/s11708-015-0362-x.
   Web of Science ®Google Scholar
• Wu, J., R. Nan, and L. Chen. 2019. Improved salp swarm algorithm based on weight factor and adaptive mutation. Journal of Experimental and Theoretical Artificial Intelligence 31 (3):493–515. doi:10.1080/0952813X.2019.1572659.
   Web of Science ®Google Scholar
• Yang, B., L. Zhong, X. Zhang, H. Shu, T. Yu, H. Li, L. Jiang, and L. Sun. 2019. Novel bio-inspired memetic salp swarm algorithm and application to MPPT for PV systems considering partial shading condition. Journal of Cleaner Production 215:1203–22. doi:10.1016/j.jclepro.2019.01.150.
   Web of Science ®Google Scholar