ANFIS Based Real-Time Power Reference Generator for PV Applications

Öz

Çalışma ile ticari bir ürün olan SIS01-TC-T PV referans modülü ve STM geliştirme kartı kullanılarak 250Wp’lik güneş panelinin gerçek zamanlı güç tahmini yapılmaktadır. Güç tahmini Uyarlamalı ağ tabanlı bulanık çıkarım sistemi (ANFIS) ile gerçekleştirilmiştir. Eğitim sürecinde 250Wp güce sahip FotoVoltaik (PV) panele ait gerçek değerler kullanılmıştır. ANFIS eğitimi hyrib öğrenme algoritması ile gerçekleştirilmiş. Yapılan güç tahmini, çeşitli uygulamalar için referans güç olarak kullanılabilecektir. Elde edilen tahmini güç değeri; uzaktan izleme sistemleri için gerçek zamanlı güç izleme veya güneş takip mekanizması için optimum açı kontrolü vb. gibi uygulamalarda kullanılabilir. Diğer bir kullanım alanı olarak hibrit yapılı Maksimum Güç Noktası Takibi (MPPT) kontrol uygulamaları veya Oransal-İntegral-Türevsel denetleyici (PID) için referans değer olarak kullanılabilir. Ek olarak bu referans güç değeri ile çeşitli güç elektroniği katlarının ihtiyaç duyduğu Darbe Genişlik Modülasyonu (PWM) sinyalinin üretilmesi sağlanılabilir.

Anahtar Kelimeler

• ANFIS
• FV Panel
• Gerçek Zamanlı Güç Tahmini

ANFIS Based Real-Time Power Reference Generator for PV Applications

Öz

In this study, a real-time power estimation of a 250Wp solar panel is performed by using a commercial product SIS01-TC-T PV reference module and STM development board. Power estimation was carried out with Adaptive Neuro-Fuzzy Inference System (ANFIS). During the training process, the actual values of the Photo Voltaic (PV) panel with a 250Wp power were used. ANFIS training was accomplished with the hybrid learning algorithm. The power estimation process can be used as a reference power for various applications. The estimated power value can be used in real-time power monitoring for remote monitoring systems or optimum angle control applications for solar tracking mechanisms. It can also be used as a reference value for hybrid Maximum Power Point Tracking (MPPT) control applications or Proportional, Integral, and Derivative (PID) control. In addition, with this reference power value, the Pulse Width Modulation (PWM) signal required by various power electronics stages can be generated.

Anahtar Kelimeler

• ANFIS
• PV Panel
• Real-Time Power Estimation

Kaynakça

1. [1].ERSÖZ, Ö., ÇERÇİ, Y., & Orçun, E. K. İ. N. An Improved Design And Analysis of A Solar Receiver. El-Cezeri, 8(3), 1272-1285.
2. [2].Ben Naceur, F., Ben Salah, C., Telmoudi, A. J., & Mahjoub, M. A. (2021). Intelligent approach for optimal sizing in photovoltaic panel-battery system and optimizing smart grid energy. Transactions of the Institute of Measurement and Control. doi:Artn 01423312211027027 10.1177/01423312211027027
3. [3].Mlakic, D., & Nikolovski, S. (2016). Anfis as a Method for Determinating MPPT in the Photovoltaic System Simulated in Matlab/Simulink. 2016 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (Mipro), 1082-1086.
4. [4].Tabak, A., & Endiz, M. S. (2016). The Comparative Analyzes of Solar Energy Production Potential between Van and Antalya Using PVSOL Simulation Tool. i-Manager's Journal on Instrumentation & Control Engineering, 4(3), 1.
5. [5].Alamoudi, R., Taylan, O., Aktacir, M. A., & Herrera-Viedma, E. (2021). Designing a Solar Photovoltaic System for Generating Renewable Energy of a Hospital: Performance Analysis and Adjustment Based on RSM and ANFIS Approaches. Mathematics, 9(22). doi:ARTN 2929 10.3390/math9222929.
6. [6].KARAFİL, A., & ÖZBAY, H. (2018). Design of Stand-Alone PV System on a Farm House in Bilecik City, Turkey. El-Cezeri Journal of Science and Engineering, 5(3), 909-916.
7. [7].Fekry, H. M., Eldesouky, A. A., Kassem, A. M., & Abdelaziz, A. Y. (2020). Power Management Strategy Based on Adaptive Neuro Fuzzy Inference System for AC Microgrid. Ieee Access, 8, 192087-192100. doi:10.1109/Access.2020.3032705
8. [8].Muthuramalingam, M., & Manoharan, P. S. (2015). Simulation and Experimental Verification of MPPT Algorithms for Partially Shaded Stand Alone Photovoltaic Systems. Power Electronics and Renewable Energy Systems, 326, 153-161. doi:10.1007/978-81-322-2119-7_16

