Müstakil çalışan PV Sistem için Yükselten Tip Dönüştürücü topolojisine sahip Yapay Sinir Ağı tabanlı MPPT Algoritması

Year 2022, , 242 - 257, 27.03.2022

Mehmet Yılmaz , Muhammedfatih Corapsiz

https://doi.org/10.18185/erzifbed.1002823

Cited By: 1

Abstract

Teknolojinin gelişmesine paralel olarak artan enerji ihtiyacı ve kaynakların tükenmesi, alternatif enerji kaynaklarının önemini artırmıştır. Güneş enerjisi sistemleri hareketli parça olmaması, güvenilir olması ve gürültüsüz çalışması gibi avantajları nedeniyle sıklıkla tercih edilmektedir. Güneş enerjisinden elektrik üretimi, istenilen gerilim ve akım değerlerine bağlı olarak fotovoltaik (PV) panellerin seri veya paralel bağlanması ile elde edilmektedir. PV panellerden elde edilen enerjiyi arzu edilen şebeke değerlerine dönüştürmek amacıyla DC-DC dönüştürücüler kullanılmaktadır. PV panellerden mümkün olan en yüksek verimi elde etmek için maksimum güç noktası izleme (MPPT) algoritmaları kullanılmaktadır. MPPT algoritmaları DC-DC dönüştürücülerin görev periyodu (D) oranını kontrol edip maksimum enerji elde etmektedirler. Bu çalışmada, Yapay Sinir Ağı (YSA) tabanlı bir MPPT algoritması önerilmiştir İlk olarak PV panel girişindeki sıcaklık ve ışınım verileri Levenberg-Marquardt algoritması kullanılarak eğitilmiştir Sonuç olarak, bir referans voltajı üretilir ve PV panel tarafından üretilen voltaj ile karşılaştırılarak MPPT gerçekleştirilmektedir. Önerilen algoritmanın performansını değerlendirmek için geleneksel MPPT yöntemlerinden Değiştir & Gözle (P&O) ve Artırılmış iletkenlik (INC) ile karşılaştırılmıştır. Benzetim çalışmaları sonucunda YSA tabanlı MPPT’nin çeşitli ışınım ve sıcaklık koşulları için P&O ve INC algoritmalarından daha başarılı olduğu gözlemlenmiştir.

Keywords

Yapay sinir ağı, maksimum güç noktası takibi, DC-DC dönüştürücü, artırılmış iletkenlik algoritması, değiştir & gözle algoritması

Artificial Neural Network based MPPT Algorithm with Boost Converter topology for Stand-Alone PV System

Abstract

The increasing energy need in parallel with the technology development and the depletion of the resources have increased the importance of alternative energy resources. Solar energy systems are frequently preferred due to their advantages such as not having moving parts, being reliable and working without noise. Production of electricity from solar energy is obtained by serial or parallel connection of photovoltaic (PV) panels, depending on the desired voltage and current values. DC-DC converters are used to convert the energy obtained from the PV panels to the desired grid values. Maximum power point tracking (MPPT) algorithms are used in order to obtain the highest possible efficiency from the PV panels. MPPT algorithms control the duty period (D) ratio of DC-DC converters and obtain maximum energy. In this study, an Artificial Neural Network (ANN) based MPPT algorithm is proposed. Firstly, the temperature and irradiance data at the PV panel input are trained using the Levenberg-Marquardt algorithm. As a result, a reference voltage is generated and MPPT is realized by comparing it with the voltage produced by the PV panel. In order to evaluate the performance of the proposed algorithm, it is compared with the traditional MPPT methods such as Perturb & Observe (P&O) and Incremental Conductance (INC). As a result of the simulation studies, it has been observed that ANN based MPPT is more successful than P&O and INC algorithms for several irradiance and temperature conditions.

