SavedAdd your journal

Journals / International Journal of Multidisciplinary Studies and Innovative Technologies / 2026 / Cilt: 10 Sayı: 1

Adaptive Control-Based Reconfigurable Boost Converter Architecture for Photovoltaic Power Conversion Under Variable Conditions

Değişken Koşullar Altında Fotovoltaik Güç Dönüşümü İçin Adaptif Kontrol Tabanlı Yeniden Yapılandırılabilir Boost Dönüştürücü Mimarisi

Burak Kara(TOKAT GAZİOSMANPAŞA ÜNİVERSİTESİ, TOKAT MESLEK YÜKSEKOKULU)0000-0002-4453-6515
Cem Emeksiz(TOKAT GAZİOSMANPAŞA ÜNİVERSİTESİ, MÜHENDİSLİK VE MİMARLIK FAKÜLTESİ, ELEKTRİK-ELEKTRONİK MÜHENDİSLİĞİ BÖLÜMÜ, ELEKTRİK-ELEKTRONİK MÜHENDİSLİĞİ PR.)0000-0002-4817-9607
Mehmet Serhat Can(TOKAT GAZİOSMANPAŞA ÜNİVERSİTESİ, MÜHENDİSLİK VE MİMARLIK FAKÜLTESİ, ELEKTRİK-ELEKTRONİK MÜHENDİSLİĞİ BÖLÜMÜ, ELEKTRİK-ELEKTRONİK MÜHENDİSLİĞİ PR.)0000-0003-2356-9921
Pages
9–22
Publication Date
2026-08-31
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Overall, the proposed adaptive converter structure provides a more stable, accurate, and efficient energy conversion compared to traditional designs, significantly enhancing the reliability of PV systems and MPPT performance.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study proposes a time-dependent switched adaptive boost converter architecture to enhance dynamic stability and improve power transfer efficiency in photovoltaic (PV) systems operating under varying irradiance and temperature conditions.
📊
Key Findings & Data:These issues negatively impact both converter performance and the effectiveness of maximum power point tracking (MPPT) algorithms.
💡
Conclusion & Impact:Overall, the proposed adaptive converter structure provides a more stable, accurate, and efficient energy conversion compared to traditional designs, significantly enhancing the reliability of PV systems and MPPT performance.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Fotovoltaik Sistemler · Adaptif Yükselten Dönüştürücü · Maksismum Güç Noktası Takibi

Abstract

This study proposes a time-dependent switched adaptive boost converter architecture to enhance dynamic stability and improve power transfer efficiency in photovoltaic (PV) systems operating under varying irradiance and temperature conditions. Conventional boost converters typically use fixed passive components, which often lead to overshoot, oscillations, and instability during rapid environmental changes. These issues negatively impact both converter performance and the effectiveness of maximum power point tracking (MPPT) algorithms. In the proposed approach, groups of inductors and capacitors are dynamically switched on and off through a time-based control mechanism, allowing real-time reconfiguration of the converter topology. This adaptive configuration introduces an additional energy buffering capability, thereby improving the system’s transient response against sudden irradiance fluctuations. The system was modeled and simulated in the MATLAB Simulink environment and evaluated using Perturb and Observe (P&O), Incremental Conductance (IC), and a hybrid Particle Swarm Optimization–Incremental Conductance (PSO–IC) MPPT algorithms. Comparative results between classical and adaptive converter structures demonstrate that the proposed system achieves lower root mean square error (RMSE), reduced mean absolute percentage error (MAPE), and decreased output power ripple. Among the tested methods, the adaptive converter integrated with the PSO–IC hybrid algorithm exhibited the best performance, achieving a tracking efficiency of 96.56%, faster convergence, and minimized power oscillations. Overall, the proposed adaptive converter structure provides a more stable, accurate, and efficient energy conversion compared to traditional designs, significantly enhancing the reliability of PV systems and MPPT performance. Future work will focus on hardware implementation and evaluation with alternative metaheuristic MPPT techniques.

