Abstract
In this study, a new hybrid method as gray wolf optimizer (GWO)-crow search algorithm (CSA) (hGWO-CSA) is proposed for solving the MPPT problem in PV energy system. In the proposed method, at first the GWO is applied for MPPT solution and then the optimal duty cycle determined by GWO is considered as the initial value to CSA method. In the hybrid method, the advantages of each method are combined that it is a method with high convergence accuracy and speed and is not trapped in local optimal and quickly achieves to global optimal. The proposed method performance is analyzed in MPPT solution under standard and partial shading condition (PSC), in solar and temperature variations and also considering various types of DC/DC converters. To verify the validity of the hGWO-CSA, the results are compared with GWO and CSA methods. The results show the superiority of the hGWO-CSA in achieving the GMPP with higher convergence speed and less transient oscillations in different condition and in comparison with GWO and CSA methods. Also, the results show that the PV system with the buck-boost converter has better performance due to the wider operation area in terms of extracted power and tracking efficiency than the other DC/DC converters.
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References
Subudhi Bidyadhar, Pradhan Raseswari (2013) A comparative study on maximum power point tracking techniques for photovoltaic power systems. IEEE Trans Sustain Energy 4(1):89–98
Verma D, Nema S, Shandilya AM, Dash SK (2016) Maximum power point tracking (MPPT) techniques: recapitulation in solar photovoltaic systems. Renew Sustain Energy Rev 54:1018–1034
Kheldoun A, Bradai R, Boukenoui R, Mellit A (2016) A new golden section method-based maximum power point tracking algorithm for photovoltaic systems. Energy Convers Manag 111:125–136
Liu L, Meng X, Liu C (2016) A review of maximum power point tracking methods of PV power system at uniform and partial shading. Renew Sustain Energy Rev 53:1500–1507
Elgendy MA, Zahawi B, Atkinson DJ (2012) Assessment of perturb and observe MPPT algorithm implementation techniques for PV pumping applications. IEEE Trans Sustain Energy 3(1):21–33
Alajmi BN, Ahmed KH, Finney SJ, Williams BW (2011) Fuzzy-logic-control approach of a modified hill-climbing method for maximum power point in microgrid standalone photovoltaic system. IEEE Trans Power Electron 26(4):1022–1030
Elgendy MA, Zahawi B, Atkinson DJ (2013) Assessment of the incremental conductance maximum power point tracking algorithm. IEEE Trans Sustain Energy 4(1):108–117
Casadei D, Grandi G, Rossi C (2006) Single-phase single-stage photovoltaic generation system based on a ripple correlation control maximum power point tracking. IEEE Trans Energy Convers 21(2):562–568
Husain MA, Tariq A, Hameed S, Arif MSB, Jain A (2017) Comparative assessment of maximum power point tracking procedures for photovoltaic systems. Green Energy Environ 2(1):5–17
Montecucco A, Knox AR (2015) Maximum power point tracking converter based on the open-circuit voltage method for thermoelectric generators. IEEE Trans Power Electron 30(2):828–839
Punitha K, Devaraj D, Sakthivel S (2013) Artificial neural network based modified incremental conductance algorithm for maximum power point tracking in photovoltaic system under partial shading conditions. Energy 62:330–340
Algazar MM, El-Halim HA, Salem MEEK (2012) Maximum power point tracking using fuzzy logic control. Int J Electr Power Energy Syst 39(1):21–28
Sundareswaran K, Palani S (2015) Application of a combined particle swarm optimization and perturb and observe method for MPPT in PV systems under partial shading conditions. Renew Energy 75:308–317
Daraban S, Petreus D, Morel C (2014) A novel MPPT (maximum power point tracking) algorithm based on a modified genetic algorithm specialized on tracking the global maximum power point in photovoltaic systems affected by partial shading. Energy 74:374–388
Mohanty S, Subudhi B, Ray PK (2016) A new MPPT design using grey wolf optimization technique for photovoltaic system under partial shading conditions. IEEE Trans Sustain Energy 7(1):181–188
Ishaque K, Salam Z, Amjad M, Mekhilef S (2012) An improved particle swarm optimization (PSO)–based MPPT for PV with reduced steady-state oscillation. IEEE Trans Power Electron 27(8):3627–3638
Ahmed J, Salam Z (2013, May) A soft computing MPPT for PV system based on Cuckoo Search algorithm. In: 2013 fourth international conference on power engineering, energy and electrical drives (POWERENG). IEEE, pp 558–562
Kheldoun A, Bradai R, Boukenoui R, Mellit A (2016) A new golden section method-based maximum power point tracking algorithm for photovoltaic systems. Energy Convers Manag 111:125–136
Harrag A, Messalti S (2015) Variable step size modified P&O MPPT algorithm using GA-based hybrid offline/online PID controller. Renew Sustain Energy Rev 49:1247–1260
Soufyane Benyoucef A, Chouder A, Kara K, Silvestre S (2015) Artificial bee colony based algorithm for maximum power point tracking (MPPT) for PV systems operating under partial shaded conditions. Appl Soft Comput 32:38–48
Lyden S, Haque ME (2016) A simulated annealing global maximum power point tracking approach for PV modules under partial shading conditions. IEEE Trans Power Electron 31(6):4171–4181
Kaced K, Larbes C, Ramzan N, Bounabi M, Elabadine Dahmane Z (2017) Bat algorithm based maximum power point tracking for photovoltaic system under partial shading conditions. Sol Energy 158:490–503
Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12
Papari B, Edrington CS, Vu TV, Diaz-Franco F (2017, June). A heuristic method for optimal energy management of DC microgrid. In: 2017 IEEE second international conference on DC microgrids (ICDCM). IEEE, pp 337–343
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Davoodkhani, F., Arabi Nowdeh, S., Abdelaziz, A.Y., Mansoori, S., Nasri, S., Alijani, M. (2020). A New Hybrid Method Based on Gray Wolf Optimizer-Crow Search Algorithm for Maximum Power Point Tracking of Photovoltaic Energy System. In: Eltamaly, A., Abdelaziz, A. (eds) Modern Maximum Power Point Tracking Techniques for Photovoltaic Energy Systems. Green Energy and Technology. Springer, Cham. https://doi.org/10.1007/978-3-030-05578-3_16
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DOI: https://doi.org/10.1007/978-3-030-05578-3_16
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