An Approach Based on Golden Eagle Optimization Algorithm for Maximum Power Point Tracking of PV Panel Under Partial Shading Conditions

Yıl 2025, ERKEN GÖRÜNÜM, 1 - 1

Waleed Mohammed .m. Aburas , Necmi Serkan Tezel

https://doi.org/10.2339/politeknik.1451381

Öz

This study introduces an innovative Maximum Power Point Tracking (MPPT) technique utilizing the Golden Eagle Optimization (GEO) method, specifically designed to enhance the efficiency of photovoltaic (PV) systems under partial shading conditions. Unlike traditional MPPT approaches that struggle with local peaks in power-voltage curves caused by shading, the GEO method leverages the hunting behavior-inspired algorithm to accurately locate the global maximum power point (GMPP). The effectiveness of the GEO MPPT technique is demonstrated through extensive simulations across three diverse case scenarios, each representing different partial shading patterns. In all scenarios, the GEO method outperforms conventional MPPT techniques, showcasing its adaptability and superior performance in challenging conditions. The successful implementation of GEO MPPT leads to substantial improvements in PV panel energy extraction efficiency, even when faced with the complexities of partial shading. This research contributes significantly to the advancement of solar PV systems, enhancing their reliability and performance in real-world environments. By mitigating the impact of partial shading, this work promotes the wider adoption of solar energy as a viable and sustainable power solution.

Anahtar Kelimeler

Golden Eagle Optimization Algorithm, Maximum Power Point Tracking, PV Panel, Partial Shading Conditions.

