PV PANELİNE BAĞLI BİR BUCK-BOOST DÖNÜŞTÜRÜCÜ İÇİN METAHÖRSTİK ALGORİTMALAR KULLANILARAK MPPT VE KONTROLÖR TASARIMI

Yıl 2025, Cilt: 6 Sayı: 2, 110 - 126, 18.12.2025

Murat Erhan Çimen , Ali Kuyumcu , Kadir Yasin Sunca , Yaprak Yalçın , Akif Akgül , Ali Fuat Boz

https://doi.org/10.58769/joinssr.1792463

Öz

Son yıllarda artan enerji talebi ve fosil yakıtların tükenmesi araştırmacıları yenilenebilir enerji kaynaklarına odaklanmaya yöneltmiştir. Bu bağlamda, genellikle güneş enerjisi üretimi için kullanılan fotovoltaik (PV) panellere bağlı DC-DC güç dönüştürücülerinin verimli kontrolü giderek daha önemli hale gelmiştir. Bu çalışmada, bir PV paneline bağlı bir Buck-Boost dönüştürücüyü en iyi şekilde kontrol etmek için yakın zamanda geliştirilen Yapay Sinekkuşu Algoritması (AHA) ve Çita Optimizasyon Algoritması (CO) kullanılmıştır. Hem AHA hem de COA, değişen sıcaklık ve ışınım koşulları altında PV panelinin maksimum güç noktasını (MPPT) izleyerek Zaman Ağırlıklı Karesel Hatanın İntegrali (ITSE) kriterine dayalı kontrol performansını artırmak için uygulanır. Algoritma, dönüştürücüde kullanılan Oransal-İntegral (PI) kontrolcünün parametrelerini optimize ederek sistemin dinamik tepkisini iyileştirir. Bulgular, önerilen yaklaşımın daha hızlı, daha kararlı ve daha doğru bir performans sağladığını göstermektedir.

Anahtar Kelimeler

Metasezgisel Algoritmalar , PV Panel , Buck-Boost Dönüştürücü , MPPT.

Proje Numarası

124E782

MPPT AND CONTROLLER DESIGN USING METAHEURISTIC ALGORITHMS FOR A BUCK-BOOST CONVERTER CONNECTED TO A PV PANEL

Öz

In recent years, the increasing energy demand and the depletion of fossil fuels have driven researchers to focus on renewable energy sources. In this context, the efficient control of DC-DC power converters connected to photovoltaic (PV) panels—commonly used for solar energy generation—has become increasingly important. This study employs the recently developed Artificial Hummingbird Algorithm (AHA) and the Cheetah Optimization Algorithm (CO) to optimally control a Buck-Boost converter coupled with a PV panel. Both AHA and COA are applied to enhance control performance based on the Integral of Time-Weighted Squared Error (ITSE) criterion by tracking the maximum power point (MPPT) of the PV panel under varying temperature and irradiance conditions. The algorithm optimizes the parameters of the Proportional–Integral (PI) controller used in the converter, thereby improving the system’s dynamic response. The findings demonstrate that the proposed approach delivers faster, more stable, and more accurate performance.

Anahtar Kelimeler

Metaheuristic Algorithms , PV Panel , Buck-Boost Converter , MPPT.

Destekleyen Kurum

Technological Research Council of Türkiye (TUBITAK), Power Electronics Technologies Research and Application Center (PETEC)

Proje Numarası

124E782

Teşekkür

This study was supported by the Scientific and Technological Research Council of Türkiye (TUBITAK) Research Fund, under the Grant Number 124E782. The authors thank to TUBITAK for their supports, and the Power Electronics Technologies Research and Application Center (PETEC), Sakarya, Türkiye.