Keywords

Artificial neural network, maximum power point tracking, DC-DC converter, incremental conductance algorithm, perturb & observe algorithm

References

• Abo-Khalil, A. G., Alharbi, W., Al-Qawasmi, A.-R., Alobaid, M., & Alarifi, I. M. (2021). Maximum Power Point Tracking of PV Systems under Partial Shading Conditions Based on Opposition-Based Learning Firefly Algorithm. Sustainability, 13(5), 2656.
• Avila, L., De Paula, M., Trimboli, M., & Carlucho, I. (2020). Deep reinforcement learning approach for MPPT control of partially shaded PV systems in Smart Grids. Applied Soft Computing, 97, 106711.
• Aydoğan D. (2019). Development and implementation of PSO based maximum power point tracking algorithm. Master dissertation, Institute of Science, Nevşehir Hacı Bektaş Veli Univ., Nevşehir, Turkey.
• Boyar A. (2018). The design and analysis of micro inverter for solar panels. Master dissertation, Institute of Science, Nevşehir Hacı Bektaş Veli Univ., Nevşehir, Turkey.
• Charin, C., Ishak, D., Zainuri, M. A. A. M., Ismail, B., & Jamil, M. K. M. (2021). A hybrid of bio-inspired algorithm based on Levy flight and particle swarm optimizations for photovoltaic system under partial shading conditions. Solar Energy, 217, 1-14.
• Çelikel, R., & Gündoğdu, A. (2020). ANN-Based MPPT Algorithm for Photovoltaic Systems. Turkish Journal of Science and Technology, 15(2), 101-110.
• Danandeh, M. (2018). Comparative and comprehensive review of maximum power point tracking methods for PV cells. Renewable and Sustainable Energy Reviews, 82, 2743-2767.
• Divyasharon, R., Banu, R. N., & Devaraj, D. (2019). Artificial neural network based MPPT with CUK converter topology for PV systems under varying climatic conditions. 2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS).
• Dorji, S., Wangchuk, D., Choden, T., & Tshewang, T. (2020). Maximum power point tracking of solar photovoltaic cell using perturb & observe and fuzzy logic controller algorithm for boost converter and quadratic boost converter. Materials Today: Proceedings, 27, 1224-1229.
• Eltamaly, A. M. (2021). An Improved Cuckoo Search Algorithm for Maximum Power Point Tracking of Photovoltaic Systems under Partial Shading Conditions. Energies, 14(4), 953.
• Fares, D., Fathi, M., Shams, I., & Mekhilef, S. (2021). A novel global MPPT technique based on squirrel search algorithm for PV module under partial shading conditions. Energy Conversion and Management, 230, 113773.
• Irmak, E., & Güler, N. (2020). A model predictive control-based hybrid MPPT method for boost converters. International Journal of Electronics, 107(1), 1-16.
• Javed, S., Ishaque, K., Siddique, S. A., & Salam, Z. (2021). A Simple yet Fully Adaptive PSO Algorithm for Global Peak Tracking of Photovoltaic Array Under Partial Shading Conditions. IEEE Transactions on Industrial Electronics.
• Mohamed, A.-E., Marei, M. I., & El-khattam, W. (2018). A maximum power point tracking technique for PV under partial shading condition. 2018 8th IEEE India International Conference on Power Electronics (IICPE).
• Samani, L., & Mirzaei, R. (2021). Maximum power point tracking for photovoltaic systems under partial shading conditions via modified model predictive control. Electrical Engineering, 1-25.
• Shahid, H., Kamran, M., Mehmood, Z., Saleem, M. Y., Mudassar, M., & Haider, K. (2018). Implementation of the novel temperature controller and incremental conductance MPPT algorithm for indoor photovoltaic system. Solar Energy, 163, 235-242.
• Srinivasan, S., Tiwari, R., Krishnamoorthy, M., Lalitha, M. P., & Raj, K. K. (2021). Neural network based MPPT control with reconfigured quadratic boost converter for fuel cell application. International Journal of Hydrogen Energy, 46(9), 6709-6719.
• Tang, L., Wang, X., Xu, W., Mu, C., & Zhao, B. (2021). Maximum power point tracking strategy for photovoltaic system based on fuzzy information diffusion under partial shading conditions. Solar Energy, 220, 523-534.
• Thueanpangthaim, C., Wongyai, P., Areerak, K., & Areerak, K. (2017). The maximum power point tracking for stand-alone photovoltaic system using current based approach. 2017 International electrical engineering congress (iEECON).
• Yılmaz, M., & Çorapsız, M.F. (2021). Adaptive-Network-based Fuzzy Inference System based MPPT Control for Stand-Alone PV Systems. Internatıonal Symposıum On Applıed Scıences And Engıneerıng (ISASE2021).
• Zafar, M. H., Khan, N. M., Mirza, A. F., & Mansoor, M. (2021). Bio-inspired optimization algorithms based maximum power point tracking technique for photovoltaic systems under partial shading and complex partial shading conditions. Journal of Cleaner Production, 309, 127279.