Özet

Bu çalışma, değişken ışınım ve sıcaklık koşulları altında çalışan fotovoltaik (PV) sistemlerde dinamik kararlılığı artırmak ve güç aktarım verimliliğini iyileştirmek amacıyla zamana bağlı anahtarlamalı adaptif bir yükselten dönüştürücü mimarisi önermektedir. Geleneksel yükselten dönüştürücüler genellikle sabit pasif bileşenler kullanır; bu durum ise hızlı çevresel değişimler sırasında aşım (overshoot), salınım ve kararsızlığa yol açmaktadır. Bu problemler hem dönüştürücü performansını hem de maksimum güç noktası takibi (MPPT) algoritmalarının etkinliğini olumsuz yönde etkilemektedir. Önerilen yaklaşımda, indüktör ve kapasitör grupları zaman tabanlı bir kontrol mekanizması aracılığıyla dinamik olarak devreye alınıp çıkarılmaktadır. Bu sayede dönüştürücü topolojisinin gerçek zamanlı olarak yeniden yapılandırılması mümkün olmaktadır. Bu adaptif yapı, ek bir enerji tamponlama (buffering) kapasitesi sağlayarak ani ışınım değişimlerine karşı sistemin geçici rejim (transient) tepkisini iyileştirmektedir. Sistem, MATLAB Simulink ortamında modellenmiş ve Değiştir-Gözle (Perturb and Observe (P&O)), Artan İletkenlik (Incremental Conductance (IC)) ve Hibrit Parçacık Sürü Optimizasyonu-Artan İletkenlik (Particle Swarm Optimization–Incremental Conductance (PSO–IC)) MPPT algoritmaları kullanılarak değerlendirilmiştir. Klasik ve adaptif dönüştürücü yapılar arasında yapılan karşılaştırmalı analiz sonuçları, önerilen sistemin daha düşük kök ortalama kare hatası (RMSE), daha düşük ortalama mutlak yüzde hata (MAPE) ve daha az çıkış gücü dalgalanması (ripple) sağladığını göstermektedir. Test edilen yöntemler arasında, adaptif dönüştürücü ile entegre edilen PSO–IC hibrit algoritması en iyi performansı sergilemiş; %96.56 izleme verimliliği, daha hızlı yakınsama ve minimum güç salınımları elde edilmiştir. Sonuç olarak, önerilen adaptif dönüştürücü yapısı, geleneksel tasarımlara kıyasla daha kararlı, daha doğru ve daha verimli bir enerji dönüşümü sağlayarak PV sistemlerin güvenilirliğini ve MPPT performansını önemli ölçüde artırmaktadır. Gelecek çalışmalar, donanım uygulamasına ve alternatif meta-sezgisel MPPT teknikleri ile değerlendirmelere odaklanacaktır.

🔖

Cite This Article

Kara, B., Emeksiz, C., Can, M. S. (2026). Adaptive Control-Based Reconfigurable Boost Converter Architecture for Photovoltaic Power Conversion Under Variable Conditions. International Journal of Multidisciplinary Studies and Innovative Technologies, Cilt: 10 Sayı: 1, 9–22. https://doi.org/10.36287/ijmsit.10.1.2

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (42)

