بهینه سازی پارامترهای مدل تک دیودی سلول فتوولتائیک با استفاده از الگوریتم کلونی مورچگان جهت استفاده در بویه های شناور

نوع مقاله : مقاله پژوهشی

نویسندگان

گروه مهندسی برق، دانشکده مهندسی و فناوری، دانشگاه مازندران، بابلسر، ایران

چکیده

یکی از کاربردهای سلول های خورشیدی تامین برق مورد نیاز در بویه های شناور است. بویه های شناور، گویچه هایی هستند که در سطح آب دریاها و اقیانوس ها قرار می گیرند و اطلاعات مختلفی را به شناورها می دهند. این گویچه ها شرایط زیست محیطی متفاوتی را تجربه می کنند، به همین دلیل مدل سازی و شبیه سازی سلول های فتوولتائیک موجب می شود تا بتوانیم سلولی با بازدهی و عملکرد بهتری را در آن ها تعبیه کنیم. در این مقاله به بررسی پارامترهای مدل تک دیودی می پردازیم به طوری که نمودارهای I-V ، P-V و مشخصه های سلول فتوولتائیک کادمیوم تلوراید (CdTe) را که با سه لایه (CdTe ،CdS ، SnOx) طراحی شده است از طریق نرم افزار SCAPS استخراج می کنیم و با استفاده از الگوریتم بهینه سازی کلونی مورچگان (ACO) پارامترهای مدل تک دیودی آن را بدست می آوریم. تابع هدف در این مقاله، RMSE (جذر میانگین مربعات خطاها ) است که بهترین مقدار به دست آمده آن پس از 30 اجرا 5-10×5.2217 در 2.46 ثانیه به ازای هر دور تکرار می باشد که نشان دهنده تطابق بسیار بالای مدل شبیه سازی شده با واقعیت است و برتری قابل توجهی را نسبت به بسیاری از الگوریتم هایی که تاکنون انجام شده نشان می دهد. بهینه سازی فوق با 200 دور تکرار، 30 جمعیت و 84 نقطه بر بستر یک سرور با 32 گیگ رم و 30 هسته پردازشی انجام شده است.

کلیدواژه‌ها


عنوان مقاله [English]

Optimization of Single Diode Model Parameters of Photovoltaic Solar Cell Using Ant Colony Algorithm for Use in Marine Floating Buoys

نویسندگان [English]

  • Vahdat Nazerian
  • Hossein Asadollahi
Department of Electrical Engineering. Faculty of Engineering & Technology. University of Mazandaran. Babolsar, Iran
چکیده [English]

One of the applications of solar cells is to supply power to floating buoys. Floating buoys are spherules that are located on the surface of seas and oceans and give different information to vessels. These spherules experience different environmental conditions, so modeling and simulating photovoltaic cells allows us to embed a cell with better efficiency and performance in them. In this paper, we study the parameters of the single diode model so that we extract the I-V, P-V diagrams and characteristics of cadmium telluride (CdTe) photovoltaic cell designed with three layers (CdTe, CdS, SnOx) through SCAPS software and using the Ant Colony Optimization (ACO) algorithm, we obtain the parameters of its single diode model. The objective function in this paper is Root Mean Square of Errors (RMSE). The best value obtained after 30 runs is 5.2217 -10 5-10 in 2.46 seconds per repetition, which indicates a very high fit of the simulated model and shows a significant advantage over many algorithms that have been done so far. The above optimization has been done with 200 iterations, 30 populations and 84 points on a server platform with 32 GB of RAM and 30 processing cores.

کلیدواژه‌ها [English]

