Abstract
The clean and abundant nature of photovoltaic technology makes it eminent among other renewable energy sources and to obtain the best benefit from these sources, an efficient maximum power point tracking technique is needed that can produce the required output even under varying environmental conditions. This work deals with the development of a global maximum power point tracking technique combining bio-inspired algorithms and one-cycle control which helps in effective tracking even under partial shading conditions. This technique generates signals for the KY converter from the duty cycle obtained from bio-inspired algorithms. The voltage at the output of the photovoltaic panel is fed to the load through KY converter. The analysis of the system is carried out using resistive load under different patterns of the photovoltaic array using particle swarm optimization, flower pollination and flying squirrel search optimization algorithms through simulation and experimentation. The performance indices like tracking speed, tracking efficiency and steady-state oscillations are taken for comparison with the existing systems without one-cycle control, and the results indicate its capability in GMPP tracking with an average efficiency and tracking time of 99.1% and 0.13 s, respectively.
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Kermadi, M.; Chin, V.J.; Mekhilef, S.; Salam, Z.: A fast and accurate generalized analytical approach for PV arrays modeling under partial shading conditions. Sol. Energy 208, 753–765 (2020)
Elgendy, M.A.; Zahawi, B.; Atkinson, D.J.: Assessment of perturb and observe MPPT algorithm implementation techniques for PV pumping applications. IEEE Trans. Sustain. Energy 3(1), 21–33 (2011)
Elgendy, M.A.; Atkinson, D.J.; Zahawi, B.: Experimental investigation of the incremental conductance maximum power point tracking algorithm at high perturbation rates. IET Renew. Power Gener. 10(2), 133–139 (2016)
Kjær, S.B.: Evaluation of the “hill climbing’’ and the “incremental conductance’’ maximum power point trackers for photovoltaic power systems. IEEE Trans. Energy Convers. 27(4), 922–929 (2012)
Ilyas, A.; Khan, M.R.; Ayyub, M.: FPGA based real-time implementation of fuzzy logic controller for maximum power point tracking of solar photovoltaic system. Optik 213, 164668 (2020)
Rizzo, S.A.; Scelba, G.: Ann based MPPT method for rapidly variable shading conditions. Appl. Energy 145, 124–132 (2015)
Karatepe, E.; Hiyama, T.: Artificial neural network-polar coordinated fuzzy controller based maximum power point tracking control under partially shaded conditions. IET Renew. Power Gener. 3(2), 239–253 (2009)
Liu, Y.-H.; Huang, S.-C.; Huang, J.-W.; Liang, W.-C.: A particle swarm optimization-based maximum power point tracking algorithm for PV systems operating under partially shaded conditions. IEEE Trans. Energy Convers. 27(4), 1027–1035 (2012)
Alshareef, M.; Lin, Z.; Ma, M.; Cao, W.: Accelerated particle swarm optimization for photovoltaic maximum power point tracking under partial shading conditions. Energies 12(4), 623 (2019)
Joisher, M.; Singh, D.; Taheri, S.; Espinoza-Trejo, D.R.; Pouresmaeil, E.; Taheri, H.: A hybrid evolutionary-based MPPT for photovoltaic systems under partial shading conditions. IEEE Access 8, 38481–38492 (2020)
Cintury, N.; Saha, S.; Roy, C.: Tracking of maximum power of solar PV array under partial shading condition using grey wolf optimization algorithm. In: Advances in Communication, Devices and Networking: Proceedings of ICCDN 2021, pp. 161–171. Springer (2022)
Janandra-Krishna-Kishore, D.; Mohamed, M.R.; Sudhakar, K.; Peddakapu, K.: Grey wolf optimization and differential evolution-based maximum power point tracking controller for photovoltaic systems under partial shading conditions. Energy Sources Part A Recovery Utilization Environ. Eff. 44(3), 6286–6302 (2022)
Sundareswaran, K.; Sankar, P.; Nayak, P.S.R.; Simon, S.P.; Palani, S.: Enhanced energy output from a PV system under partial shaded conditions through artificial bee colony. IEEE Trans. Sustain. Energy 6(1), 198–209 (2014)
Ahmed, J.; Salam, Z.: A maximum power point tracking (MPPT) for PV system using cuckoo search with partial shading capability. Appl. Energy 119, 118–130 (2014)
Kumar, N.; Hussain, I.; Singh, B.; Panigrahi, B.K.: Maximum power peak detection of partially shaded pv panel by using intelligent monkey king evolution algorithm. IEEE Trans. Ind. Appl. 53(6), 5734–5743 (2017)
Sarwar, S.; Hafeez, M.A.; Javed, M.Y.; Asghar, A.B.; Ejsmont, K.: A horse herd optimization algorithm (HOA)-based MPPT technique under partial and complex partial shading conditions. Energies 15(5), 1880 (2022)