  1. T.C. Enerji ve Tabii Kaynaklar Bakanlığı, “Elektrik,” Bilgi Merkezi Enerji, [Online]. Available: https://enerji.gov.tr/bilgi-merkezi-enerji-elektrik. Accessed: Oct. 27, 2025.
  2. K. Javed, H. Ashfaq, and R. Singh, “An Improved MPPT Algorithm to Minimize Transient and Steady State Oscillation Conditions for Small SPV Systems,” International Journal of Renewable Energy Development (IJRED), vol. 7, no. 3, pp. 191–197, 2018. doi: 10.14710/ijred.7.3.191-197.
  3. B. Liu, S. Duan, F. Liu, and Y. Kang, “A variable step size INC MPPT method for PV systems,” IEEE Trans. Ind. Electron., vol. 55, no. 7, pp. 2622–2628, Jul. 2011.
  4. Z. Li, S. Kai, X. Yan, F. Lanlan, and H. Guo, “A modular grid-connected photovoltaic generation system based on DC bus,” IEEE Trans. Power Electron., vol. 26, no. 2, pp. 523–531, Feb. 2011.
  5. J. L. Agorreta, M. Borrega, J. López, and L. Marroyo, “Modeling and control of N-paralleled grid-connected inverters with LCL filter coupled due to grid impedance in PV plants,” IEEE Trans. Power Electron., vol. 26, no. 3, pp. 770–785, Mar. 2011.
  6. Y. Bo, W. Liu, Y. Zhang, and X. He, “Design and analysis of a grid-connected photovoltaic power system,” IEEE Trans. Power Electron., vol. 25, no. 4, pp. 992–1000, Apr. 2010.
  7. A. Saberi, M. Niroomand, and B. M. Dehkordi, “An improved P&O based MPPT for PV systems with reduced steady-state oscillation,” Int. J. Energy Res., vol. 47, no. ?, pp. 1–15, Jul. 2023, doi: 10.1155/2023/4694583.
  8. M. R. Vincheh, A. Kargar, and G. A. Markadeh, “A hybrid control method for maximum power point tracking (MPPT) in photovoltaic systems,” Arabian J. Sci. Eng., vol. 39, pp. 4715–4725, 2014.
  9. G. N. Arpacı, “Design and comparison of Perturb & Observe, Fuzzy Logic and ANFIS based MPPT controller for PV system,” M.S. thesis, Dept. of Electrical and Electronics Eng., Gazi Univ., Ankara, Turkey, 2019.
  10. E. Irmak and N. Güler, “A model predictive control-based hybrid MPPT method for boost converters,” Int. J. Electron., vol. 107, no. 1, pp. 1–16, 2020.
  11. N. Kacimi, A. Idir, S. Grouni, and M. S. Boucherit, “A new combined method for tracking the global maximum power point of photovoltaic systems,” Rev. Roum. Sci. Tech. – Série Électrotech. Énerg., vol. 67, no. 3, pp. 349–354, 2022.
  12. Z. Gümüş and M. Demirtaş, “Fotovoltaik sistemlerde maksimum güç noktası takibinde kullanılan algoritmaların kısmi gölgeleme koşulları altında karşılaştırılması,” Politeknik Dergisi, vol. 24, no. 3, pp. 853–865, 2021.
  13. A. Gil-Velasco and C. Aguilar-Castillo, “A modification of the perturb and observe method to improve the energy harvesting of PV systems under partial shading conditions,” Energies, vol. 14, no. 9, Art. no. 2521, 2021.
  14. A. Baraean, M. Kassas, M. S. Alam, and M. A. Abido, “Hybrid neural network and adaptive terminal sliding mode MPPT controller for partially shaded standalone PV systems,” Arab. J. Sci. Eng., vol. 48, no. 11, pp. 15527–15539, 2023.
  15. R. G. Mohamed, H. M. Hasanien, and M. A. Ebrahim, “Global MPPT controllers for enhancing dynamic performance of photovoltaic systems under partial shading condition,” e-Prime – Adv. Electr. Eng., Electron. Energy, vol. 9, Art. no. 100638, 2024.
  16. N. Benabdallah, B. Belkacem, A. Tahri, H. Benbouhenni, R. Bouddou, S. Heroual, S. Zaidi, Z. M. S. Elbarbary, and S. A. Mohammed, “Enhancing MPPT optimization with hybrid predictive control and adaptive P&O for better efficiency and power quality in PV systems,” Sci. Rep., vol. 15, Art. no. 24559, 2025, doi: 10.1038/s41598-025-10335-0.
  17. J. Prakash and S. K. Sahoo, “Design of soft switching interleaved boost converter for photovoltaic application,” Res. J. Appl. Sci. Eng. Technol., vol. 9, no. 4, pp. 296–308, 2015, doi: 10.19026/rjaset.9.1408.
  18. Y. Amara, “A comparison analysis between a fixed and multi-step size MPPTs for PV system under variable irradiance and real solar curve,” Int. J. Eng. Manag. Res., vol. 12, no. 3, pp. 136–144, Jun. 2022, doi: 10.31033/ijemr.12.3.20.
  19. B. Ge, F. Z. Peng, and Y. Li, “Multilevel converter/inverter topologies and applications,” in Power Electronics for Renewable Energy Systems, Transportation, and Industrial Applications, H. Abu-Rub, M. Malinowski, and K. Al-Haddad, Eds. Hoboken, NJ, USA: Wiley–IEEE Press, 2014, ch. 18, pp. 422–462.
  20. R. Niu, H. Zhang, and J. Song, “Model predictive control of DC–DC boost converter based on generalized proportional integral observer,” Energies, vol. 16, Art. no. 1245, 2023, doi: 10.3390/en16031245.
  21. V. Paduani, L. Song, B. Xu, and N. Lu, “Maximum power reference tracking algorithm for power curtailment of photovoltaic systems,” Proc. IEEE Power & Energy Soc. Gen. Meet., pp. –, 2021, doi: 10.1109/PESGM46819.2021.9638127.
