Fotovoltaik Sistemlerde Harris Hawks Optimizasyonu Kullanılarak Gözlem ve Bozma MPPT Algoritması İçin Hızlı Yakınsama Süresinin Performansı

Yıl 2024, Cilt: 27 Sayı: 6, 2377 - 2387

Seraj Asta Omar , Bilgehan Erkal

https://doi.org/10.2339/politeknik.1488197

Öz

Maksimum Güç Noktası İzleme (MPPT) algoritmalarının verimliliği, fotovoltaik (PV) sistemlerin performansını optimize etmek için çok önemlidir. Perturb ve Observe (P&O) algoritması gibi geleneksel yöntemler, basitlikleri nedeniyle yaygın olarak kullanılır, ancak genellikle yavaş yakınsama ve değişen çevre koşulları altında maksimum güç noktası etrafında salınımlar gibi sorunlardan muzdariptirler. Bu çalışma, sağlam yakınsama özellikleriyle bilinen doğadan ilham alan bir optimizasyon tekniği olan Harris Hawks Optimizasyonu (HHO) ile entegre edilerek geliştirilmiş bir P&O algoritması sunmaktadır. Önerilen hibrit P&O-HHO algoritması, yakınsama süresini hızlandırmayı ve PV sisteminin genel izleme performansını iyileştirmeyi amaçlamaktadır. Değişen güneş ışınımı seviyelerinde PV modüllerinden gelen gücü en üst düzeye çıkarmak için, fotovoltaik sistemlerde geleneksel Perturb and Observe (P&O) yaklaşımı için bir performans iyileştirme yöntemi olarak Harris-Hawks Optimizasyonu (HHO) önerilmektedir. Önerilen model, bir MPPT algoritması, bir PV paneli ve bir dirençli yük tarafından kontrol edilen DC-DC gücü için bir yükseltme dönüştürücüsünü kapsar. Önerilen MPPT algoritması, doğadan ilham alan yeni bir yöntem olan Harris-Hawks Optimizasyonu ile geleneksel P&O yaklaşımını birleştiren hibrit bir tekniğin uygulanması üzerine kurulmuştur. Önerilen yöntem, MATLAB Simulink tarafından oluşturulan ortamdan yararlanılarak simülasyon testi yoluyla test edilmiştir. Simülasyonun bulguları, HHO-P&O MPPT algoritmasının, açıklandığı gibi, küresel maksimum güç noktasını daha verimli bir şekilde başarılı bir şekilde tanımladığını göstermektedir. Ek olarak, standart Perturb ile karşılaştırıldığında hızlı bir yakınsama hızı, üstün sonuçlar sergiledi ve Yöntemi ve hızlı dinamik reaksiyonu gözlemleyin.

Anahtar Kelimeler

Maksimum Güç Noktası Takibi, Harris-Hawks Optimizasyonu, Perturb and Observe, Fotovoltaik

Performance of the Rapid Convergence Time for The Perturb and Observe MPPT Algorithm by Using Harris Hawks Optimization in Photovoltaic Systems

Öz

The efficiency of Maximum Power Point Tracking (MPPT) algorithms is crucial for optimizing the performance of photovoltaic (PV) systems. Traditional methods like the Perturb and Observe (P&O) algorithm are commonly used due to their simplicity, but they often suffer from issues such as slow convergence and oscillations around the maximum power point under changing environmental conditions. This study introduces an enhanced P&O algorithm by integrating it with Harris Hawks Optimization (HHO), a nature-inspired optimization technique known for its robust convergence characteristics. The proposed hybrid P&O-HHO algorithm aims to accelerate convergence time and improve the overall tracking performance of the PV system. To maximize power from PV modules at varying sun irradiance levels, Harris-Hawks Optimization (HHO) is offered as a performance improvement method for the conventional Perturb and Observe (P&O) approach in photovoltaic systems. The proposed model encompasses a boost converter for DC-DC power controlled by an MPPT algorithm, a PV panel, and a resistive load. The MPPT algorithm proposed is founded upon the execution of a hybrid technique that combines Harris-Hawks Optimization, a new method inspired by nature, and the conventional P&O approach. The suggested method has been tested through simulation testing utilizing the environment created by MATLAB Simulink. The findings of the simulation illustrate that‎‎ the HHO-P&O MPPT algorithm, as described, successfully ‎identified the global maximum power point more ‎efficiently.‎ Additionally, it exhibited a rapid convergence speed, superior outcomes in comparison to the standard Perturb and Observe method, and a swift dynamic reaction.

