Hardware design for maximum power point tracking (MPPT) based on metaheuristic algorithm in photovoltaic (PV) systems

Authors

DOI:

https://doi.org/10.15587/1729-4061.2024.317948

Keywords:

algorithm, partial shading, particle swarm optimization, artificial bee colony, MPPT

Abstract

PSO and ABC algorithms with Arduino microcontrollers are focused on developing efficient solutions for control systems, energy optimization, and signal processing. These algorithms are generally for platforms with large resources, making them difficult to implement directly on Arduino. Adjustments are needed so that the algorithm can work efficiently without sacrificing the quality of the results. Both are implemented for partially shaded conditions in photovoltaic (PV) systems. The MPPT hardware development method with this meta algorithm can be a solution in dealing with the constraints of partially shaded disturbances. Meanwhile, other studies of the two concepts of the PSO and ABC algorithms have also been developed through software simulations for both MPPT applications and other fields. Evaluation criteria and methods for optimizing MPPT performance have been proposed by implementing a DC-DC Boost Converter. Testing was conducted with a PV with of 47.6 V and Isc of 11.6 A under two conditions to assess the performance of the PSO and ABC. The test resulted in the average power generated by the system with PSO algorithm on three unshaded PV with irradiation of 801 W/m² and a temperature of 84.5 °C with load variations of 50 Ω, 100 Ω, 200 Ω, and 400 Ω was 49.06 W, while the irradiation on one shaded PV at 198 W/m² resulted in an average power of 46.13 W. The system using the ABC algorithm on three unshaded PV generated an average power of 48.35 W, and with irradiation on one shaded solar panel at 198 W/m², it generated an average power of 45.03 W. Overall, the study demonstrates that both PSO and ABC algorithms effectively improve power generation in partially shaded conditions, with PSO showing better performance. These findings suggest that implementing these algorithms can enhance the efficiency of PV systems in practical applications

Supporting Agency

  • The author would like to express his deepest gratitude to the Department of Electrical Engineering, Faculty of Engineering, Diponegoro University, which has provided a Strategic Grant research. Special thanks to the faculty members and administrative staff for their invaluable assistance and guidance. We also appreciate the collaborative environment and the access to the laboratory facilities, which were instrumental in the successful completion of this study.