Kaynakça

• [1] A. Raihan et al., “Nexus between carbon emissions, economic growth, renewable energy use, urbanization, industrialization, technological innovation, and forest area towards achieving environmental sustainability in Bangladesh,” Energy Clim. Chang., vol. 3, p. 100080, (2022).
• [2] M. A. Bhuiyan, Q. Zhang, V. Khare, A. Mikhaylov, G. Pinter, and X. Huang, “Renewable energy consumption and economic growth nexus—a systematic literature review,” Front. Environ. Sci., vol. 10, p. 878394, (2022).
• [3] N. R. Deevela, T. C. Kandpal, and B. Singh, “A review of renewable energy based power supply options for telecom towers,” Environ. Dev. Sustain., pp. 1–68, (2023).
• [4] 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).
• [5] M. Ali, M. Ahmad, M. A. Koondhar, M. S. Akram, A. Verma, and B. Khan, “Maximum power point tracking for grid-connected photovoltaic system using Adaptive Fuzzy Logic Controller,” Comput. Electr. Eng., vol. 110, p. 108879, (2023).
• [6] P. V. Mahesh, S. Meyyappan, and R. Alla, “Maximum power point tracking with regression machine learning algorithms for solar PV systems,” Int. J. Renew. Energy Res., vol. 12, no. 3, pp. 1327–1338, (2022).
• [7] E. Yaghoubi, E. Yaghoubi, Z. Yusupov, and J. Rahebi, “Real-time techno-economical operation of preserving microgrids via optimal NLMPC considering uncertainties,” Eng. Sci. Technol. an Int. J., vol. 57, p. 101823, (2024).
• [8] N. M. M. Altwallbah, M. A. M. Radzi, N. Azis, S. Shafie, and M. A. A. M. Zainuri, “New perturb and observe algorithm based on trapezoidal rule: Uniform and partial shading conditions,” Energy Convers. Manag., vol. 264, p. 115738, (2022).
• [9] Z. Yusupov, E. Yaghoubi, and E. Yaghoubi, “Controlling and tracking the maximum active power point in a photovoltaic sys-tem connected to the grid using the fuzzy neural controller”.
• [10] A. Afzal et al., “Optimizing the thermal performance of solar energy devices using meta-heuristic algorithms: A critical review,” Renew. Sustain. Energy Rev., vol. 173, p. 112903, (2023).
• [11] 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).
• [12] 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).
• [13] 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).
• [14] K. Ishaque and Z. Salam, “A deterministic particle swarm optimization maximum power point tracker for photovoltaic system under partial shading condition,” IEEE Trans. Ind. Electron., vol. 60, no. 8, pp. 3195–3206, (2012).
• [15] Z.-K. Fan, K.-L. Lian, and J.-F. Lin, “A New Golden Eagle Optimization with Stooping Behaviour for Photovoltaic Maximum Power Tracking under Partial Shading,” Energies, vol. 16, no. 15, p. 5712, (2023).
• [16] P. Hou, W. Hu, and Z. Chen, “Optimisation for offshore wind farm cable connection layout using adaptive particle swarm optimisation minimum spanning tree method,” IET Renew. Power Gener., vol. 10, no. 5, pp. 694–702, (2016).
• [17] E. Yaghoubi, E. Yaghoubi, Z. Yusupov, and M. R. Maghami, “A Real-Time and Online Dynamic Reconfiguration against Cyber-Attacks to Enhance Security and Cost-Efficiency in Smart Power Microgrids Using Deep Learning,” Technologies, vol. 12, no. 10, p. 197, (2024).
• [18] D. Zhang et al., “Economic and sustainability promises of wind energy considering the impacts of climate change and vulnerabilities to extreme conditions,” Electr. J., vol. 32, no. 6, pp. 7–12, (2019).
• [19] D. Icaza and D. Borge-Diez, “Technical and economic design of a novel hybrid system photovoltaic/wind/hydrokinetic to supply a group of sustainable buildings in the shape of airplanes,” Heliyon, vol. 9, no. 3, (2023).
• [20] İ. Erdoğan, K. Bilen, and S. Kıvrak, “Experimental investigation of the efficiency of solar panel over which water film flows,” Politek. Derg., vol. 27, no. 2, pp. 699–707, (2023).
• [21] A. E. COŞGUN and H. DEMİR, “The experimental study of dust effect on solar panel efficiency,” Politek. Derg., vol. 25, no. 4, pp. 1429–1434, (2022).
