References
- Abdelhamid, L., M. Haddadi, and S. Messalti. 2016. Simulation and experimental design of a new advanced variable step size incremental conductance MPPT algorithm for PV systems. Isa Transactions 62 (1):30–38. doi:https://doi.org/10.1016/j.isatra.2015.08.006.
- Ahmed, J., and Z. Salam. 2015. An improved perturb and observe (P&O) maximum power point tracking (MPPT) algorithm for higher efficiency. Applied Energy 150:97–108. doi:https://doi.org/10.1016/j.apenergy.2015.04.006.
- Al-Majidi, S. D., M. F. Abbod, and H. S. Al-Raweshidy. 2018. A novel maximum power point tracking technique based on fuzzy logic for photovoltaic systems. International Journal of Hydrogen Energy 43 (31):14158–71. doi:https://doi.org/10.1016/j.ijhydene.2018.06.002.
- Benyoucef, A. S., A. Chouder, K. Kara, S. Silvestre, and O. Ait Sahed. 2015. Artificial bee colony based algorithm for maximum power point tracking (MPPT) for PV systems operating under partial shaded conditions. Applied Soft Computing 32:38–48. doi:https://doi.org/10.1016/j.asoc.2015.03.047.
- Bhatnagar, P., and R. K. Nema. 2013. Maximum power point tracking control techniques: State-of-the-art in photovoltaic applications. Renewable and Sustainable Energy Reviews 23:224–41. doi:https://doi.org/10.1016/j.rser.2013.02.011.
- Bouhali, O., Boumaaraf H., Talha, A., et al. 2015. A three-phase NPC grid-connected inverter for photovoltaic applications using neural network MPPT. Renewable & Sustainable Energy Reviews 49:1171–79. doi:https://doi.org/10.1016/j.rser.2015.04.066.
- Chen, M., S. Ma, J. Wu, and L. Huang. 2017. Analysis of MPPT failure and development of an augmented nonlinear controller for MPPT of photovoltaic systems under partial shading conditions. Applied Sciences 7 (1):95. doi:https://doi.org/10.3390/app7010095.
- Dadkhah, J., and M. Niroomand. 2021. Optimization methods of MPPT parameters for PV systems: Review, classification, and comparison. Journal of Modern Power Systems and Clean Energy 9 (2):225–36. doi:https://doi.org/10.35833/MPCE.2019.000379.
- Deshkar S, N., B. Dhale S, S. Mukherjee J, Babu, T, S., Rajaselar, N., et al. 2015. Solar PV array reconfiguration under partial shading conditions for maximum power extraction using genetic algorithm. Renewable & Sustainable Energy Reviews 43:102–10. doi:https://doi.org/10.1016/j.rser.2014.10.098.
- Duman, S., Yorukeren N., Altas, Ismail, H, et al. 2018. A novel MPPT algorithm based on optimized artificial neural network by using FPSOGSA for standalone photovoltaic energy systems. Neural Computing & Applications 29(1):257–78. doi:https://doi.org/10.1007/s00521-016-2447-9.
- 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 2011. 4 (1):108–17. doi:https://doi.org/10.1109/TSTE.2012.2202698.
- Elgendy, M. A., B. Zahawi, and D. J. Atkinson. 2015. Operating characteristics of the P&O algorithm at high perturbation frequencies for standalone PV Systems. IEEE Transactions on Energy Conversion 30 (1):189–98. doi:https://doi.org/10.1109/TEC.2014.2331391.
- Femia, N., G. Petrone, G. Spagnuolo, and M. Vitelli. 2005. Optimization of perturb and observe maximum power point tracking method. IEEE Transactions on Power Electronics 20 (4):963–73. doi:https://doi.org/10.1109/TPEL.2005.850975.
- Gupta, A., Y. K. Chauhan, and R. K. Pachauri. 2016. A comparative investigation of maximum power point tracking methods for solar PV system. Solar Energy 136:236–53. doi:https://doi.org/10.1016/j.solener.2016.07.001.
- He, Y., W. Xu, and Y. Cheng. 2010. A novel scheme for sliding-mode control of DC-DC converters with a constant frequency based on the averaging model. Journal of Power Electronics 10 (1):1. doi:https://doi.org/10.6113/JPE.2010.10.1.001.