Author Biographies

Darjat Darjat, Diponegoro University

Doctor of Engineering

Department of Electrical Engineering

Satria Arya Bima, Diponegoro University

Bachelor of Engineering Graduates

Department of Electrical Engineering

Hieronimus Emilianus Evangelista, Diponegoro University

Bachelor of Engineering Graduates

Department of Electrical Engineering

Bambang Winardi, Diponegoro University

Master of Computer

Department of Electrical Engineering

Ajub Ajulian Zahra, Diponegoro University

Master of Engineering

Department of Electrical Engineering

Nooritawati Md Tahir, Universiti Teknologi MARA

PhD

College of Engineering

References

  1. Rimantho, D., Hidayah, N. Y., Pratomo, V. A., Saputra, A., Akbar, I., Sundari, A. S. (2023). The strategy for developing wood pellets as sustainable renewable energy in Indonesia. Heliyon, 9 (3), e14217. https://doi.org/10.1016/j.heliyon.2023.e14217
  2. Aprilianto, R. A., Ariefianto, R. M. (2021). Peluang Dan Tantangan Menuju Net Zero Emission (NZE) Menggunakan Variable Renewable Energy (VRE) Pada Sistem Ketenagalistrikan Di Indonesia. Jurnal Paradigma, 2 (2). Available at: https://www.researchgate.net/profile/Rizky-Ajie-Aprilianto/publication/357448042_Peluang_Dan_Tantangan_Menuju_Net_Zero_Emission_NZE_Menggunakan_Variable_Renewable_Energy_VRE_Pada_Sistem_Ketenagalistrikan_Di_Indonesia/links/61ce9438d4500608167c1faf/Peluang-Dan-Tantangan-Menuju-Net-Zero-Emission-NZE-Menggunakan-Variable-Renewable-Energy-VRE-Pada-Sistem-Ketenagalistrikan-Di-Indonesia.pdf
  3. Fathoni, A. M., Utama, N. A., Kristianto, M. A. (2014). A Technical and Economic Potential of Solar Energy Application with Feed-in Tariff Policy in Indonesia. Procedia Environmental Sciences, 20, 89–96. https://doi.org/10.1016/j.proenv.2014.03.013
  4. Nurjaman, H. B., Purnama, T. (2022). Pembangkit Listrik Tenaga Surya (PLTS) Sebagai Solusi Energi Terbarukan Rumah Tangga. Jurnal Edukasi Elektro, 6 (2), 136–142. https://doi.org/10.21831/jee.v6i2.51617
  5. Tampubolon, A. C. P., Adiatma, J. (2023). Laporan Status Energi Bersih Indonesia. IESR.
  6. Annual Report ∙ PT PLN (Persero). Available at: https://web.pln.co.id/statics/uploads/2024/10/AR-PLN-2023_1610-hi.pdf
  7. Refaat, A., Khalifa, A.-E., Elsakka, M. M., Elhenawy, Y., Kalas, A., Elfar, M. H. (2023). A novel metaheuristic MPPT technique based on enhanced autonomous group Particle Swarm Optimization Algorithm to track the GMPP under partial shading conditions - Experimental validation. Energy Conversion and Management, 287, 117124. https://doi.org/10.1016/j.enconman.2023.117124
  8. Gautam, V., Jalil, M. F., Khatoon, S., Bakhsh, F. I. (2023). Improved power generation from PV array operating in partial shading scenarios by shade dispersion using Sumoku reconfiguration. 2023 IEEE 3rd International Conference on Smart Technologies for Power, Energy and Control (STPEC), 1–6. https://doi.org/10.1109/stpec59253.2023.10430632
  9. Thanikanti, S. B., B, P. K., S, D., Aljafari, B., Colak, I. (2023). A dynamic mismatch loss mitigation algorithm with dual input dual output converter for solar PV systems. Solar Energy Materials and Solar Cells, 251, 112163. https://doi.org/10.1016/j.solmat.2022.112163
  10. Krishnan M., M., Bharath, K. R. (2019). A Novel Sensorless Hybrid MPPT Method Based on FOCV Measurement and P&O MPPT Technique for Solar PV Applications. 2019 International Conference on Advances in Computing and Communication Engineering (ICACCE), 1–5. https://doi.org/10.1109/icacce46606.2019.9079953
  11. Raziya, F., Afnaz, M., Jesudason, S., Ranaweera, I., Walpita, H. (2019). MPPT Technique Based on Perturb and Observe Method for PV Systems Under Partial Shading Conditions. 2019 Moratuwa Engineering Research Conference (MERCon), 474–479. https://doi.org/10.1109/mercon.2019.8818684
  12. Maulana, F. (2023). Impementasi MPPT Menggunakan Human Psychology Optimization (HPO) Algorithm dengan Boost Converter pada Panel Surya dengan Kondisi Partial Shading. Politeknik Negeri Jakarta. Available at: https://repository.pnj.ac.id/id/eprint/10622/
  13. Subudhi, B., Pradhan, R. (2013). A Comparative Study on Maximum Power Point Tracking Techniques for Photovoltaic Power Systems. IEEE Transactions on Sustainable Energy, 4 (1), 89–98. https://doi.org/10.1109/tste.2012.2202294
  14. Li, P., Zhang, J., Xu, R., Zhou, J., Gao, Z. (2024). Integration of MPPT algorithms with spacecraft applications: Review, classification and future development outlook. Energy, 308, 132927. https://doi.org/10.1016/j.energy.2024.132927
  15. Águila-León, J., Vargas-Salgado, C., Díaz-Bello, D., Montagud-Montalvá, C. (2024). Optimizing photovoltaic systems: A meta-optimization approach with GWO-Enhanced PSO algorithm for improving MPPT controllers. Renewable Energy, 230, 120892. https://doi.org/10.1016/j.renene.2024.120892