Anahtar Kelimeler

Maximum Power Point Tracking, ‎ Harris-Hawks Optimization, Perturb and Observe‎, Photovoltaic

Kaynakça

• [1] I. Yadav, S. K. Maurya, and G. K. Gupta, “A literature review on industrially accepted MPPT techniques for solar PV system,” Int. J. Electr. Comput. Eng., vol. 10, no. 2, pp. 2117–2127, (2020).
• [2] D. Ounnas, D. Guiza, Y. Soufi, and M. Maamri, “Design and hardware implementation of modified incremental conductance algorithm for photovoltaic system,” Adv. Electr. Electron. Eng., vol. 19, no. 2, pp. 100–111, (2021).
• [3] R. Elshara, A. Hançerlioğullari, J. Rahebi, and J. M. Lopez-Guede, “PV Cells and Modules Parameter Estimation Using Coati Optimization Algorithm,” Energies, vol. 17, no. 7, p. 1716, (2024).
• [4] K. H. Hussein, I. Muta, T. Hoshino, and M. Osakada, “Maximum photovoltaic power tracking: an algorithm for rapidly changing atmospheric conditions,” IEE Proceedings-Generation, Transm. Distrib., vol. 142, no. 1, pp. 59–64, (1995).
• [5] A. A. M. Nureddin, J. Rahebi, and A. Ab-BelKhair, “Power management controller for microgrid integration of hybrid PV/fuel cell system based on artificial deep neural network,” Int. J. Photoenergy, vol. 2020, pp. 1–21, (2020).
• [6] O. H. H. Hameed, U. Kutbay, J. Rahebi, and F. Hardalaç, “Fault Classification for Protection in MMC-HVDC Using Machine Learning Algorithms,” in 2023 IEEE 3rd Mysore Sub Section International Conference (MysuruCon), pp. 1–4, (2023).
• [7] D. P. Hohm and M. E. Ropp, “Comparative study of maximum power point tracking algorithms,” Prog. photovoltaics Res. Appl., vol. 11, no. 1, pp. 47–62, (2003).
• [8] J. Ma, K. L. Man, T. O. Ting, N. Zhang, S.-U. Guan, and P. W. H. Wong, “Dem: direct estimation method for photovoltaic maximum power point tracking,” Procedia Comput. Sci., vol. 17, pp. 537–544, (2013).
• [9] A. H. Abed, J. Rahebi, and A. Farzamnia, “Improvement for power quality by using dynamic voltage restorer in electrical distribution networks,” in 2017 IEEE 2nd International Conference on Automatic Control and Intelligent Systems (I2CACIS), pp. 122–127, (2017).
• [10] T. Esram and P. L. Chapman, “Comparison of photovoltaic array maximum power point tracking techniques,” IEEE Trans. energy Convers., vol. 22, no. 2, pp. 439–449, (2007).
• [11] F. Liu, Y. Kang, Y. Zhang, and S. Duan, “Comparison of P&O and hill climbing MPPT methods for grid-connected PV converter,” in 2008 3rd IEEE Conference on Industrial Electronics and Applications, pp. 804–807, (2008).
• [12] B. Liu, S. Duan, F. Liu, and P. Xu, “Analysis and improvement of maximum power point tracking algorithm based on incremental conductance method for photovoltaic array,” in 2007 7th International Conference on Power Electronics and Drive Systems, pp. 637–641, (2007).
• [13] J. Zhao, X. Zhou, Y. Ma, and Y. Liu, “Analysis of dynamic characteristic for solar arrays in series and global maximum power point tracking based on optimal initial value incremental conductance strategy under partially shaded conditions,” Energies, vol. 10, no. 1, p. 120, (2017).
• [14] L. Shang, H. Guo, and W. Zhu, “An improved MPPT control strategy based on incremental conductance algorithm,” Prot. Control Mod. Power Syst., vol. 5, no. 2, pp. 1–8, (2020).
• [15] A. Ab-BelKhair, J. Rahebi, and A. Abdulhamed Mohamed Nureddin, “A study of deep neural network controller-based power quality improvement of hybrid PV/Wind systems by using smart inverter,” Int. J. Photoenergy, vol. 2020, pp. 1–22, (2020).
