بهینه‌سازی فرایند شناسایی پارامتر مدل های تک‌دیودی، دودیودی، و سه‌دیودی سلول خورشیدی توسط الگوریتم بهینه سازی شبکه عصبی

نوع مقاله : مقاله پژوهشی

نویسندگان

• علی سعداله ¹
• جواد ریاضت ²

¹ دانشکده مهندسی مکانیک. دانشگاه علم و فرهنگ، تهران، ایران

² دانشکده مهندسی مکانیک، دانشگاه علم و فرهنگ، تهران، ایران

10.22067/jacsm.2023.81702.1176

چکیده

شناخت دقیق متغیرهای مجهول برای انواع سلول‌های خورشیدی با استفاده از داده‌های آزمایشگاهی اهمیت حیاتی در طراحی، کنترل، کیفیت، برآورد هزینه، و پیش-بینی عملکرد سلول‌های خورشیدی دارد. گسترش مشخصه‌های‌ یک سلول‌ خورشیدی واحد به مجموعه‌ای از سلول‌ها (پنل خورشیدی) معمولا ً براساس یک نقطه عملیاتی واحد بر روی منحنی مشخصه جریان- ولتاژ یکی از این سلول‌ها انجام می‌شود. در سال‌های اخیر، یک روش جدید برای پیش‌بینی عملکرد سلول و غربالگری سلولی با مدل‌سازی سلول با استفاده از یک مدار الکتریکی معادل ارائه شده ‌است که در آن، هر متغیر، مربوط به یک پدیده فیزیکی در سلول خورشیدی است. این مدل‌های تحلیلی می‌توانند با یک مدل پنج، هفت، و اخیرا نه متغیری نشان داده شوند. به واسطه غیرخطی بودن و ناتوانی روش های بهینه سازی سنتی در شناسایی دقیق متغیر های ناشناخته سیستم، اخیرا الگوریتم های بهینه سازی هوشمند، توجهات چشمگیری را در حل این نوع مسایل مهندسی به خود جلب کرده اند. الگوریتم شبکه عصبی یک الگوریتم فراابتکاری است که از عملکرد شبکه عصبی مغز انسان الهام گرفته است. در این مقاله، تکنیک شناسایی پارامتر بهینه یک سلول خورشیدی تجاری برای مدل تک دیودی، دو دیودی، و سه ‌دیودی توسط الگوریتم شبکه عصبی انجام گرفته است. نتایج بدست آمده از روش پیشنهادی دارای دقت پاسخ بهتری نسبت به الگوریتم ‌های بهینه سازی اخیر در منابع است. این بهبود عملکرد به ترتیب برای مدل های تک دیودی، دو دیودی، و سه دیودی به میزان 0.44، 0.085، و 17.97 درصد نسبت به بهترین روش مطالعه شده در منابع حاصل شده است.

کلیدواژه‌ها

• سلول خورشیدی
• بهینه سازی
• الگوریتم های فراابتکاری
• شناسایی پارامتر
• الگوریتم شبکه عصبی

موضوعات

• بهینه سازی

---

عنوان مقاله [English]

Parameter Optimization of Various Solar Cell Models by Neural Network Algorithm

نویسندگان [English]

• Ali Sadollah ¹
• Javad Riazat ²

¹ Department of Mechanical Engineering, University of Science and Culture, Tehran, Iran

² Department of Mechanical Engineering, University of Science and Culture, Iran

چکیده [English]

Precise recognition of unknown variables for different types of solar cells is important in design, control, quality, cost estimation, and prediction of solar cell performance. Development of a single solar cell to a set of cells (solar panels) is usually based on a single operating point on the current-voltage characteristic curve. In recent years, a new method to predict cell performance and cell screening by modeling the cell is presented using an equivalent electrical circuit in which each variable corresponds to a physical phenomenon in the solar cell. These analytical models can be represented by a five-variable, seven-variable models, and recently nine-variable model. Due to the nonlinearities and inability of traditional methods in identifying the unknown variables of the system, recently intelligent algorithms have attracted considerable attentions in solving engineering problems. Neural network algorithm (NNA) is a metaheuristic optimization algorithm that is inspired by the function of the neural network of human brain. In this article, the optimum parameter identification technique of a silicon commercial solar cell is used for single diode, two diode, and three diode models. The obtained optimization results of this research are compared with other optimizers in the literature and the surrounding discussions are done. The improvement level reported by the NNA in comparison with the best reported results in the literature for one, two, and three diode models are 0.44, 0.085, and 17.97 percent, respectively. The obtained results of the proposed NNA method have the highest accuracy among the other optimizers in the literature.