- Kihal, A., F. Krim, A. Laib, Talbi, B., Afghoula, Hamza, et al. 2018. An improved MPPT scheme employing adaptive integral derivative sliding mode control for photovoltaic systems under fast irradiation changes. ISA Transactions 87:297–306. doi:https://doi.org/10.1016/j.isatra.2018.11.020.
- Kwan, T. H., and X. Wu. 2016. Maximum power point tracking using a variable antecedent fuzzy logic controller. Solar Energy 137:189–200. doi:https://doi.org/10.1016/j.solener.2016.08.008.
- Li, X., Q. Wang, H. Wen, W. Xiao. 2019. Comprehensive studies on operational principles for maximum power point tracking in photovoltaic systems. IEEE Access 7:121407–20. doi:https://doi.org/10.1109/ACCESS.2019.2937100.
- Liu, L., C. Liu, J. Wang, and Y. Kong. 2015. Simulation and hardware implementation of a hill-climbing modified fuzzy-logic for maximum power point tracking with direct control method using boost converter. Journal of Vibration and Control 21 (2):335–42. doi:https://doi.org/10.1177/1077546313486912.
- Ma, S., M. Chen, J. Wu, W. Huo, and L. Huang. 2016. Augmented nonlinear controller for maximum power-point tracking with artificial neural network in grid-connected photovoltaic systems. Energies 9 (12):1005. doi:https://doi.org/10.3390/en9121005.
- Mamarelis, E., G. Petrone, and G. Spagnuolo. 2013. Design of a sliding-mode-controlled SEPIC for PV MPPT applications. IEEE Transactions on Industrial Electronics 61 (7):3387–98. doi:https://doi.org/10.1109/TIE.2013.2279361.
- Mao, M.-X., L. Zhang, Q.-C. Duan, and B. Chong. 2017. Multilevel DC-link converter photovoltaic system with modified PSO based on maximum power point tracking. Solar Energy 153:329–42. doi:https://doi.org/10.1016/j.solener.2017.05.017.
- Montoya, D. G., C. A. Ramos-Paja, and R. Giral. 2015. Improved design of sliding mode controllers based on the requirements of MPPT techniques. IEEE Transactions on Power Electronics 31 (1):235–47. doi:https://doi.org/10.1109/TPEL.2015.2397831.
- Qiai, X. 2020. Improved sliding mode control method for photovoltaic maximum power tracking. ACTA ENERGIAE SOLARIS SINICA 41 (10):381–88.
- Renaudineau, H., F. Donatantonio, J. Fontchastagner, G. Petrone, G. Spagnuolo, J.-P. Martin, and S. Pierfederici. 2015. A PSO-based global MPPT technique for distributed PV power generation. IEEE Transactions on Industrial Electronics 62 (2):1047–58. doi:https://doi.org/10.1109/TIE.2014.2336600.
- Rezvani, A., M. Gandomkar, M. Izadbakhsh, and A. Ahmadi. 2015. Environmental/economic scheduling of a micro-grid with renewable energy resources. Journal of Cleaner Production 87:216–26. doi:https://doi.org/10.1016/j.jclepro.2014.09.088.
- Safari, A., and S. Mekhilef. 2011. Simulation and hardware implementation of incremental conductance MPPT with direct control method using cuk converter. IEEE Transactions on Industrial Electronics 58 (4):1154–61. doi:https://doi.org/10.1109/TIE.2010.2048834.
- Spagnuolo, G., G. Petrone, S. V. Araujo, C. Cecati, E. Friis-Madsen, E. Gubia, D. Hissel, M. Jasinski, W. Knapp, M. Liserre, et al. 2010. Renewable energy operation and conversion schemes. IEEE Industrial Electronics Magazine 4:38–51.
- Sundareswaran, K., V. Vigneshkumar, P. Sankar, Simon, S., P., Srinivasa, Rao, Nayak, P., et al. 2016. Development of an improved P&O algorithm assisted through a colony of foraging ants for MPPT in PV system. IEEE Transactions on Industrial Informatics. 12(1):187–200. doi:https://doi.org/10.1109/TII.2015.2502428.
- Villalva, M. G., J. R. Gazoli, and E. R. Filho. 2009. Comprehensive approach to modeling and simulation of photovoltaic arrays. IEEE Transactions on Power Electronics 24 (5):1198–208. doi:https://doi.org/10.1109/TPEL.2009.2013862.