• [22] E. E. H. A. O. Swese and A. Hançerlioğulları, “Investigation of performance on photovoltaic/thermal (PV/T) system using magnetic nanofluids,” Politek. Derg., vol. 25, no. 1, pp. 411–416, (2022).
• [23] M. R. Maghami, H. Hizam, C. Gomes, M. A. Radzi, M. I. Rezadad, and S. Hajighorbani, “Power loss due to soiling on solar panel: A review,” Renew. Sustain. Energy Rev., vol. 59, pp. 1307–1316, (2016).
• [24] L. Zareian, J. Rahebi, and M. J. Shayegan, “Bitterling fish optimization (BFO) algorithm,” Multimed. Tools Appl., pp. 1–34, (2024).
• [25] D. C. Jones and R. W. Erickson, “Probabilistic analysis of a generalized perturb and observe algorithm featuring robust operation in the presence of power curve traps,” IEEE Trans. Power Electron., vol. 28, no. 6, pp. 2912–2926, (2012).
• [26] A. Faisal, J. Munilla, and J. Rahebi, “Detection of optic disc in human retinal images utilizing the Bitterling Fish Optimization (BFO) algorithm,” Sci. Rep., vol. 14, no. 1, p. 25824, (2024).
• [27] O. S. Al-butti, M. Burunkaya, J. Rahebi, and J. M. Lopez-Guede, “Optimal Power Flow using PSO algorithms based on Artificial Neural Networks,” IEEE Access, (2024).
• [28] M. A. Elberri, Ü. Tokeşer, J. Rahebi, and J. M. Lopez-Guede, “A cyber defense system against phishing attacks with deep learning game theory and LSTM-CNN with African vulture optimization algorithm (AVOA),” Int. J. Inf. Secur., pp. 1–24, (2024).
• [29] R. C. N. Pilawa-Podgurski, W. Li, I. Celanovic, and D. J. Perreault, “Integrated CMOS energy harvesting converter with digital maximum power point tracking for a portable thermophotovoltaic power generator,” IEEE J. Emerg. Sel. Top. Power Electron., vol. 3, no. 4, pp. 1021–1035, (2015).
• [30] B. N. Alajmi, K. H. Ahmed, S. J. Finney, and B. W. Williams, “Fuzzy-logic-control approach of a modified hill-climbing method for maximum power point in microgrid standalone photovoltaic system,” IEEE Trans. power Electron., vol. 26, no. 4, pp. 1022–1030, (2010).
• [31] G.-C. Hsieh, H.-I. Hsieh, C.-Y. Tsai, and C.-H. Wang, “Photovoltaic power-increment-aided incremental-conductance MPPT with two-phased tracking,” IEEE Trans. Power Electron., vol. 28, no. 6, pp. 2895–2911, (2012).
• [32] J. Ahmed and Z. Salam, “An enhanced adaptive P&O MPPT for fast and efficient tracking under varying environmental conditions,” IEEE Trans. Sustain. Energy, vol. 9, no. 3, pp. 1487–1496, (2018).
• [33] P. Manoharan et al., “Improved perturb and observation maximum power point tracking technique for solar photovoltaic power generation systems,” IEEE Syst. J., vol. 15, no. 2, pp. 3024–3035, (2020).
• [34] Z. Sun, Y. Jang, and S. Bae, “Optimized Voltage Search Algorithm for Fast Global Maximum Power Point Tracking in Photovoltaic Systems,” IEEE Trans. Sustain. Energy, vol. 14, no. 1, pp. 423–441, (2022).
• [35] R. Ahmed Ali Agoub, A. Hançerlioğullari, J. Rahebi, and J. M. Lopez-Guede, “Battery Charge Control in Solar Photovoltaic Systems Based on Fuzzy Logic and Jellyfish Optimization Algorithm,” Appl. Sci., vol. 13, no. 20, p. 11409, (2023).
• [36] 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).
• [37] S. Lyden and M. E. Haque, “A simulated annealing global maximum power point tracking approach for PV modules under partial shading conditions,” IEEE Trans. Power Electron., vol. 31, no. 6, pp. 4171–4181, (2015).
• [38] M. Miyatake, M. Veerachary, F. Toriumi, N. Fujii, and H. Ko, “Maximum power point tracking of multiple photovoltaic arrays: A PSO approach,” IEEE Trans. Aerosp. Electron. Syst., vol. 47, no. 1, pp. 367–380, (2011).
• [39] E. Yaghoubi, E. Yaghoubi, A. Khamees, and A. H. Vakili, “A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering,” Neural Comput. Appl., pp. 1–45, (2024).
