Fotovoltaik Sistemler için Maksimum Güç Noktasının İzlenmesinde Değiştir ve Gözlemle (P&O) ve Artan İletkenlik Algoritmaların (InC) Deneysel Analizi

Year 2024, Issue: 53, 140 - 149, 15.02.2024

Mustafa Şeker , Talha Tan , Sinem Melike Turan

Abstract

Fotovoltaik (PV) sistemler ucuz, kurulabilir alanlarının fazla olması, çevre dostu ve enerji verimliliği gibi doğal avantajları nedeniyle dünyadaki en önemli enerji kaynaklarından biri olarak kabul edilmektedir. Güneş PV sisteminin çıkış gücünü artırmak için, mümkün olan en yüksek maksimum güç noktası izleme (MPPT) algoritması, fotovoltaik (PV) üretim sisteminin performansını optimize etmede önemli bir rol oynar. Bu makalede yaygın olarak kullanılan Değiştir & Gözlemle (Perturb and Observe (P&O)) yöntemini ve Artan İletkenlik (Incremental Conductance (IC)) yöntemlerinin hem deneysel hemde Matlab/Simulink benzetim modeli kullanılarak kapsamlı bir değerlendirilmesi sunulmuştur. Deneysel çalışmalar Arduino Uno kullanılarak 1 Khz PWM çıkışı ile gerçekleştirilmiştir. Konvertör yapısı olarak Boost konvertör kullanılmıştır. Elde edilen deneysel sonuçlar InC yönteminin ani değişen ışınım altında P&O algoritmasına göre daha iyi yakınsama ve daha yüksek doğrulukla maksimum güç noktasını tespit edebildiğini göstermektedir.

Keywords

Maksimum Güç Noktası İzleme, Artan İletkenlik, Değiştir Gözlemle, Güneş Enerjisi

Supporting Institution

Cumhuriyet Üniversitesi Bilimsel Araştırma Projeleri Birimi (CUBAP)

Project Number

HAMYO-003

Thanks

Bu çalışmada yazarlar Cumhuriyet Üniversitesi Bilimsel Araştırma Projeleri Birimi (CUBAP) tarafından HAMYO-003 numaralı proje numarası ve TÜBİTAK Bilim İnsanı Destek Programları Başkanlığı (BİDEB) tarafından yürütülen, 2209-A Üniversite Öğrencileri Araştırma Projeleri Destekleme Programı 2021 yılı 2 dönem kapsamında 1919B012111890 numaralı proje ile çalışmayı destekleyen kuruluşlara teşekkür etmektedir.

Experimental Analysis of Perturb and Observe (P&O) and Incremental Conductance (InC) Algorithms for Maximum Power Point Tracking in Photovoltaic Systems

Abstract

Photovoltaic (PV) systems are considered as one of the most important energy sources in the world due to their inherent advantages such as cheap, large installable area, environmental friendliness and energy efficiency. To increase the output power of the solar PV system, the maximum possible maximum power point tracking (MPPT) algorithm plays an important role in optimizing the performance of the photovoltaic (PV) generation system. This paper presents a comprehensive evaluation of the widely used Perturb and Observe (P&O) and Incremental Conductance (IC) methods using both experimental and Matlab/Simulink simulation models. Experimental studies were carried out using Arduino Uno board with 1 Khz PWM output. Boost converter was used as the converter structure. The experimental results show that the InC method is able to detect the maximum power point with better convergence and higher accuracy than the P&O algorithm under sudden changing irradiance.

Keywords

Maximum Power Point Tracking, Increased Conductivity, Change Observe, Solar Power