کلیدواژه‌ها [English]

• Solar cell
• Optimization
• Metaheuristics
• Parameter identification
• Neural network algorithm

مراجع

[1] Arshad, M. "Clean and sustainable energy technologies." In Clean energy for sustainable development, pp. 73-89. Academic Press (2017).

[2] Q. Li, S. Yu, A. S. Al-Sumaiti, K. Turitsyn, “Micro Water–Energy Nexus: Optimal Demand-Side Management and Quasi-Convex Hull Relaxation”, IEEE Transactions on Control of Network Systems, vol. 6, no. 4, pp. 1313-1322, (2018).

[3]           B. Mohandes, S. Acharya, M. S. El Moursi, A. S. Al-Sumaiti, H. Doukas, S. Sgouridis, “Optimal Design of an Islanded Microgrid with Load Shifting Mechanism Between Electrical and Thermal Energy Storage Systems”, IEEE Transactions on Power Systems, vol. 35, no. 4, pp. 2642-2657, (2020).

[4] T. Ma, H. Yang, L. Lu, “Solar PhotovoltaicSsystem Modeling and Performance Prediction”, Renewable and Sustainable Energy Reviews, Vol. 36, pp. 304-315, (2014).

[5] A. Al-Sumaiti, M. H. Ahmed, S. Rivera, M. S. El Moursi, M. M. A. Salama, T. S. Alsumaiti, “A Stochastic PV Model for Power System Planning Applications ”, IET Renewable Power Generation, vol. 13, no. 16, pp. 3168-3179, (2019).

[6] V. Lo Brano, A. Orioli, G. Ciulla, A. Di Gangi, “An Improved Five-Parameter Model for Photovoltaic Modules”, Solar Energy Materials and Solar Cells, vol. 94, no. 8, pp. 1358-1370, (2010).

[7] A. S. Al-Sumaiti, M. M. A. Salama, S. Reddy, A. Kavousi-Fard, “A Guided Procedure for Governance Institutions to Regulate Funding Requirements of Solar PV Projects”, IEEE Access, vol. 7, pp. 54203-54217, (2019).

[8] B. Amrouche, A. Guessoum, M. Belhamel, “A Simple Behavioural Model for Solar Module Electric Characteristics Based on the First Order System Step Response for MPPT study and comparison”, Applied Energy, vol. 91, no. 1, pp. 395-404, (2012).

[9] A. Orioli, A. Di Gangi, “A Procedure to Calculate the Five-Parameter Model of Crystalline Silicon Photovoltaic Modules on the Basis of the Tabular Performance Data”, Applied Energy, vol. 102, pp. 1160-1177, (2013).

[10]L. Sandrolini, M. Artioli, U. Reggiani, “Numerical Method for the Extraction of Photovoltaic Module Double-Diode Model Parameters Through Cluster Analysis”, Applied Energy, vol. 87, no. 2, pp. 442-451, (2010).

[11]F. Bonanno, G. Capizzi, G. Graditi, G. Napoli, G. M. Tina, “A Radial Basis Function Neural Network Based Approach for the Electrical Characteristics Estimation of A Photovoltaic Module”, Applied Energy, vol. 97, pp. 956-961, (2012).

[12]V. Khanna, B. k. Das, D. Bisht, D. Singh, vandana, P. K. Singh, “A Three Diode Model for Industrial Solar Cells and Estimation of Solar Cell Parameters Using PSO Algorithm”, Renewable Energy, vol. 78, pp. 105-113, (2015).

[13]T. Easwarakhanthan, J. Bottin, I. Bouhouch, C. Boutrit, “Nonlinear Minimization Algorithm for Determining the Solar Cell Parameters with Microcomputers”, International journal of Solar Energy, vol. 4, no. 1, pp. 1-12, (1986).