• [16] A. M. Eltamaly, “Photovoltaic maximum power point trackers: an overview,” Adv. Technol. Sol. photovoltaics energy Syst., pp. 117–200, (2021).
• [17] O. Hazim Hameed Hameed, U. Kutbay, J. Rahebi, F. Hardalaç, and I. Mahariq, “Enhancing Fault Detection and Classification in MMC‐HVDC Systems: Integrating Harris Hawks Optimization Algorithm with Machine Learning Methods,” Int. Trans. Electr. Energy Syst., vol. 2024, no. 1, p. 6677830, (2024).
• [18] H. DemiRel, M. K. Karagöz, and B. Erkal, “A Novel MPPT Method for PV Arrays Based on Modified Bat Algorithm and Incremental Conductance Algorithm with Partial Shading Capability,” in Proceedings of the First International Conference on Energy Systems Engineering, Karabuk, Turkey, pp. 2–4, (2017).
• [19] M. V. Da Rocha, L. P. Sampaio, and S. A. O. da Silva, “Comparative analysis of MPPT algorithms based on Bat algorithm for PV systems under partial shading condition,” Sustain. Energy Technol. Assessments, vol. 40, p. 100761, (2020).
• [20] Ö. Çelik and A. Teke, “A Hybrid MPPT method for grid connected photovoltaic systems under rapidly changing atmospheric conditions,” Electr. Power Syst. Res., vol. 152, pp. 194–210, (2017).
• [21] K. Bataineh and D. Dalalah, “Optimal configuration for design of stand-alone PV system,” Smart Grid Renew. Energy, vol. 3, no. 02, p. 139, (2012).
• [22] A. O. Baba, G. Liu, and X. Chen, “Classification and evaluation review of maximum power point tracking methods,” Sustain. Futur., vol. 2, p. 100020, (2020).
• [23] Z. Yusupov, N. Almagrahi, E. Yaghoubi, E. Yaghoubi, A. Habbal, and D. Kodirov, “Modeling and Control of Decentralized Microgrid Based on Renewable Energy and Electric Vehicle Charging Station,” in World Conference Intelligent System for Industrial Automation, pp. 96–102, (2022).
• [24] K. Hasan, S. B. Yousuf, M. S. H. K. Tushar, B. K. Das, P. Das, and M. S. Islam, “Effects of different environmental and operational factors on the PV performance: A comprehensive review,” Energy Sci. Eng., vol. 10, no. 2, pp. 656–675, (2022).
• [25] Z. Yusupov, E. Yaghoubi, and E. Yaghoubi, “Controlling and tracking the maximum active power point in a photovoltaic system connected to the grid using the fuzzy neural controller,” in 2023 14th International Conference on Electrical and Electronics Engineering (ELECO), pp. 1–5, (2023).
• [26] D. Verma, S. Nema, A. M. Shandilya, and S. K. Dash, “Maximum power point tracking (MPPT) techniques: Recapitulation in solar photovoltaic systems,” Renew. Sustain. Energy Rev., vol. 54, pp. 1018–1034, (2016).
• [27] J. Ma, “Optimization approaches for parameter estimation and maximum power point tracking (MPPT) of photovoltaic systems.” University of Liverpool Liverpool, UK, (2014).
• [28] M. S. Nkambule, Photovoltaic system maximum power point tracking under partial shaded weather conditions using machine learning algorithms. University of Johannesburg (South Africa), (2019).
• [29] H. Belghiti et al., “Efficient and robust control of a standalone PV-storage system: An integrated single sensor-based nonlinear controller with TSCC-battery management,” J. Energy Storage, vol. 95, p. 112630, (2024).
• [30] H. Dinçer, A. M. Ibrahimi, M. Ahmadi, and M. S. S. Danish, “A Blueprint for Sustainable Electrification by Designing and Implementing PV Systems in Small Scales,” in International Conference on Collaborative Endeavors for Global Sustainability, pp. 163–186, (2024).
• [31] A. A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, and H. Chen, “Harris hawks optimization: Algorithm and applications,” Futur. Gener. Comput. Syst., vol. 97, pp. 849–872, (2019).