Project Number

HAMYO-003

References

• Abdulkadir, M., & Yatim, A. H. M. (2014). Hybrid maximum power point tracking technique based on PSO and incremental conductance. 2014 IEEE Conference on Energy Conversion, CENCON 2014, 271–276. https://doi.org/10.1109/CENCON.2014.6967514
• Aygül, K., Cikan, M., Demirdelen, T., & Tumay, M. (2019). Butterfly optimization algorithm based maximum power point tracking of photovoltaic systems under partial shading condition. Energy Sources, Part A: Recovery, Utilization and Environmental Effects, 00(00), 1–19. https://doi.org/10.1080/15567036.2019.1677818
• BADAK, U., & YILDIZ, A. B. (2021). Maksimum Güç Noktası İzleyici Algoritmalarının Verim, Salınım Miktarı ve Yakınsama Süresi Açısından Karşılaştırılması. European Journal of Science and Technology, 21, 463–472. https://doi.org/10.31590/ejosat.822975
• Besheer, A. H., & Adly, M. (2012). Ant colony system based PI maximum power point tracking for stand alone photovoltaic system. 2012 IEEE International Conference on Industrial Technology, ICIT 2012, Proceedings, 693–698. https://doi.org/10.1109/ICIT.2012.6210019
• Eltawil, M. A., & Zhao, Z. (2013). MPPT techniques for photovoltaic applications. Renewable and Sustainable Energy Reviews, 25, 793–813. https://doi.org/10.1016/j.rser.2013.05.022
• Femia, N., 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
• Ishaque, K., Salam, Z., & Lauss, G. (2014). The performance of perturb and observe and incremental conductance maximum power point tracking method under dynamic weather conditions. Applied Energy, 119, 228–236. https://doi.org/10.1016/j.apenergy.2013.12.054
• Kobayashi, K., Takano, I., & Sawada, Y. (2006). A study of a two stage maximum power point tracking control of a photovoltaic system under partially shaded insolation conditions. Solar Energy Materials and Solar Cells, 90(18–19), 2975–2988. https://doi.org/10.1016/j.solmat.2006.06.050
• Morales-Acevedo, A., Diaz-Bernabe, J. L., & Garrido-Moctezuma, R. (2014). Improved MPPT adaptive incremental conductance algorithm. IECON Proceedings (Industrial Electronics Conference), 5540–5545. https://doi.org/10.1109/IECON.2014.7049347
• Paraskevadaki, E. V., & Papathanassiou, S. A. (2011). Evaluation of MPP voltage and power of mc-Si PV modules in partial shading conditions. IEEE Transactions on Energy Conversion, 26(3), 923–932. https://doi.org/10.1109/TEC.2011.2126021
• Radjai, T., Rahmani, L., Mekhilef, S., & Gaubert, J. P. (2014). Implementation of a modified incremental conductance MPPT algorithm with direct control based on a fuzzy duty cycle change estimator using dSPACE. Solar Energy, 110, 325–337. https://doi.org/10.1016/j.solener.2014.09.014
• Rajiv Roshan; Yatendra Yadav; S Umashankar; D Vijayakumar; D P Kothari. (n.d.). Modeling and simulation of Incremental conductance MPPT algorithm based solar Photo Voltaic system using CUK converter.
• Rutkowski, L. (2008). Computational intelligence: Methods and techniques. In Computational Intelligence: Methods and Techniques (1st ed.). Berlin: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-76288-1
• Salam, Z., Ahmed, J., & Merugu, B. S. (2013). The application of soft computing methods for MPPT of PV system: A technological and status review. Applied Energy, 107, 135–148. https://doi.org/10.1016/j.apenergy.2013.02.008
• Sun, L., Wang, J., & Tang, L. (2021). A Powerful Bio-Inspired Optimization Algorithm Based PV Cells Diode Models Parameter Estimation. Frontiers in Energy Research, 9(April), 1–15. https://doi.org/10.3389/fenrg.2021.675925
• Sundareswaran, K., Vignesh kumar, V., & Palani, S. (2015). Application of a combined particle swarm optimization and perturb and observe method for MPPT in PV systems under partial shading conditions. Renewable Energy, 75, 308–317. https://doi.org/10.1016/j.renene.2014.09.044
• Tan, Y. T., Kirschen, D. S., & Jenkins, N. (2004). A model of PV generation suitable for stability analysis. IEEE Transactions on Energy Conversion, 19(4), 748–755. https://doi.org/10.1109/TEC.2004.827707
• Tekeshwar Prasad Sahu and T. V. Dixit. (2014). No Title. Modelling and Analysis of Perturb & Observe and Incremental Conductance MPPT Algorithm for PV Array Using Ċuk Converter,.
• Xiao, W., & Dunford, W. G. (2004). A modified adaptive hill climbing MPPT method for photovoltaic power systems. PESC Record - IEEE Annual Power Electronics Specialists Conference, 3, 1957–1963. https://doi.org/10.1109/PESC.2004.1355417
• Zaki Diab, A. A., & Rezk, H. (2017). Global MPPT based on flower pollination and differential evolution algorithms to mitigate partial shading in building integrated PV system. Solar Energy, 157, 171–186. https://doi.org/10.1016/j.solener.2017.08.024