Comparison of PO and INC MPPT Methods Using FPGA In-The-Loop Under Different Radiation Conditions

Yıl 2021, Cilt: 9 Sayı: 2, 114 - 122, 30.04.2021

Reşat Çelikel , Ahmet Gündoğdu

https://doi.org/10.17694/bajece.884815

Cited By: 9

Öz

The Maximum Power Point Tracking (MPPT) algorithms are applied to obtain maximum efficiency under different atmospheric conditions in photovoltaic (PV) systems. Perturb&Observe (PO) and Incremental Conductance (INC) methods are the oldest algorithms used among MPPT methods. Field Programmable Gate Arrays (FPGA) are used especially in applications requiring high speed. FPGA in-the-loop feature is used to test algorithms designed in MATLAB/Simulink environment. In this study, PO and INC methods have been designed to work in FPGA environment. Both algorithms have been tested under different radiation conditions using FPGA-in-the-loop feature. The FPGA in-the-loop simulation result of PO and INC methods has been graphically shown. In this study, Altera DE2-115 development board was used to test PO and INC MPPT algorithms. On the other hand, PO and INC methods were synthesized using the Quartus-II program. Comparisons of simplicity of algorithms were made according to synthesis results. Thus, by using the FGPA in-the-loop feature and performing the synthesis process, both the algorithms were tested and the areas covered by the algorithms in the FPGA were compared.

Anahtar Kelimeler

Solar Energy, PV systems, MPPT Algorithms, FPGA, Perturb and Observe, Incremental Conductance, Photovoltaic Energy Conversion