Mohebbi, P.;, Aazami, R.; Moradkhani, A.; Danyali, S.: A novel intelligent hybrid algorithm for maximum power point tracking in PV system. Int. J. Electron. (2023)
Davoodkhani, F., Arabi Nowdeh, S., Abdelaziz, A.Y., Mansoori, S., Nasri, S., Alijani, M.: A new hybrid method based on gray wolf optimizer-crow search algorithm for maximum power point tracking of photovoltaic energy system. In: Modern Maximum Power Point Tracking Techniques for Photovoltaic Energy Systems, pp. 421–438 (2020)
Eltamaly, A.M.; Al-Saud, M.; Abokhalil, A.G.: A novel scanning bat algorithm strategy for maximum power point tracker of partially shaded photovoltaic energy systems. Ain Shams Eng. J. 11(4), 1093–1103 (2020)
Dadkhah, J.; Niroomand, M.: Optimization methods of MPPT parameters for PV systems: review, classification, and comparison. J. Mod. Power Syst. Clean Energy 9(2), 225–236 (2021)
Nkambule, M.S.; Hasan, A.N.; Ali, A.; Hong, J.; Geem, Z.W.: Comprehensive evaluation of machine learning MPPT algorithms for a PV system under different weather conditions. J. Electric. Eng. Technol. 16, 411–427 (2021)
Mao, M.; Cui, L.; Zhang, Q.; Guo, K.; Zhou, L.; Huang, H.: Classification and summarization of solar photovoltaic MPPT techniques: a review based on traditional and intelligent control strategies. Energy Rep. 6, 1312–1327 (2020)
Smedley, K.M.; Cuk, S.: One-cycle control of switching converters. IEEE Trans. Power Electron. 10(6), 625–633 (1995)
de Carvalho Neto, J.T.; Salazar, A.O.; da Costa, A.H.; Leite, A.C.: One-cycle control based maximum power point tracker using constant voltage method for battery charging applications. In: 2017 Brazilian Power Electronics Conference (COBEP), pp. 1–7. IEEE (2017)
Kumar, T.G.S.: Comparison of PWM and one-cycle control for switching converters (2013)
Fortunato, M.; Giustiniani, A.; Petrone, G.; Spagnuolo, G.; Vitelli, M.: Maximum power point tracking in a one-cycle-controlled single-stage photovoltaic inverter. IEEE Trans. Industr. Electron. 55(7), 2684–2693 (2008)
de Carvalho Neto, J.T.; Salazar, A.O.; Lock, A.S.; Fonseca, D.: One cycle control for battery connected standalone photovoltaic systems for DC loads. IEEE Lat. Am. Trans. 16(7), 1977–1983 (2018)
Lock, A.S.; da Silva, E.R.C.; Elbuluk, M.E.; Fernandes, D.A.: An APF-OCC strategy for common-mode current rejection. IEEE Trans. Ind. Appl. 52(6), 4935–4945 (2016)
Luo, F.L.: Positive output LUO converters: voltage lift technique. IEE Proc. Electric Power Appl. 146(4), 415–432 (1999)
Zhu, M.; Luo, F.L.: Implementing of developed voltage lift technique on SEPIC, CUK and double-output DC–DC converters. In: 2007 2nd IEEE Conference on Industrial Electronics and Applications, pp. 674–681. IEEE (2007)
Sugavanam, K.; Kumar, R.S.; Kumar, S.S.K.; Karthikumar, S.; Tamilmullai, V.: Design of FLC for OVR reduction of negative output KY converter. Int. J. Appl. Eng. Res. (IJAER) 9(24), 23689–23699 (2014)
Ridley, R.B.: Secondary LC filter analysis and design techniques. IEEE Trans. Power Electron 3 (1988)
Hwu, K.; Yau, Y.: KY converter and its derivatives. IEEE Trans. Power Electron. 24(1), 128–137 (2009)
Ahmad, R.; Murtaza, A.F.; Sher, H.A.: Power tracking techniques for efficient operation of photovoltaic array in solar applications—a review. Renew. Sustain. Energy Rev. 101, 82–102 (2019)
Singh, N.; Gupta, K.K.; Jain, S.K.; Dewangan, N.K.; Bhatnagar, P.: A flying squirrel search optimization for MPPT under partial shaded photovoltaic system. IEEE J. Emerg. Sel. Top. Power Electron. 9(4), 4963–4978 (2020)
Eltamaly, A.M.: An improved cuckoo search algorithm for maximum power point tracking of photovoltaic systems under partial shading conditions. Energies 14(4), 953 (2021)
Kishore, D.K.; Mohamed, M.; Sudhakar, K.; Peddakapu, K.: An improved grey wolf optimization based MPPT algorithm for photovoltaic systems under diverse partial shading conditions. J. Phys. Conf. Ser. 2312, 012063 (2022)
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R designed, carried out research work and drafted the manuscript. Dr. K. Gnana Sheela coordinated, carried out research work, and read, corrected and approved the final draft of the manuscript.
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Nisha, R., Sheela, K.G. Global Maximum Power Point Tracking of Photovoltaic Systems Using Bio-inspired Algorithm-Based MPPT and One-Cycle Control. Arab J Sci Eng 49, 6799–6814 (2024). https://doi.org/10.1007/s13369-023-08493-2
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DOI: https://doi.org/10.1007/s13369-023-08493-2