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Development of rapid and reliable cuckoo search algorithm for global maximum power point tracking of solar PV systems in partial shading condition

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The solar photovoltaic output power fluctuates according to solar irradiation, temperature, and load impedance variations. Due to the operating point fluctuations, extracting maximum power from the PV generator, already having a low power conversion ratio, becomes very complicated. To reach a maximum power operating point, a maximum power point tracking technique (MPPT) should be used. Under partial shading condition, the nonlinear PV output power curve contains multiple maximum power points with only one global maximum power point (GMPP). Consequently, identifying this global maximum power point is a difficult task and one of the biggest challenges of partially shaded PV systems. The conventional MPPT techniques can easily be trapped in a local maximum instead of detecting the global one. The artificial neural network techniques used to track the GMPP have a major drawback of using huge amount of data covering all operating points of PV system, including different uniform and non-uniform irradiance cases, different temperatures and load impedances. The biological intelligence techniques used to track GMPP, such as grey wolf algorithm and cuckoo search algorithm (CSA), have two main drawbacks; to be trapped in a local MPP if they have not been well tuned and the precision-transient tracking time complex paradox. To deal with these drawbacks, a Distributive Cuckoo Search Algorithm (DCSA) is developed, in this paper, as GMPP tracking technique. Simulation results of the system for different partial shading patterns demonstrated the high precision and rapidity, besides the good reliability of the proposed DCSA-GMPPT technique, compared to the conventional CSA-GMPPT.
Rocznik
Strony
495--526
Opis fizyczny
Bibliogr. 44 poz., rys., tab., wzory
Twórcy
  • Laboratory Materials and Sustainable Development (LMDD), Electrical Engineering Department, Faculty of Science and Applied Sciences, University of Bouira, Algeria
  • Electrical Engineering Department, Faculty of Science and Applied Sciences, University of Bouira, Algeria
  • LTII Laboratory, University of Bejaia, Algeria
  • Electrical Engineering Department, University of M’sila, Algeria
  • SGRE Laboratory, University of Béchar, Algeria
Bibliografia
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  • [3] Liqun Liu, Xiaoli Meng, and Chunxia Liu: A review of maximum power point tracking methods of PV power system at uniform and partial shading. Renewable and Sustainable Energy Reviews, 53 (2016), 1500-1507, DOI: 10.1016/j.rser.2015.09.065.
  • [4] Yanzhi Wang, Xue Lin, Younghyun Kim, Naehyuck Chang, and Massoud Pedram: Enhancing efficiency and robustness of a photovoltaic power system under partial shading. Thirteenth International Symposium on Quality Electronic Design (ISQED), Santa Clara USA, (2012), 592-600, DOI: 10.1109/ISQED.2012.6187554.
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  • [6] Kashif Ishaque and Zainal Salam: A review of maximum power point tracking techniques of PV system for uniform insolation and partial shading condition. Renewable and Sustainable Energy Reviews, 19 (2013), 475-488, DOI: 10.1016/j.rser.2012.11.032.
  • [7] Jubaer Ahmed and Zainal Salam: A critical evaluation on maximum power point tracking methods for partial shading in PV systems. Renewable and Sustainable Energy Reviews, 47 (2015), 933-953, DOI: 10.1016/j.rser.2015.03.080.
  • [8] Ali M. Eltamaly: Performance of MPPT techniques of photovoltaic systems under normal and partial shading conditions. Advances in Renewable Energies and Power Technologies, vol. 1, Solar and Wind Energies, I. Yahyaoui, 2018, Elsevier, Chapter 4, 115-161.
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  • [14] Hassan M.H. Farh, Mohamed F. Othman, and Ali M. Eltamaly: Maximum power extraction from grid-connected PV system. Saudi Arabia Smart Grid (SASG), (2017), 1-6, DOI: 10.1109/SASG.2017.8356498.