Kaynakça

• C. Larbes, S.M.A. Cheikh, T. Obeidi, A. Zerguerras. “Genetic algorithms optimized fuzzy logic control for the maximum power point tracking in photovoltaic system.” Renew Energy. Vol. 34, 2009, pp 2093-2100.
• R. Celikel, A. Gundogdu. “System identification‐based MPPT algorithm for PV systems under variable atmosphere conditions using current sensorless approach.” International Transactions on Electrical Energy Systems, 2020, e12433.
• K. Ishaque, Z. Salam, G. Lauss. “The performance of perturb and observe and incremental conductance maximum power point tracking method under dynamic weather conditions.” Applied Energy, vol. 119, 2014, pp 228-236.
• A. Gupta, Y. K. Chauhan, R. K. Pachauri. “A comparative investigation of maximum power point tracking methods for solar PV system.” Solar Energy, vol. 136, 2016, pp 236-253.
• X. Li, H. Wen, Y. Hu, Y. Du, Y. Yang. “A comparative study on photovoltaic MPPT algorithms under EN50530 dynamic test procedure.” IEEE Transactions on Power Electronics, vol. 36. 4, 2020, pp 4153-4168.
• A. R. Reisi, M. H. Moradi, S. Jamasb. “Classification and comparison of maximum power point tracking techniques for photovoltaic system. A review.” Renewable and Sustainable Energy Reviews, vol. 19, 2013, pp 433-443.
• M. A. Husain, A. Tariq, S. Hameed, M. S. B. Arif, A. Jain. “Comparative assessment of maximum power point tracking procedures for photovoltaic systems.” Green Energy & Environment, vol. 2. 1, 2017, pp 5-17.
• H. Bounechba, A. Bouzid, K. Nabti, H. Benalla. “Comparison of perturb & observe and fuzzy logic in maximum power point tracker for PV systems.” Energy Procedia, vol. 50. 1, 2014, pp 677-684.
• S. Motahhir, A. El Hammoumi, A. El Ghzizal. “The most used MPPT algorithms: Review and the suitable low-cost embedded board for each algorithm.” Journal of Cleaner Production, vol. 246, 2020, 118983.
• M. E. Başoğlu. “Analyzes of Flyback DC-DC Converter for Submodule Level Maximum Power Point Tracking in Off-grid Photovoltaic Systems.” Balkan Journal of Electrical and Computer Engineering, vol. 7. 3, 2019, pp 269-275.
• A., Harrag, 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, 2015, pp 1247-1260.
• Z. M. Ali, N. V. Quynh, S. Dadfar, H. Nakamura. “Variable step size perturb and observe MPPT controller by applying θ-modified krill herd algorithm-sliding mode controller under partially shaded conditions.” Journal of Cleaner Production, vol. 271, 2020, 122243.
• F. Liu, S. Duan, F. Liu, B. Liu, Y. Kang. “A variable step size INC MPPT method for PV systems.” IEEE Transactions on Industrial Electronics, vol. 55. 7, 2008, pp 2622-2628.
• L. Xu, R. Cheng, J. Yang. “A Modified INC Method for PV String Under Uniform Irradiance and Partially Shaded Conditions.” IEEE Access, vol. 8, 2020, pp 131340-131351.
• V. K. Devi, K. Premkumar, A. B. Beevi, S. Ramaiyer. “A modified Perturb & Observe MPPT technique to tackle steady state and rapidly varying atmospheric conditions.” Solar Energy, vol. 157, 2017, pp 419-426.
• A. Ghamrawi, J. P. Gaubert, D. Mehdi. “A new dual-mode maximum power point tracking algorithm based on the Perturb and Observe algorithm used on solar energy system.” Solar Energy, vol. 174, 2018, pp 508-514.
• S. C. Wang, H. Y. Pai, G. J. Chen, Y. H. Liu. “A Fast and Efficient Maximum Power Tracking Combining Simplified State Estimation With Adaptive Perturb and Observe.” IEEE Access, vol. 8, 2020, pp 155319-155328.
• R. Balasankar, G. T. Arasu, J. C. M. Raj. “A global MPPT technique invoking partitioned estimation and strategic deployment of P&O to tackle partial shading conditions.” Solar Energy, vol. 143, 2017, pp 73-85.
• S. Mohanty, B. Subudhi, P. K. Ray. “A grey wolf-assisted perturb & observe MPPT algorithm for a PV system.” IEEE Transactions on Energy Conversion, vol. 32. 1, 2016, pp 340-347.
• S. Lyden, H. Galligan, M. E. Haque. “A Hybrid Simulated Annealing and Perturb and Observe Maximum Power Point Tracking Method.” IEEE Systems Journal, 2020, DOI: 10.1109/JSYST.2020.3021379.