• [40] E. Yaghoubi, E. Yaghoubi, A. Khamees, D. Razmi, and T. Lu, “A systematic review and meta-analysis of machine learning, deep learning, and ensemble learning approaches in predicting EV charging behavior,” Eng. Appl. Artif. Intell., vol. 135, p. 108789, (2024).
• [41] K. L. Lian, J. H. Jhang, and I. S. Tian, “A maximum power point tracking method based on perturb-and-observe combined with particle swarm optimization,” IEEE J. photovoltaics, vol. 4, no. 2, pp. 626–633, (2014).
• [42] K. Ishaque, Z. Salam, M. Amjad, and S. Mekhilef, “An improved particle swarm optimization (PSO)–based MPPT for PV with reduced steady-state oscillation,” IEEE Trans. Power Electron., vol. 27, no. 8, pp. 3627–3638, (2012).
• [43] S. Makhloufi and S. Mekhilef, “Logarithmic PSO-based global/local maximum power point tracker for partially shaded photovoltaic systems,” IEEE J. Emerg. Sel. Top. Power Electron., vol. 10, no. 1, pp. 375–386, (2021).
• [44] I. S. Millah, P. C. Chang, D. F. Teshome, R. K. Subroto, K. L. Lian, and J.-F. Lin, “An enhanced grey wolf optimization algorithm for photovoltaic maximum power point tracking control under partial shading conditions,” IEEE Open J. Ind. Electron. Soc., vol. 3, pp. 392–408, (2022).
• [45] D. F. Teshome, C. H. Lee, Y. W. Lin, and K. L. Lian, “A modified firefly algorithm for photovoltaic maximum power point tracking control under partial shading,” IEEE J. Emerg. Sel. Top. Power Electron., vol. 5, no. 2, pp. 661–671, (2016).
• [46] A. M. Eltamaly, Z. A. Almutairi, and M. A. Abdelhamid, “Modern optimization algorithm for improved performance of maximum power point tracker of partially shaded PV systems,” Energies, vol. 16, no. 13, p. 5228, (2023).
• [47] K. Aygül, M. Cikan, T. Demirdelen, and M. Tumay, “Butterfly optimization algorithm based maximum power point tracking of photovoltaic systems under partial shading condition,” Energy Sources, Part A Recover. Util. Environ. Eff., vol. 45, no. 3, pp. 8337–8355, (2023).
• [48] N. Akram, L. Khan, S. Agha, and K. Hafeez, “Global Maximum Power Point Tracking of Partially Shaded PV System Using Advanced Optimization Techniques,” Energies, vol. 15, no. 11, p. 4055, (2022).
• [49] J. P. Ram and N. Rajasekar, “A new global maximum power point tracking technique for solar photovoltaic (PV) system under partial shading conditions (PSC),” Energy, vol. 118, pp. 512–525, (2017).
• [50] S. Chtita et al., “A novel hybrid GWO–PSO-based maximum power point tracking for photovoltaic systems operating under partial shading conditions,” Sci. Rep., vol. 12, no. 1, p. 10637, (2022).
• [51] G. Hu, L. Chen, X. Wang, and G. Wei, “Differential evolution-boosted sine cosine golden eagle optimizer with Lévy flight,” J. Bionic Eng., vol. 19, no. 6, pp. 1850–1885, (2022).
• [52] Z. A. Khan, S. F. Akhter, S. Islam, and F. Abid, “A Golden Eagle Optimization Based MPPT Control For Partial Shading Conditions,” in 2022 IEEE International Conference on Power Electronics, Smart Grid, and Renewable Energy (PESGRE), pp. 1–6. (2022).
• [53] A. Mohammadi-Balani, M. D. Nayeri, A. Azar, and M. Taghizadeh-Yazdi, “Golden eagle optimizer: A nature-inspired metaheuristic algorithm,” Comput. Ind. Eng., vol. 152, p. 107050, (2021).
• [54] Q. Kellner, E. Hosseinzadeh, G. Chouchelamane, W. D. Widanage, and J. Marco, “Battery cycle life test development for high-performance electric vehicle applications,” J. Energy Storage, vol. 15, pp. 228–244, (2018).
• [55] J. Qiu et al., “GHz laser-free time-resolved transmission electron microscopy: A stroboscopic high-duty-cycle method,” Ultramicroscopy, vol. 161, pp. 130–136, (2016).
• [56] V. B. Llorente, L. A. Diaz, G. I. Lacconi, G. C. Abuin, and E. A. Franceschini, “Effect of duty cycle on NiMo alloys prepared by pulsed electrodeposition for hydrogen evolution reaction,” J. Alloys Compd., vol. 897, p. 163161, (2022).
• [57] H. A. Alsattar, A. A. Zaidan, and B. B. Zaidan, “Novel meta-heuristic bald eagle search optimisation algorithm,” Artif. Intell. Rev., vol. 53, pp. 2237–2264, (2020).