  • [15] Seyedali Mirjalili, Seyed Mohammad Mirjalili, and Andrew Lewis: Grey Wolf optimizer. Advances in Engineering Software, 69 (2014), 46-61, DOI: 10.1016/j.advengsoft.2013.12.007.
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  • [18] Lian Lian Jiang, R. Srivatsan, and Douglas L. Maskell: Computational intelligence techniques for maximum power point tracking in PV systems: A review. Renewable and Sustainable Energy Reviews, 85 (2018), 14-45, DOI: 10.1016/j.rser.2018.01.006.
  • [19] Ali M. Eltamaly and Hassan M.H. Farh: Dynamic global maximum power point tracking of the PV systems under variant partial shading using hybrid GWO-FLC. Solar Energy, 177 (2019), 306-316, DOI: 10.1016/j.solener.2018.11.028.
  • [20] Jubaer Ahmed and Zainal Salam: A maximum power point tracking (MPPT) for PV system using cuckoo search with partial shading capability. Applied Energy, 119 (2014), 118-130, DOI: 10.1016/j.apenergy.2013.12.062.
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  • [22] Jubaer Ahmed and Zainal Salam: A soft computing MPPT for PV system based on cuckoo search algorithm. 4th International Conference on Power Engineering, Energy and Electrical Drives, Istanbul, Turkey, (2013), 558-562, DOI: 10.1109/PowerEng.2013.6635669.
  • [23] Ahmed A. El Baset, A. El Halim, Naggar H. , and Ahmed A. El Sattar: A comparative study between perturb and observe and cuckoo search algorithm for maximum power point tracking. 21st International Middle East Power Systems Conference (MEPCON), Cairo, Egypt, (2019), 716-723, DOI: 10.1109/MEPCON47431.2019.9008210.
  • [24] Filippo Spertino and Jean Sumaili Akilimali: Are manufacturing I-V mismatch and reverse currents key factors in large photovoltaic arrays? IEEE Transactions on Industrial Electronics, 56(11), (2009), 4520-4531, DOI: 10.1109/TIE.2009.2025712.
  • [25] M. Drif, P.J. Perez, J. Aguilera, and J.D. Aguilar: A new estimation method of irradiance on a partially shaded PV generator in grid-connected photovoltaic systems. Renewable Energy, 33(9), (2008), 2048-2056, DOI: 10.1016/j.renene.2007.12.010.
  • [26] Bidyadhar Subudhi and Raseswari Pradhan: A comparative study on maximum power point tracking techniques for photovoltaic power systems. IEEE Transactions on Sustainable Energy, 4(1), (2012), 89-98, DOI: 10.1109/TSTE.2012.2202294.
  • [27] Kashif Ishaque and Zainal Salam:AcomprehensiveMATLAB Simulink PV system simulator with partial shading capability based on two-diode model. Solar Energy, 85(9), (2011), 2217-2227, DOI: 10.1016/j.solener.2011.06.008.
  • [28] Mohamed I. Mosaad, M. Osama Abed el-Raouf, Mahmoud A. Al-Ahmar, and Fahd A. Banakher: Maximum power point tracking of PV system based cuckoo search algorithm; review and comparison. Energy Procedia, 162 (2019), 117-126, DOI: 10.1016/j.egypro.2019.04.013.
  • [29] Bo Yang, Jingbo Wang, Xiaoshun Zhang, Tao Yu, Wei Yao, Hongchun Shu, Fang Zeng, and Liming Sun: Comprehensive overview of metaheuristic algorithm applications on PV cell parameter identification. Energy Conversion and Management, 208 (2020), 112595, DOI: 10.1016/j.enconman.2020.112595.
  • [30] Tong Kang, Jiangang Yao, Min Jin, Shengjie Yang, and Thanh Long Duong: A novel improved cuckoo search algorithm for parameter estimation of photovoltaic (PV) models. Energies, 11(5), (2018), 1060, DOI: 10.3390/en11051060.