[14]A. Jain, A. Kapoor, “Exact Analytical Solutions of the Parameters of Real Solar Cells Using Lambert W-Function”, Solar Energy Materials and Solar Cells, vol. 81, no. 2, pp. 269-277, (2004).

[15]S. Haneefa, S. Karmalkar, “An Analytical Method to Extract the Physical Parameters of a Solar Cell From Four Points on the Illuminated J-V Curve”, IEEE Electron Device Letters, vol. 30, no. 4, pp. 349-352, (2009).

[16]D. S. H. Chan, J. R. Phillips, J. C. H. Phang, “A Comparative Study of Extraction Methods for Solar Cell Model Parameters”, Solid-State Electronics, vol. 29, no. 3, pp. 329-337, (1986).

[17]H. Wei, J. Cong, X. Lingyun, S. Deyun, “Extracting Solar Cell Model Parameters Based on Chaos Particle Swarm Algorithm”, International conference on electric information and control engineering, IEEE, Wuhan, pp. 398-402, 15-17 April, (2011).

[18]M. R. AlRashidi, M. F. AlHajri, K. M. El-Naggar, A. K. Al-Othman, “A New Estimation Approach for Determining the I–V Characteristics of Solar Cells”, Solar Energy, vol. 85, no. 7, pp. 1543-1550, (2011).

[19]K. M. El-Naggar, M. R. AlRashidi, M. F. AlHajri, A. K. Al-Othman, “Simulated Annealing Algorithm for Photovoltaic Parameters Identification”, Solar Energy, vol. 86, no. 1, pp. 266-274, (2012).

[20]M. Gómez, M. López, F. Jurado, “Optimal Placement and Sizing From Standpoint of the Investor of Photovoltaics Grid-Connected Systems Using Binary Particle Swarm Optimization”, Applied Energy, vol. 87, no. 6, pp. 1911-1918, (2010).

[21]O. Ekren, B. Y. Ekren, “Size Optimization of A PV/Wind Hybrid Energy Conversion System with Battery Storage Using Simulated Annealing”, Applied Energy, vol. 87, no. 2, pp. 592-598, (2010).

[22]C. R. S. Reinoso, M. Cutrera, M. Battioni, D. Milone, R. Buitrago, “Photovoltaic Generation Model as a Function of Weather Variables Using Artificial Intelligence Techniques”, International journal of hydrogen energy, vol. 37, no. 19, pp. 14781-14785, (2012).

[23]T. Niknam, S. I. Taheri, J. Aghaei, S. Tabatabaei, M. Nayeripour, “A Modified Honey Bee Mating Optimization Algorithm for Multiobjective Placement of Renewable Energy Resources”, Applied Energy, vol. 88, no. 12, pp. 4817-4830, (2011).

[24]T. R. Ayodele, A. S. O. Ogunjuyigbe, E. E. Ekoh, “Evaluation of Numerical Algorithms Used in Extracting the Parameters of a Single-Diode Photovoltaic Model”, Sustainable Energy Technologies and Assessments, vol. 13, pp. 51-59, (2016).

[25]D. Oliva, M. Abd Elaziz, A. H. Elsheikh, A. A. Ewees, “A Review on Meta-Heuristics Methods for Estimating Parameters of Solar Cells”, Journal of Power Sources, vol. 435, p. 126683, (2019).

[26]G. Xiong, J. Zhang, X. Yuan, D. Shi, Y. He, “Application of Symbiotic Organisms Search Algorithm for Parameter Extraction of Solar Cell Models”, Applied Sciences, vol. 8, no. 11, pp. 2155, (2018).

[27] C. Dai, W. Chen, Y. Zhu, “Seeker Optimization Algorithm for Digital IIR Filter Design”, IEEE transactions on industrial electronics, vol. 57, no. 5, pp.1710-1718, (2009).

[28] A. Sadollah, H. Sayyaadi, A. Yadav, “A Dynamic Metaheuristic Optimization Model Inspired By Biological Nervous Systems: Neural Network Algorithm”, Applied Soft Computing, vol. 71, pp. 747-782, (2018).