  • Ant Colony Optimization (ACO) Algorithm
  • Single Diode Model
  • Root Mean Square of Errors (RMSE)
  • Photovoltaic Solar Cell
  • Floating Buoys
  • SCAPS Software
[1] Guo S, Zheng Y, Gan L, editors. The Design and Application of Intelligent Buoys in Polar Water2018 2018/05: Atlantis Press.
[2] Rozali R, Mohd Yusop MY, Mohd Dahalan W, Yahaya A, Mohamid Salih N, Yaakop S. Floating Buoy Technology for Reseach Purposes. International Journal of Innovative Technology and Exploring Engineering. 2019;8:5514.
[3] Smyth TJ, Fishwick JR, Gallienne CP, Stephens JA, Bale AJ. Technology, Design, and Operation of an Autonomous Buoy System in the Western English Channel. Journal of Atmospheric and Oceanic Technology. 2010;27(12):2056-64.
[4] Falleni S, Unal D, Neerman A, Enhos K, Demirors E, Basagni S, et al., editors. Design, Development, and Testing of a Smart Buoy for Underwater Testbeds in Shallow Waters. Global Oceans 2020: Singapore – US Gulf Coast; 2020 5-30 Oct. 2020.
[5] Kannan N, Vakeesan D. Solar energy for future world: - A review. Renewable and Sustainable Energy Reviews. 2016;62:1092-105.
[6] Chen J, Li Y, Zhang X, Ma Y. Simulation and Design of Solar Power System for Ocean Buoy. Journal of Physics: Conference Series. 2018;1061:012018.
[7] Nayak PK, Mahesh S, Snaith HJ, Cahen D. Photovoltaic solar cell technologies: analysing the state of the art. Nature Reviews Materials. 2019;4(4):269-85.
[8] El Chaar L, lamont LA, El Zein N. Review of photovoltaic technologies. Renewable and Sustainable Energy Reviews. 2011;15(5):2165-75.
[9] Bahrami A, Mohammadnejad S, Soleimaninezhad S. Photovoltaic cells technology: principles and recent developments. Optical and Quantum Electronics. 2013;45(2):161-97.
[10] Khursheed Mu-N, Khan MFN, Ali G, Khan AK, editors. A Review of Estimating Solar Photovoltaic Cell Parameters. 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET); 2019 30-31 Jan. 2019.
[11]Diab AAZ, Sultan HM, Do TD, Kamel OM, Mossa MA. Coyote Optimization Algorithm for Parameters Estimation of Various Models of Solar Cells and PV Modules. IEEE Access. 2020;8:111102-40.
[12] Dittrich T. Basic Characteristics and Characterization of Solar Cells. Materials Concepts for Solar Cells: WORLD SCIENTIFIC (EUROPE); 2017. p. 3-43.
[13] Jordehi AR. Parameter estimation of solar photovoltaic (PV) cells: A review. Renewable and Sustainable Energy Reviews. 2016;61:354-71.
[14] Lin X, Wu Y. Parameters identification of photovoltaic models using niche-based particle swarm optimization in parallel computing architecture. Energy. 2020;196:117054.
[15] Chen X, Xu B, Mei C, Ding Y, Li K. Teaching–learning–based artificial bee colony for solar photovoltaic parameter estimation. Applied Energy. 2018;212:1578-88.
[16] AlHajri MF, El-Naggar KM, AlRashidi MR, Al-Othman AK. Optimal extraction of solar cell parameters using pattern search. Renewable Energy. 2012;44:238-45.
[17] Dorigo M, Birattari M, Stutzle T. Ant colony optimization. IEEE Computational Intelligence Magazine. 2006;1(4):28-39.
[18] Dorigo M, Blum C. Ant colony optimization theory: A survey. Theoretical Computer Science. 2005;344(2):243-78.
[19] Blum C. Ant colony optimization: Introduction and recent trends. Physics of Life Reviews. 2005;2(4):353-73.
[20] Dorigo M, Stützle T. Ant Colony Optimization: The MIT Press; 2004. Available from: https://doi.org/10.7551/mitpress/1290.001.0001.
[21] Wilson D, editor The Chesapeake Bay Interpretive Buoy System: Recent expansion and advances. OCEANS 2009; 2009 26-29 Oct. 2009.
 [22] Li L, Xiong G, Yuan X, Zhang J, Chen J. Parameter Extraction of Photovoltaic Models Using a Dynamic Self-Adaptive and Mutual- Comparison Teaching-Learning-Based Optimization. IEEE Access. 2021;9:52425-41.
[23] Ma J, Man KL, Ting TO, Zhang N, Guan S-U, Wong PWH. Approximate Single-Diode Photovoltaic Model for Efficient I- V Characteristics Estimation. The Scientific World Journal. 2013;2013:230471.
[24] Lekouaghet B, Boukabou A, Boubakir C. Estimation of the photovoltaic cells/modules parameters using an improved Rao-based chaotic optimization technique. Energy Conversion and Management. 2021;229:113722.
[25] Chin VJ, Salam Z, Ishaque K. Cell modelling and model parameters estimation techniques for photovoltaic simulator application: A review. Applied Energy. 2015;154:500-19.
[26] Abd Elaziz M, Oliva D. Parameter estimation of solar cells diode models by an improved opposition-based whale optimization algorithm. Energy Conversion and Management. 2018;171:1843-59.