  • [31] S. Walton, O. Hassan, K. Morgan, and M.R. Brown: Modified cuckoo search: a new gradient free optimisation algorithm. Chaos, Solitons & Fractals, 44(9), (2011), 710718, DOI: 10.1016/j.chaos.2011.06.004.
  • [32] Amir Hossein Gandomi, Xin-She Yang, and Amir Hossein Alavi: Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Engineering with Computers, 29(1), (2013), 17-35, DOI: 10.1007/s00366-011-0241-y.
  • [33] Abdesslem Layeb: A novel quantum inspired cuckoo search for knapsack problems. International Journal of Bio-Inspired Computation, 3(5), (2011), 297-305, DOI: 10.1504/IJBIC.2011.042260.
  • [34] Ehsan Valian, Saeed Tavakoli, Shahram Mohanna, and Atiyeh Haghi: Improved cuckoo search for reliability optimization problems. Computers & Industrial Engineering, 64(1), (2013), 459-468, DOI: 10.1016/j.cie.2012.07.011.
  • [35] Xiangtao Li, Jianan Wang, and Minghao Yin: Enhancing the performance of cuckoo search algorithm using orthogonal learning method. Neural Computing and Applications, 24(6), (2014), 1233-1247, DOI: 10.1007/s00521-013-1354-6.
  • [36] Hui Wang, Wenjun Wang, Hui Sun, Zhihua Cui, Shahryar Rahnamayan, and Sanyou Zeng: A new cuckoo search algorithm with hybrid strategies for flow shop scheduling problems. Soft Computing, 21(15), (2017), 4297-4307, DOI: 10.1007/s00500-016-2062-9.
  • [37] Wang Jianzhou, He Jiang, Yujie Wu, and Yao Dong: Forecasting solar radiation using an optimized hybrid model by cuckoo search algorithm. Energy, 81 (2015), 627-644, DOI: 10.1016/j.energy.2015.01.006.
  • [38] Wen Long, Shaohong Cai, Jianjun Jiao, Ming Xu, and Tiebin Wu: A new hybrid algorithm based on grey wolf optimizer and cuckoo search for parameter extraction of solar photovoltaic models. Energy Conversion and Management, 203 (2020), 112243, DOI: 10.1016/j.enconman.2019.112243.
  • [39] Diego Oliva, Ahmed A. Ewees, Mohamed Abd El Aziz, Aboul Ella Hassanien, and Marco Perez-Cisneros: A chaotic improved artificial bee colony for parameter estimation of photovoltaic cells. Energies, 10(7), (2017), 865, DOI: 10.3390/en10070865.
  • [40] Xiaofang Yuan, Yuqing He, and Liangjiang Liu: Parameter extraction of solar cell models using chaotic asexual reproduction optimization. Neural Computing and Applications, 26(5), (2015), 1227-1239, DOI: 10.1007/s00521-014-1795-6.
  • [41] Xiaofang Yuan, Yongzhong Xiang, and Yuqing He: Parameter extraction of solar cell models using mutative-scale parallel chaos optimization algorithm. Solar Energy, 108 (2014), 238-251, DOI: 10.1016/j.solener.2014.07.013.
  • [42] Alireza Askarzadeh and Alireza Rezazadeh: Artificial bee swarm optimization algorithm for parameters identification of solar cell models. Applied Energy, 102 (2013), 943-949, DOI: 10.1016/j.apenergy.2012.09.052.
  • [43] Santhan Kumar Cherukuri and Srinivasa Rao Rayapudi: Enhanced grey wolf optimizer based MPPT algorithm of PV system under partial shaded condition. International Journal of Renewable Energy Development, 6(3), (2017), 203-212, DOI: 10.14710/ijred.6.3.203-212.
  • [44] Adeel Feroz Mirza, Qiang Ling, M. Yaqoob Javed, and Majad Mansoor: Novel MPPT techniques for photovoltaic systems under uniform irradiance and Partial shading. Solar Energy, 184 (2019), 628-648, DOI: 10.1016/j.solener.2019.04.034.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-32e60f96-4075-4d68-ae7b-df21378de05e
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