Parçacık Sürüsü Optimizasyonu ile Fotovoltaik Sistemlerde Maksimum Güç Noktası Takibi ve Verimlilik Artışı

Öz

Artan küresel enerji talebi ve çevresel sürdürülebilirlik hedefleri doğrultusunda yenilenebilir enerji kaynaklarına olan ilgi hızla artmaktadır. Bu kaynaklar arasında fotovoltaik (FV) sistemler, doğrudan güneş enerjisinden elektrik üretme kabiliyetleri ve çevre dostu yapıları nedeniyle önemli bir alternatif olarak ortaya çıkmıştır. Ancak, fotovoltaik panellerin çıkış gücü, güneş ışınımı, ortam sıcaklığı ve yük koşulları gibi faktörlere bağlı olarak sürekli dalgalanmakta ve bu da sistem verimliliğinde değişikliklere yol açmaktadır. Bu nedenle, toplam enerji üretimini optimize etmek için sistemi her zaman Maksimum Güç Noktası'nda (MPP) çalıştırmak esastır. Bu çalışmada, fotovoltaik sistemlerde maksimum güç noktasını doğru, hızlı ve kararlı bir şekilde izlemek için Parçacık Sürüsü Optimizasyonu (PSO) tabanlı bir Maksimum Güç Noktası Takibi (MPPT) algoritması önerilmiştir. Doğadan ilham alan meta-sezgisel bir algoritma olan PSO, geleneksel MPPT tekniklerine kıyasla daha yüksek takip doğruluğu, daha hızlı tepki süresi ve iyileştirilmiş kararlılık sunmaktadır. MATLAB/Simulink ortamında yürütülen simülasyon çalışmaları, PSO tabanlı yaklaşımın değişen çevre koşullarında etkili bir performans gösterdiğini ve fotovoltaik sistemlerin enerji verimliliğini önemli ölçüde artırdığını göstermektedir. Sonuçlar, PSO algoritmasının PV sistemlerinin dinamik özelliklerine uyum sağlayabildiğini ve gerçek zamanlı izleme uygulamaları için verimli bir çözüm sunduğunu göstermektedir.

Anahtar Kelimeler

• Fotovoltaik Sistemler
• Maksimum Güç Noktası Takibi
• Parçacık Sürüsü Optimizasyonu
• Yenilenebilir Enerji
• Enerji Verimliliği

Maximum Power Point Tracking and Efficiency Increase in Photovoltaic Systems with Particle Swarm Optimization

Öz

In line with the growing global energy demand and environmental sustainability goals, interest in renewable energy sources has been increasing rapidly. Among these sources, photovoltaic (PV) systems have emerged as a significant alternative due to their ability to generate electricity directly from solar energy and their environmentally friendly nature. However, the output power of photovoltaic panels fluctuates continuously depending on factors such as solar irradiance, ambient temperature, and load conditions, which in turn leads to variations in system efficiency. Therefore, operating the system at the Maximum Power Point (MPP) at all times is essential for optimizing total energy production. In this study, a Particle Swarm Optimization (PSO)-based Maximum Power Point Tracking (MPPT) algorithm is proposed to accurately, rapidly, and stably track the maximum power point in photovoltaic systems. PSO, a nature-inspired metaheuristic algorithm, offers higher tracking accuracy, faster response time, and improved stability compared to conventional MPPT techniques. Simulation studies conducted in the MATLAB/Simulink environment demonstrate that the PSO-based approach performs effectively under varying environmental conditions and significantly enhances the energy efficiency of photovoltaic systems. The results indicate that the PSO algorithm can adapt to the dynamic characteristics of PV systems and serves as an efficient solution for real-time tracking applications.

Anahtar Kelimeler

• Photovoltaic Systems
• Maximum Power Point Tracking
• Particle Swarm Optimization
• Renewable Energy;
• Energy Efficiency

Kaynakça

1. Adinoyi, M. J., & Said, S. A. M. (2013). Effect of dust accumulation on the power outputs of solar photovoltaic modules. *Renewable Energy, 60*, 633–636. https://doi.org/10.1016/j.renene.2013.06.014
2. Al-Rashidi, M. R., & El-Hawary, M. E. (2009). Applications of computational intelligence techniques for solving the revived optimal power flow problem. *Electric Power Systems Research, 79*(4), 694–702. https://doi.org/10.1016/j.epsr.2008.08.004
3. Bansal, J. C., Sharma, H., & Arya, K. V. (2014). Particle swarm optimization: Method, variants and applications. *International Journal of Computer Applications, 98*(6), 38–45. https://doi.org/10.5120/17294-7430
4. Benyoucef, M. E. H., & Benbouzid, M. F. (2010). A novel maximum power point tracking control based on particle swarm optimization for photovoltaic systems. *IEEE Transactions on Industrial Electronics, 57*(5), 1637–1645. https://doi.org/10.1109/TIE.2009.2027922
5. Carpenter, P., Snyman, D., & Wills, R. (2012). Off-grid solar PV systems for rural electrification in South Africa. *Journal of Energy in Southern Africa, 23*(1), 1–9.
6. Duffie, J. A., & Beckman, W. A. (2013). *Solar engineering of thermal processes* (4th ed.). Wiley.
7. Dursun, B., & Kurak, E. (2016). Design and implementation of maximum power point tracker in photovoltaic systems.* Duzce University Science and Technology Journal, 4*(1), 581–592.
8. Engelbrecht, A. P. (2005). *Fundamentals of computational swarm intelligence*. John Wiley & Sons.