[29]J. S. Chohan, N. Mittal, R. Kumar, S. Singh, S. Sharma, J. Singh, K. V. Rao, M. Mia, D. Y. Pimenov, S. P. Dwivedi, “Mechanical Strength Enhancement of 3D Printed Acrylonitrile Butadiene Styrene Polymer Components Using Neural Network Optimization Algorithm”, Polymers, vol. 12, no. 10, pp. 2250, (2020).

[30]M. S. AbouOmar, H-J. Zhang, Y. Su, “Fractional Order Fuzzy PID Control of Automotive PEM Fuel Cell Air Feed System Using Neural Network Optimization Algorithm”, Energies, vol. 12, no. 8, p. 1435, (2019).

[31] M. Fawzi, A. El‐Fergany, H. M. Hasanien, “Effective Methodology Based on Neural Network Optimizer for Extracting Model Parameters of PEM Fuel Cells”, International Journal of Energy Research, vol. 43, no. 14, pp. 8136-8147, (2019).

[32] Y. Zhang, Z. Jin, Y. Chen, “Hybrid Teaching–Learning-Based Optimization and Neural Network Algorithm for Engineering Design Optimization Problems”, Knowledge-Based Systems, vol. 187, pp. 104836, (2020).

[33] Z. Yiying, Z. Jin, Y. Chen, “Hybridizing Grey Wolf Optimization with Neural Network Algorithm for Global Numerical Optimization Problems”, Neural Computing and Applications, vol. 32, pp. 10451-10470, (2020).

[34] D. Khurana, A. Yadav, A. Sadollah, “A Non-Dominated Sorting Based Multi-Objective Neural Network Algorithm”, MethodsX, vol. 10, pp. 102152, (2023).

[35] D. C. Montgomery, C. St, “Design and Analysis of Experiments”, John Wiley and Sons, (2005).

[36] A. Askarzadeh, A. Rezazadeh, “Artificial Bee Swarm Optimization Algorithm for Parameters Identification of Solar Cell Models”, Applied Energy, vol. 102, pp. 943-949, (2013).

[37] M. AlHajri, K. El-Naggar, M. AlRashidi, A. Al-Othman, “Optimal Extraction of Solar Cell Parameters Using Pattern Search”, Renewable Energy, vol. 44, pp. 238-245, (2012).

[38] A. Askarzadeh, A. Rezazadeh, “Parameter Identification for Solar Cell Models Using Harmony Search-Based Algorithms”, Solar Energy, vol. 86, no. 11, pp. 3241-3249, (2012).

[39] S. Mirjalili, S. M. Mirjalili, A. Lewis, “Grey Wolf Optimizer”, Advances in Engineering Software, vol. 69, pp. 46-61, (2014).

[40] S. Mirjalili, “Moth-Flame Optimization Algorithm: A Novel Nature-Inspired Heuristic Paradigm”, Knowledge-based systems, vol. 89, pp. 228-249, (2015).

[41] D. Oliva, M. Abd El Aziz, A. E. Hassanien, “Parameter Estimation of Photovoltaic Cells Using an Improved Chaotic Whale Optimization Algorithm”, Applied Energy, vol. 200, pp. 141-154, (2017).

[42] S. Mirjalili, “SCA: A Sine Cosine Algorithm for Solving Optimization Problems”, Knowledge-based systems, vol. 96, pp. 120-133, (2016).

[43] S. Mirjalili, S. M. Mirjalili, A. Hatamlou, “Multi-Verse Optimizer: A Nature-Inspired Algorithm for Global Optimization”, Neural Computing and Applications, vol. 27, pp. 495-513, (2016).

[44] S. Mirjalili, “The Ant Lion Optimizer”, Advances in Engineering Software, vol. 83, pp. 80-98, (2015).

[45] E. H. Houssein, G. N. Zaki, A. A. Z. Diab, E. M. G. Younis, “An Efficient Manta Ray Foraging Optimization Algorithm for Parameter Extraction of Three-Diode Photovoltaic Model”, Computers & Electrical Engineering, vol. 94, pp. 107304, (2021).