Kaynakça

• Akgul, A., Karaca, Y., Pala, M. A., Cimen, M. E., Boz, A. F., & Yildiz, M. Z. (2024). Chaos theory, advanced metaheuristic algorithms and their newfangled deep learning architecture optimization applications: A review. Fractals, 32(03), 2430001.
• Akbari, M. A., Zare, M., Azizipanah-Abarghooee, R., Mirjalili, S., & Deriche, M. (2022). The cheetah optimizer: A nature-inspired metaheuristic algorithm for large-scale optimization problems. Scientific Reports, 12(1), 10953.
• Alhumade, H., Houssein, E. H., Rezk, H., Moujdin, I. A., & Al-Shahrani, S. (2023). Modified artificial hummingbird algorithm-based single-sensor global MPPT for photovoltaic systems. Mathematics, 11(4), 979.
• Atallah, A. M., Abdelaziz, A. Y., & Jumaah, R. S. (2014). Implementation of perturb and observe MPPT of PV system with direct control method using buck and buck-boost converters. Emerging Trends in Electrical, Electronics & Instrumentation Engineering: An International Journal (EEIEJ), 1(1), 31-44.
• Badis, A., Mansouri, M. N., & Boujmil, M. H. (2017, May). A genetic algorithm optimized MPPT controller for a PV system with DC-DC boost converter. In 2017 International Conference on Engineering & MIS (ICEMIS) (pp. 1-6). IEEE.
• Banakhr, F. A., & Mosaad, M. I. (2021). High performance adaptive maximum power point tracking technique for off-grid photovoltaic systems. Scientific Reports, 11(1), 20400.
• Brahim, B., Abdelkader, H., Djamel, B., Fadila, T., & Fatiha, B. (2021). Optimal PI Parameters Tuning for a DC-DC Boost Converter. Algerian Journal of Renewable Energy and Sustainable Development, 3(2), 223-229.
• Chiang, S. J., Shieh, H. J., & Chen, M. C. (2008). Modeling and control of PV charger system with SEPIC converter. IEEE Transactions on Industrial Electronics, 56(11), 4344-4353.
• Çimen, M. E., & Yalçın, Y. (2022). A novel hybrid firefly–whale optimization algorithm and its application to optimization of MPC parameters. Soft Computing, 26(4), 1845-1872.
• Darwish, A., Holliday, D., Ahmed, S., Massoud, A. M., & Williams, B. W. (2014). A single-stage three-phase inverter based on Cuk converters for PV applications. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2(4), 797-807.
• Dada, M., & Popoola, P. (2023). Recent advances in solar photovoltaic materials and systems for energy storage applications: A review. Beni-Suef University Journal of Basic and Applied Sciences, 12(1), 1-15.
• Elbaksawi, O., Elminshawy, N. A., Diab, S., Eltamaly, A. M., Mahmoud, A., & Elhadidy, H. (2024). Innovative metaheuristic algorithm with comparative analysis of MPPT for 5.5 kW floating photovoltaic system. Process Safety and Environmental Protection, 185, 1072-1088.
• Eltamaly, A. M. (2021). A novel musical chairs algorithm applied for MPPT of PV systems. Renewable and Sustainable Energy Reviews, 146, 111135.
• Farahat, M. A., Metwally, H. M. B., & Mohamed, A. A. E. (2012). Optimal choice and design of different topologies of DC–DC converter used in PV systems, at different climatic conditions in Egypt. Renewable Energy, 43, 393-402.
• Guerra, M. I., Ugulino de Araújo, F. M., Dhimish, M., & Vieira, R. G. (2021). Assessing maximum power point tracking intelligent techniques on a PV system with a buck–boost converter. Energies, 14(22), 7453.
• Hassan, Q., Viktor, P., Al-Musawi, T. J., Ali, B. M., Algburi, S., Alzoubi, H. M., … & Jaszczur, M. (2024). The renewable energy role in the global energy transformations. Renewable Energy Focus, 48, 100545.
• Koad, R. B., Zobaa, A. F., & El-Shahat, A. (2016). A novel MPPT algorithm based on particle swarm optimization for photovoltaic systems. IEEE Transactions on Sustainable Energy, 8(2), 468-476.
• Mühürcü, G., Kose, E., Muhurcu, A., & Kuyumcu, A. (2017, September). Parameter optimization of PI controller by PSO for optimal controlling of a buck converter's output. In 2017 International Artificial Intelligence and Data Processing Symposium (IDAP) (pp. 1-6). IEEE.
• Pakkiraiah, B., & Sukumar, G. D. (2016). Research survey on various MPPT performance issues to improve the solar PV system efficiency. Journal of Solar Energy, 2016(1), 8012432.
• Suganya, R., Rajkumar, M. V., & Pushparani, P. (2017). Simulation and analysis of boost converter with MPPT for PV system using chaos PSO algorithm. Int. J. Emerg. Technol. Eng. Res, 5, 97-105.
• Sunca, K. Y., & Boz, A. F. (2025). Artificial hummingbird algorithm-based PID controller for DC motor speed control. Politeknik Dergisi, 1-1. https://doi.org/10.2339/politeknik.1687239.
• Sunca, K. Y., & Koçkanat, S. (2024). ÇİTA OPTİMİZASYON ALGORİTMASI KULLANARAK İVMEÖLÇER SİNYALLERİNİN HARMONİK ANALİZİ. Sivas Cumhuriyet Üniversitesi Mühendislik Fakültesi Dergisi, 2(2), 51-57.