• S. Bhattacharyya, D. S. K. Patnam, S. Samanta, S. Mishra. “Steady Output and Fast Tracking MPPT (SOFT MPPT) for P&O and InC Algorithms.” IEEE Transactions on Sustainable Energy, vol. 12. 1, 2021, pp 293-302.
• A. Safari, S. Mekhilef. “Simulation and hardware implementation of incremental conductance MPPT with direct control method using cuk converter.” IEEE Transactions on Industrial Electronics, vol. 58. 4, 2010, pp 1154-1161.
• E. Bianconi, J. Calvente, R. Giral, E. Mamarelis, G. Petrone, C. A. Ramos-Paja, M. Vitelli. “Perturb and observe MPPT algorithm with a current controller based on the sliding mode.” International Journal of Electrical Power & Energy Systems, vol. 44. 1, 2013, pp 346-356.
• D. Sera, L. Mathe, T. Kerekes, S. V. Spataru, R. Teodorescu. “On the perturb-and-observe and incremental conductance MPPT methods for PV systems.” IEEE Journal of Photovoltaics, vol. 3. 3, 2013, pp 1070-1078.
• K. Yan, Y. Du, Z. Ren. “MPPT perturbation optimization of photovoltaic power systems based on solar irradiance data classification.” IEEE Transactions on Sustainable Energy, vol. 10. 2, 2018, pp 514-521.
• V. Kumar, M. Singh. “Derated Mode of Power Generation in PV System Using Modified Perturb and Observe MPPT Algorithm.” Journal of Modern Power Systems and Clean Energy, 2020, DOI: 10.35833/MPCE.2019.000258
• S. S. Mohammed, D. Devaraj, T. I. Ahamed. “A novel hybrid maximum power point tracking technique using perturb & observe algorithm and learning automata for solar PV system.” Energy, vol. 112, 2016, pp 1096-1106.
• V. R. Kota, M. N. Bhukya. “A novel linear tangents-based P&O scheme for MPPT of a PV system.” Renewable and Sustainable Energy Reviews, vol. 71, 2017, pp 257-267.
• E. Mamarelis, G. Petrone, G. Spagnuolo. “A two-steps algorithm improving the P&O steady state MPPT efficiency.” Applied Energy, vol. 113, 2014, pp 414-421.
• D. Pilakkat, S. Kanthalakshmi. “Single phase PV system operating under Partially Shaded Conditions with ABC-PO as MPPT algorithm for grid connected applications.” Energy Reports, vol. 6, 2020, pp 1910-1921.
• D. Pilakkat, S. Kanthalakshmi. “An improved P&O algorithm integrated with artificial bee colony for photovoltaic systems under partial shading conditions.” Solar Energy, vol. 178, 2019, pp 37-47.
• Y. Yang, H. Wen. “Adaptive perturb and observe maximum power point tracking with current predictive and decoupled power control for grid-connected photovoltaic inverters.” Journal of Modern Power Systems and Clean Energy, vol. 7. 2, 2019, pp 422-432.
• Y. Yang, F. P. Zhao. “Adaptive perturb and observe MPPT technique for grid-connected photovoltaic inverters.” Procedia Engineering, vol. 23, 2011, pp 468-473.
• J. Ahmed, Z. Salam. “An enhanced adaptive P&O MPPT for fast and efficient tracking under varying environmental conditions.” IEEE Transactions on Sustainable Energy, vol. 9. 3, 2018, pp 1487-1496.
• R. Alik, A. Jusoh. “An enhanced P&O checking algorithm MPPT for high tracking efficiency of partially shaded PV module.” Solar Energy, vol. 163, 2018, pp 570-580.
• J. L. Díaz-Barnabé, A. Morales-Acevedo. “Experimental study of the equivalence of the Adaptive Incremental Conductance (AIC) and the Adaptive Perturb and Observe (APO) algorithms for PV systems maximum power tracking.” IEEE Latin America Transactions, vol. 17. 8, 2019, pp 1237-1243.
• S. Motahhir, A. El Hammoumi, A. El Ghzizal. “Photovoltaic system with quantitative comparative between an improved MPPT and existing INC and P&O methods under fast varying of solar irradiation.” Energy Reports, vol. 4, 2018, pp 341-350.
• A. Loukriz, M. Haddadi, S. Messalti. “Simulation and experimental design of a new advanced variable step size Incremental Conductance MPPT algorithm for PV systems.” ISA transactions, vol. 62, 2016, pp 30-38.
• M. Al-Dhaifallah, A. M. Nassef, H. Rezk, K. S. Nisar. “Optimal parameter design of fractional order control-based INC-MPPT for PV system.” Solar Energy, vol. 159, 2018, 650-664.