9. [9].Patil, S., Goudar, M., & Kharadkar, R. (2021). Neural network-based estimation of lighting condition in indoor environment with improved brain storm algorithm. Journal of Engineering Design and Technology. doi:10.1108/Jedt-03-2021-0143
10. [10].Vafaei, S., Rezvani, A., Gandomkar, M., & Izadbakhsh, M. (2015). Enhancement of grid-connected photovoltaic system using ANFIS-GA under different circumstances. Frontiers in Energy, 9(3), 322-334. doi:10.1007/s11708-015-0362-x
11. [11].Guo, S., Abbassi, R., Jerbi, H., Rezvani, A., & Suzuki, K. (2021). Efficient maximum power point tracking for a photovoltaic using hybrid shuffled frog-leaping and pattern search algorithm under changing environmental conditions. Journal of Cleaner Production, 297. doi:ARTN 126573 10.1016/j.jclepro.2021.126573
12. [12]. Omar, F. A., Pamuk, N., & KULAKSIZ, A. A. (2023). A critical evaluation of maximum power point tracking techniques for PV systems working under partial shading conditions. Turkish Journal of Engineering, 7(1), 73-81. [13]. Varghese, N., & Reji, P. (2016). Battery Charge Controller for Hybrid Stand Alone System Using Adaptive Neuro Fuzzy Inference System. 2016 International Conference on Energy Efficient Technologies for Sustainability (Iceets), 171-175.
13. [14].Arora, A., & Gaur, P. (2015). Comparison of ANN and ANFIS based MPPT controller for grid connected PV Systems. 2015 Annual Ieee India Conference (Indicon).
14. [15].Farzaneh, J. (2020). A hybrid modified FA-ANFIS-P&O approach for MPPT in photovoltaic systems under PSCs. International Journal of Electronics, 107(5), 703-718. doi:10.1080/00207217.2019.1672808
15. [16].Muniz, L. R., Severo, M. M., Braga, G. T., & Guimaraes, F. G. (2015). Neuro-Fuzzy Structure Applied in Maximum Power Point Tracking in Photovoltaic Panels. 2015 Ieee 13th Brazilian Power Electronics Conference and 1st Southern Power Electronics Conference (Cobep/Spec).
16. [17]. Manikandan, P. V., & Selvaperumal, S. (2020). EANFIS-based Maximum Power Point Tracking for Standalone PV System. Iete Journal of Research. doi:10.1080/03772063.2020.1788425
17. [18].Manikandan, P. V., & Selvaperumal, S. (2020). EANFIS-based Maximum Power Point Tracking for Standalone PV System. Iete Journal of Research. doi:10.1080/03772063.2020.1788425
18. [19].Dec, G., Dralus, G., Mazur, D., & Kwiatkowski, B. (2021). Forecasting Models of Daily Energy Generation by PV Panels Using Fuzzy Logic. Energies, 14(6). doi:ARTN 1676 10.3390/en14061676
19. [20]. Sinha, D. (2020). Adaptive Neuro-Fuzzy Approach for Forecasting of Solar Power Generation. Proceedings of the 2nd International Conference on Communication, Devices and Computing, 602, 429-439. doi:10.1007/978-981-15-0829-5_42
20. [21]. Amara, K., Fekik, A., Hocine, D., Hamida, M. L., Bourennane, E. B., Bakir, T., & Malek, A. (2018). Improved Performance of a PV Solar Panel with Adaptive Neuro Fuzzy Inference System ANFIS based MPPT. 2018 7th International Conference on Renewable Energy Research and Applications (Icrera), 1098-1101.