9. Esram, T., & Chapman, P. L. (2007). Comparison of photovoltaic array maximum power point tracking techniques. *IEEE Transactions on Energy Conversion, 22*(2), 439–449. https://doi.org/10.1109/TEC.2006.874230
10. Femia, N., Lisi, S., Petrone, G., Spagnuolo, G., & Vitelli, M. (2005). Optimization of perturb and observe maximum power point tracking method. *IEEE Transactions on Power Electronics, 20*(4), 963–973. https://doi.org/10.1109/TPEL.2005.850975
11. Gonzalez, A., Rodriguez, J., & Gubia, E. (2014). Particle swarm optimization based maximum power point tracking for photovoltaic systems. *IEEE Transactions on Industrial Electronics, 61*(12), 6735–6742. https://doi.org/10.1109/TIE.2014.2316223
12. Green, M. A., Emery, K., Hishikawa, Y., & Warta, W. (2015). Solar cell efficiency tables (version 45). *Progress in Photovoltaics: Research and Applications, 23*(1), 1–9. https://doi.org/10.1002/pip.2573
13. Hohm, D. P., & Ropp, M. E. (2003). Comparative study of maximum power point tracking algorithms. *Progress in Photovoltaics: Research and Applications, 11*(1), 47–62. https://doi.org/10.1002/pip.459
14. Hussain, A., & Asghar, M. (2017). Maximum power point tracking of photovoltaic systems using particle swarm optimization. *Energy Reports, 3*, 1–9. https://doi.org/10.1016/j.egyr.2016.11.002
15. International Energy Agency Photovoltaic Power Systems Programme (IEA PVPS). (2020). *Trends in photovoltaic applications 2020*.
16. Jordan, D. C., & Kurtz, S. R. (2013). Photovoltaic degradation rates An analytical review. *Progress in Photovoltaics: Research and Applications, 21*(1), 12–29. https://doi.org/10.1002/pip.1182
17. Jordehi, A. R. (2015). Particle swarm optimization for photovoltaic maximum power point tracking. *Renewable and Sustainable Energy Reviews, 50*, 1336–1346. https://doi.org/10.1016/j.rser.2015.05.054
18. Jordehi, A. R., & Jovanovic, M. (2013). Particle swarm optimization for maximum power point tracking in photovoltaic systems. *Renewable Energy, 50*, 210–216. https://doi.org/10.1016/j.renene.2012.06.028
19. Kalogirou, S. A. (2009). *Solar energy engineering: Processes and systems*. Academic Press.
20. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. *Proceedings of the IEEE International Conference on Neural Networks* (Vol. 4, pp. 1942–1948). https://doi.org/10.1109/ICNN.1995.488968
21. Luthander, R., Widéna, J., Nilssonb, D., & Palm, J. (2015). Photovoltaic self-consumption in buildings: A review. *Applied Energy, 142*, 80–94. http://dx.doi.org/10.1016/j.apenergy.2014.12.028
22. Meziane, F., & El-Amine, B. (2021). Improved particle swarm optimization algorithm and its application to MPPT control for PV systems under partial shading conditions. *Solar Energy, 221*, 34–46. https://doi.org/10.1016/j.solener.2021.04.016
23. Mohanty, S., Subudhi, B., & Ray, K. P. (2017). A grey wolf assisted perturb and observe MPPT algorithm for a PV system. *IEEE Transactions on Energy Conversion, 32*(1), 340–347. https://doi.org/10.1109/TEC.2016.2627540
24. Poli, R., Kennedy, J., & Blackwell, T. (2007). Particle swarm optimization. *Swarm Intelligence, 1*(1), 33–57. https://doi.org/10.1007/s11721-007-0002-0
25. Yasko, M. A. (2018). *Fotovoltaik sistemlerde düşürücü tip DA-DA dönüştürücülü MGNİ'nin gerçekleştirilmesi* [Yüksek lisans tezi, Kocaeli Üniversitesi].
26. Zhang, W., & Chen, Q. (2016). Review of maximum power point tracking algorithms in photovoltaic systems. *Renewable and Sustainable Energy Reviews, 14*, 1–9. https://doi.org/10.1016/j.rser.2016.01.009