  16. Samir Eldessouky, A., Mahmoud, I. M., Abdel-Salam, T. S. (2023). MPPT based on a novel load segmentations structure for PV applications. Ain Shams Engineering Journal, 14 (4), 101937. https://doi.org/10.1016/j.asej.2022.101937
  17. Talib Naser, A., Khairullah Mohammed, K., Fadilah Ab Aziz, N., Elsanabary, A., Binti Kamil, K., Mekhilef, S. (2024). A fast-tracking MPPT-based modified coot optimization algorithm for PV systems under partial shading conditions. Ain Shams Engineering Journal, 15 (10), 102967. https://doi.org/10.1016/j.asej.2024.102967
  18. Yang, B., Xie, R., Duan, J., Wang, J. (2023). State-of-the-art review of MPPT techniques for hybrid PV-TEG systems: Modeling, methodologies, and perspectives. Global Energy Interconnection, 6 (5), 567–591. https://doi.org/10.1016/j.gloei.2023.10.005
  19. Yatimi, H., Aroudam, E. (2018). MPPT algorithms based modeling and control for photovoltaic system under variable climatic conditions. Procedia Manufacturing, 22, 757–764. https://doi.org/10.1016/j.promfg.2018.03.108
  20. Ullah, K., Ishaq, M., Tchier, F., Ahmad, H., Ahmad, Z. (2023). Fuzzy-based maximum power point tracking (MPPT) control system for photovoltaic power generation system. Results in Engineering, 20, 101466. https://doi.org/10.1016/j.rineng.2023.101466
  21. Arduino Uno R3. Arduino. Available at: https://docs.arduino.cc/resources/datasheets/A000066-datasheet.pdf
  22. Li, L., Chen, Y. Z., Zhou, H., Ma, H., Liu, J. (2010). The application of hall sensors ACS712 in the protection circuit of controller for humanoid robots. 2010 International Conference on Computer Application and System Modeling (ICCASM 2010). https://doi.org/10.1109/iccasm.2010.5622149
  23. Li, H., Yang, D., Su, W., Lu, J., Yu, X. (2019). An Overall Distribution Particle Swarm Optimization MPPT Algorithm for Photovoltaic System Under Partial Shading. IEEE Transactions on Industrial Electronics, 66 (1), 265–275. https://doi.org/10.1109/tie.2018.2829668
  24. Li, H., Gao, S., Chen, C., Melnikov, S. N., Yang, X., Li, J. (2019). MPPT Algorithm Based on Improved PSO and Fuzzy Algorithm. 2019 Chinese Automation Congress (CAC), 243–248. https://doi.org/10.1109/cac48633.2019.8997254
  25. Abdullah, M. A., Al-Hadhrami, T., Tan, C. W., Yatim, A. H. (2018). Towards Green Energy for Smart Cities: Particle Swarm Optimization Based MPPT Approach. IEEE Access, 6, 58427–58438. https://doi.org/10.1109/access.2018.2874525
  26. Hassan, S., Abdelmajid, B., Mourad, Z., Aicha, S., Abdenaceur, B. (2017). An Advanced MPPT Based on Artificial Bee Colony Algorithm for MPPT Photovoltaic System under Partial Shading Condition. International Journal of Power Electronics and Drive Systems (IJPEDS), 8 (2), 647. https://doi.org/10.11591/ijpeds.v8.i2.pp647-653
  27. Fanani, M. R., Sudiharto, I., Ferdiansyah, I. (2020). Implementation of Maximum Power Point Tracking on PV System using Artificial Bee Colony Algorithm. 2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), 117–122. https://doi.org/10.1109/isriti51436.2020.9315527
  28. Hart, W. D. (2011). Power Electronics. McGraw-Hill, 477.
  29. IRFP260NPbF. Infineon. Available at: https://www.infineon.com/dgdl/Infineon-IRFP260N-DataSheet-v01_01-EN.pdf?fileId=5546d462533600a401535628a2ef1fe4
  30. STTH3003CW Datasheet (PDF) - STMicroelectronics. Available at: https://www.alldatasheet.com/datasheet-pdf/pdf/24779/STMICROELECTRONICS/STTH3003CW.html
  31. Shi, J., Zhang, W., Zhang, Y., Xue, F., Yang, T. (2015). MPPT for PV systems based on a dormant PSO algorithm. Electric Power Systems Research, 123, 100–107. https://doi.org/10.1016/j.epsr.2015.02.001
  32. Benyoucef, A. soufyane, Chouder, A., Kara, K., Silvestre, S., sahed, O. A. (2015). Artificial bee colony based algorithm for maximum power point tracking (MPPT) for PV systems operating under partial shaded conditions. Applied Soft Computing, 32, 38–48. https://doi.org/10.1016/j.asoc.2015.03.047
  33. Javed, S., Ishaque, K. (2022). A comprehensive analyses with new findings of different PSO variants for MPPT problem under partial shading. Ain Shams Engineering Journal, 13 (5), 101680. https://doi.org/10.1016/j.asej.2021.101680
Hardware design for maximum power point tracking (MPPT) based on metaheuristic algorithm in photovoltaic (PV) systems

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Published

2024-12-30

How to Cite

Darjat, D., Bima, S. A., Evangelista, H. E., Winardi, B., Zahra, A. A., & Tahir, N. M. (2024). Hardware design for maximum power point tracking (MPPT) based on metaheuristic algorithm in photovoltaic (PV) systems. Eastern-European Journal of Enterprise Technologies, 6(5 (132), 22–32. https://doi.org/10.15587/1729-4061.2024.317948

Issue

Vol. 6 No. 5 (132) (2024): Applied physics

Section

Applied physics

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Copyright (c) 2024 Darjat Darjat, Satria Arya Bima, Hieronimus Emilianus Evangelista, Bambang Winardi, Ajub Ajulian Zahra, Nooritawati Md Tahir

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