[27] Hamid NFA, Rahim NA, Selvaraj J, editors. Solar cell parameters extraction using particle swarm optimization algorithm. 2013 IEEE Conference on Clean Energy and Technology (CEAT); 2013 18-20 Nov. 2013.
[28] Yousri D, Thanikanti SB, Allam D, Ramachandaramurthy VK, Eteiba MB. Fractional chaotic ensemble particle swarm optimizer for identifying the single, double, and three diode photovoltaic models’ parameters. Energy. 2020;195:116979.
[29] Diab AAZ, Sultan HM, Aljendy R, Al-Sumaiti AS, Shoyama M, Ali ZM. Tree Growth Based Optimization Algorithm for Parameter Extraction of Different Models of Photovoltaic Cells and Modules. IEEE Access. 2020;8:119668-87.
[30] Bai J, Liu S, Hao Y, Zhang Z, Jiang M, Zhang Y. Development of a new compound method to extract the five parameters of PV modules. Energy Conversion and Management. 2014;79:294–303.
[31] Mughal M, Ma Q, Xiao C. Photovoltaic Cell Parameter Estimation Using Hybrid Particle Swarm Optimization and Simulated Annealing. Energies. 2017;10:1213.
[32] Nazerian V, Babaei S, editors. Optimization of Exponential Double-Diode Model for Photovoltaic Solar Cells Using GA-PSO Algorithm. Fundamental Research in Electrical Engineering; 2019 2019//; Singapore: Springer Singapore.
[33] Dorigo M, Bonabeau E, Theraulaz G. Ant algorithms and stigmergy. Future Generation Computer Systems. 2000;16(8):851-71.
[34] Mirjalili S. Ant Colony Optimisation. In: Mirjalili S, editor. Evolutionary Algorithms and Neural Networks: Theory and Applications. Cham: Springer International Publishing; 2019. p. 33-42.
[35] Dorigo M, Caro GD, editors. Ant colony optimization: a new meta-heuristic. Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat No 99TH8406); 1999 6-9 July 1999.
[36] Dorigo M, Stützle T. The Ant Colony Optimization Metaheuristic: Algorithms, Applications, and Advances. In: Glover F, Kochenberger GA, editors. Handbook of Metaheuristics. Boston, MA: Springer US; 2003. p. 250-85.
[37] Maniezzo V, Carbonaro A. Ant Colony Optimization: An Overview. In: Ribeiro CC, Hansen P, editors. Essays and Surveys in Metaheuristics. Boston, MA: Springer US; 2002. p. 469-92.
[38] Romeo A, Artegiani E. CdTe-Based Thin Film Solar Cells: Past, Present and Future. Energies. 2021;14.
[39] Oliva D, Cuevas E, Pajares G. Parameter identification of solar cells using artificial bee colony optimization. Energy. 2014;72:93-102.
[40] Yu K, Qu B, Yue C, Ge S, Chen X, Liang J. A performance-guided JAYA algorithm for parameters identification of photovoltaic cell and module. Applied Energy. 2019;237:241-57.
[41] Oliva D, Abd El Aziz M, Ella Hassanien A. Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm. Applied Energy. 2017;200:141-54.
[42] Xu S, Wang Y. Parameter estimation of photovoltaic modules using a hybrid flower pollination algorithm. Energy Conversion and Management. 2017;144:53-68.
[43] Yu K, Chen X, Wang X, Wang Z. Parameters identification of photovoltaic models using self-adaptive teaching-learning-based optimization. Energy Conversion and Management. 2017;145:233-46.
[44] Rezaee Jordehi A. Enhanced leader particle swarm optimisation (ELPSO): An efficient algorithm for parameter estimation of photovoltaic (PV) cells and modules. Solar Energy. 2018;159:78-87.
[45] Beigi AM, Maroosi A. Parameter identification for solar cells and module using a Hybrid Firefly and Pattern Search Algorithms. Solar Energy. 2018;171:435-46.
[46] Yu K, Liang JJ, Qu BY, Cheng Z, Wang H. Multiple learning backtracking search algorithm for estimating parameters of photovoltaic models. Applied Energy. 2018;226:408-22.
[47] Jordehi AR. Time varying acceleration coefficients particle swarm optimisation (TVACPSO): A new optimisation algorithm for estimating parameters of PV cells and modules. Energy Conversion and Management. 2016;129:262-74.
[48] Jiao S, Chong G, Huang C, Hu H, Wang M, Heidari AA, Chen H, Zhao X. Orthogonally adapted Harris hawks optimization for parameter estimation of photovoltaic models. Energy. 2020;203:117804.
[49] Askarzadeh A, Rezazadeh A. Artificial bee swarm optimization algorithm for parameters identification of solar cell models. Applied Energy. 2013;102:943-49.
[50] Long W, Cai S, Jiao J, Xu M, Wu T. A new hybrid algorithm based on grey wolf optimizer and cuckoo search for parameter extraction of solar photovoltaic models. Energy Conversion and Management. 2020;203:112243.
[51] Deotti LMP, Pereira JLR, Silva Júnior ICd. Parameter extraction of photovoltaic models using an enhanced Lévy flight bat algorithm. Energy Conversion and Management. 2020;221:113114