  22. H. Li, Q. Liu, Z. Zhang, X. Jiang, G.Cao and Y. Zhang, “A voltage overshoot design method for DC–DC converter based on state overshoot concept,” in Proc. IECON 2021 – 47th Annual Conf. IEEE Ind. Electron. Soc., Toronto, ON, Canada, 2021, pp. 1–6, doi: 10.1109/IECON48115.2021.9589276.
  23. C. İncebay, “ Fotovoltaik Sistem İçin Bulanık Mantık Destekli Adaptif PID Tabanlı Maksimum Güç Noktası İzleyici Tasarımı”, M.S. thesis, Inst. of Graduate Studies, Dept. of Electrical and Electronics Eng., Tokat Gaziosmanpasa Univ., Tokat, Turkey, 2025.
  24. M. G. Villalva, J. R. Gazoli, and E. R. Filho, “Comprehensive approach to modeling and simulation of photovoltaic arrays,” IEEE Trans. Power Electron., vol. 24, no. 5, pp. 1198–1208, May 2009.
  25. R. Singh, S. Kumar, A. Gehlot, and R. Pachauri, “An imperative role of sun trackers in photovoltaic technology: A review,” Renew. Sustain. Energy Rev., vol. 82, pp. 3263–3278, Aug. 2018, doi: 10.1016/j.rser.2017.10.018.
  26. R. Menak, Design and modeling of a dual-axis solar tracking system, M.S. thesis, Dept. of Electrical and Electronics Eng., Siirt Univ., Siirt, Turkey, 2018.
  27. S. Büyükkelek, Development of a model-based hybrid MPPT method for photovoltaic applications, M.S. thesis, Konya Technical Univ., Konya, Turkey, 2024.
  28. N. Çoruh, T. Erfidan, and S. Ürgün, “Design and implementation of DC-DC boost converter,” Elektrik Mühendisleri Odası (EMO), presented paper, Turkey, [Online]. Available: https://www.emo.org.tr/ekler/22f3e199a63dcb5_ek.pdf
  29. A. Nonaka, “Boost converter inductor sizing effects on the performance of MPPT algorithms,” M.S. thesis, Dept. of Electrical Engineering, California Polytechnic State Univ., San Luis Obispo, CA, USA, Aug. 2020.
  30. M. Yılmaz, “ Güneş Enerji Sistemleri İçin Yeni Bir Hibrit MPPT Yönteminin Geliştirilmesi ve Gerçek Zamanlı Uygulaması”, Ph.D. dissertation, Dept. of Electrical and Electronics Engineering, Graduate School of Natural Sciences, Atatürk University, Erzurum, Türkiye, 2024.
  31. T. Şahin, “ Fotovoltaik Sistemlerde Kısmi Gölgelenme Durumları İçin Hibrit Maksimum Güç Noktası İzleyicisi Tasarımı”, M.Sc. thesis, Dept. of Electrical and Electronics Engineering, Institute of Science, Sakarya University, Sakarya, Türkiye, Jan. 2025.
  32. R. B. Bollipo, S. Mikkili, and P. K. Bonthagorla, “Hybrid, optimal, intelligent and classical PV MPPT techniques: A review,” CSEE Journal of Power and Energy Systems, vol. 7, no. 1, pp. 9–33, 2020, doi: 10.17775/CSEEJPES.2020.00440.
  33. A. Harrag and S. Messalti, “Variable step size modified P&O MPPT algorithm using GA-based hybrid offline/online PID controller,” Renewable and Sustainable Energy Reviews, vol. 49, pp. 1247–1260, 2015.
  34. H. H. Mousa, A. R. Youssef, and E. E. Mohamed, “State of the art perturb and observe MPPT algorithms based wind energy conversion systems: A technology review,” International Journal of Electrical Power & Energy Systems, vol. 126, p. 106598, 2021.
  35. K. H. Hussein, I. Muta, T. Hoshino, and M. Osakada, “Maximum photovoltaic power tracking: an algorithm for rapidly changing atmospheric conditions,” IEE Proceedings - Generation, Transmission and Distribution, vol. 142, no. 1, pp. 59–64, 1995.
  36. J. Kennedy and R. Eberhart, “Particle swarm optimization,” Proc. IEEE Int. Conf. Neural Networks (ICNN'95), Perth, Australia, vol. 4, pp. 1942–1948, 1995, doi: 10.1109/ICNN.1995.488968.
  37. A. L. M. Leopoldino, C. M. Freitas, and L. F. C. Monteiro, "Analysis of the Hybrid PSO-InC MPPT for Different Partial Shading Conditions," Advances in Electrical and Computer Engineering, vol. 22, no. 2, pp. 29-36, 2022, doi: 10.4316/AECE.2022.02004.
  38. E. W. Mukti, A. Risdiyanto, A. A. Kristi, and R. Darussalam, "Particle Swarm Optimization (PSO) based Photovoltaic MPPT Algorithm under Partial Shading Condition," Jurnal Elektronika dan Telekomunikasi (JET), vol. 23, no. 2, pp. 99-107, Dec. 2023, doi: 10.55981/jet.552.
  39. 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 Journal of Photovoltaics, vol. 4, no. 2, pp. 626-633, March 2014, doi: 10.1109/JPHOTOV.2013.2297513.
  40. A. Chaturvedi, “Performance Evaluation of MPPT Algorithms on Solar PV System for Asynchronous Loads,” M.Tech thesis, Dept. of Electrical and Electronics Engineering, University of Petroleum and Energy Studies, Dehradun, India, 2022.
  41. C. L. S. Srinivas and S. E., “A Maximum Power Point Tracking Technique Based on Ripple Correlation Control for Single Phase Photovoltaic System with Fuzzy Logic Controller,” Energy Procedia, vol. 90, pp. 69–77, 2016.
  42. S. Manna, A. K. Akella, and D. K. Singh, “Novel Lyapunov-based rapid and ripple-free MPPT using a robust model reference adaptive controller for solar PV system,” Protection and Control of Modern Power Systems, vol. 8, no. 13, 2023.