• O. Oussalem, M. Kourchi, A. Rachdy, M. Ajaamoum, H. Idadoub, S. Jenkal. “A low cost controller of PV system based on Arduino board and INC algorithm.” Materials Today: Proceedings, vol. 24, 2020, pp 104-109.
• K. Punitha, D. Devaraj, S. Sakthivel. “Artificial neural network based modified incremental conductance algorithm for maximum power point tracking in photovoltaic system under partial shading conditions.” Energy, vol. 62, 2013, pp 330-340.
• M. A. Elgendy, B. Zahawi, D. J. Atkinson. “Assessment of the incremental conductance maximum power point tracking algorithm.” IEEE Transactions on Sustainable Energy, vol. 4. 1, 2012, pp 108-117.
• S. Motahhir, A. Chalh, A. El Ghzizal, A. Derouich. “Development of a low-cost PV system using an improved INC algorithm and a PV panel Proteus model.” Journal of Cleaner Production, vol. 204, 2018, pp 355-365.
• S. Necaibia, M. S. Kelaiaia, H. Labar, A. Necaibia, E. D. Castronuovo. “Enhanced auto-scaling incremental conductance MPPT method, implemented on low-cost microcontroller and SEPIC converter.” Solar Energy, vol. 180, 2019, pp 152-168.
• T. Radjai, L. Rahmani, S. Mekhilef, J. P. Gaubert. “Implementation of a modified incremental conductance MPPT algorithm with direct control based on a fuzzy duty cycle change estimator using dSPACE.” Solar Energy, vol. 110, 2014, pp 325-337.
• K. S. Tey, S. Mekhilef. “Modified incremental conductance algorithm for photovoltaic system under partial shading conditions and load variation.” IEEE Transactions on Industrial Electronics, vol. 61. 10, 2014, pp 5384-5392.
• K. S. Tey, S. Mekhilef. “Modified incremental conductance MPPT algorithm to mitigate inaccurate responses under fast-changing solar irradiation level.” Solar Energy, vol. 101, 2014, pp 333-342.
• R. Çelikel, A. Gündoğdu. “ANN-Based MPPT Algorithm for Photovoltaic Systems.” Turkish Journal of Science and Technology, vol. 15. 2, 2020, pp 101-110.
• F. Kentli, M. Yilmaz. "Mathematical modelling of two-axis photovoltaic system with improved efficiency." Elektronika Ir Elektrotechnika, vol. 21. 4, 2015, pp 40-43.
• A. Youssef, M. El Telbany, A. Zekry. “Reconfigurable generic FPGA implementation of fuzzy logic controller for MPPT of PV systems.” Renewable and Sustainable Energy Reviews, vol. 82, 2018, pp 1313-1319.
• A. Senthilvel, K. N. Vijeyakumar, B. Vinothkumar. FPGA Based Implementation of MPPT Algorithms for Photovoltaic System under Partial Shading Conditions. Microprocessors and Microsystems, 2020, 103011.
• A. Messai, A. Mellit, A. M. Pavan, A. Guessoum, H. Mekki. “FPGA-based implementation of a fuzzy controller (MPPT) for photovoltaic module.” Energy Conversion and Management, vol. 52. 7, 2011, pp 2695-2704.
• A. Mellit, H. Rezzouk, A. Messai, B. Medjahed. “FPGA-based real time implementation of MPPT-controller for photovoltaic systems.” Renewable Energy, vol. 36. 5, 2011, 1652-1661.
• R. Celikel. “ANN based angle tracking technique for shaft resolver.” "Measurement, vol. 148, 2019, 106910.
• S. Jaiswal, M. S. Ballal. “FDST-based PQ event detection and energy metering implementation on FPGA-in-the-loop and NI-LabVIEW.” IET Science, Measurement & Technology, vol. 11. 4, 2017, pp 453-463.
• S. Karimi, P. Poure, S. Saadate. “Fast power switch failure detection for fault tolerant voltage source inverters using FPGA.” IET Power Electronics, vol. 2. 4, 2009, pp 346-354.
• M. Morales-Caporal, J. Rangel-Magdaleno, H. Peregrina-Barreto, R. Morales-Caporal. “FPGA-in-the-loop simulation of a grid-connected photovoltaic system by using a predictive control.” Electrical Engineering, vol. 100. 3, 2018, pp 1327-1337.
• E. Deniz. “ANN-based MPPT algorithm for solar PMSM drive system fed by direct-connected PV array.” Neural Computing and Applications, vol. 28. 10, 2017, pp 3061-3072.
• O. Aydogmus. “Design of a solar motor drive system fed by a direct-connected photovoltaic array.” Advances in Electrical and Computer Engineering, vol. 12. 3, 2012, pp 53-58.