21. [22].Umadevi, K., & Nagarajan, C. (2020). Design and implementation of novel soft switching method based DC-DC converter with non-isolated coupled inductor in solar system using FPGA. Microprocessors and Microsystems, 73. doi:ARTN 102952 10.1016/j.micpro.2019.102952
22. [23].Benaissa, M. O., Hadjeri, S., Zidi, S. A., & Kobibi, Y. I. D. (2018). Photovoltaic Solar Farm with High Dynamic Performance Artificial Intelligence Based on Maximum Power Point Tracking Working as Statcom. Revue Roumaine Des Sciences Techniques-Serie Electrotechnique Et Energetique, 63(2), 156-161.
23. [24].Azizi, A., & Izadfar, H. R. (2019). A novel ANFIS-based MPPT controller for two-switch flyback inverter in photovoltaic systems. Journal of Renewable and Sustainable Energy, 11(4). doi:Artn 044702 10.1063/1.5082736
24. [25].Jyothirmayi, C. J., & Nasar, A. (2014a). A Real Time Algorithm Based Cascade Multilevel Inverter with Step Modulation Integrated Upon ANFIS Based Solar MPPT. 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (Iccicct), 1393-1399.
25. [26].Jyothirmayi, C. J., & Nasar, A. (2014b). Step Modulated Multilevel Inverter Incorporated Upon ANFIS based Intelligent PV MPPT. 2014 Annual International Conference on Emerging Research Areas: Magnetics, Machines and Drives (Aicera/Icmmd).
26. [27].Priyadharsini, K., Kumar, J. R. D., Babu, C. G., Srikanth, A., Sounddar, V., & Senthamilselvan, M. (2021). Elegant method to improve the efficiency of remotely located solar panels using IoT. Materials Today-Proceedings, 45, 8094-8104. doi:10.1016/j.matpr.2021.01.572
27. [28].Garcia, P., Garcia, C. A., Fernandez, L. M., Llorens, F., & Jurado, F. (2014). ANFIS-Based Control of a Grid-Connected Hybrid System Integrating Renewable Energies, Hydrogen and Batteries. Ieee Transactions on Industrial Informatics, 10(2), 1107-1117. doi:10.1109/Tii.2013.2290069
28. [29].Dahmane, M., Bosche, J., & El-Hajjaji, A. (2015). Power Management Strategy Based on Weather Prediction for Hybrid Stand-alone System. Sustainability in Energy and Buildings: Proceedings of the 7th International Conference Seb-15, 83, 330-340. doi:10.1016/j.egypro.2015.12.187
29. [30].Puri, V., Jha, S., Kumar, R., Priyadarshini, I., Son, L. H., Abdel-Basset, M., . . . Long, H. V. (2019). A Hybrid Artificial Intelligence and Internet of Things Model for Generation of Renewable Resource of Energy. Ieee Access, 7, 111181-111191. doi:10.1109/Access.2019.2934228
30. [31]. Shah, M. H., & Abosaq, N. H. (2020). Iot Based Efficient Solar Panel Monitoring. 3c Tecnologia, 9(4), 87-93. doi:10.17993/3ctecno/2020.v9n4e36.87-93
31. [32].Moyo, R. T., Tabakov, P. Y., & Moyo, S. (2021). Design and Modeling of the ANFIS-Based MPPT Controller for a Solar Photovoltaic System. Journal of Solar Energy Engineering-Transactions of the Asme, 143(4). doi:Artn 041002 10.1115/1.4048882
32. [33].GÜÇLÜ, Y. S. (2019). ANGSTRÖM-PRESCOTT MODELİNİN POLİNOM İLE GELİŞTİRİLMESİ VE DİYARBAKIR GÜNEŞ IŞINIMI VERİLERİNE UYGULANMASI. Selçuk Üniversitesi Mühendislik, Bilim ve Teknoloji Dergisi, 7(1), 75-88.