Real-Time Analysis for Enhancement of Photovoltaic Panel Efficiency and Quality

Bachir Zine, Chouaib Labiod, Kamel Srairi, Amel Benmouna, Mohamed Becherif, Abderrahmane Khechekhouche, Blaise Ravelo, Mohamed Naoui

Abstract


This study addressed the quality assessment of photovoltaic (PV) panels by analyzing their efficiency and electrical power under varying environmental conditions. A sophisticated algorithm was developed to extract and analyze PV panel voltage and current data in a complex manner, allowing the identification of key parameters such as open circuit voltage (Voc), short circuit current (Isc), and peak electrical power. These parameters were compared to the input power to accurately determine the panel efficiency. The novelty of this approach lies in its real-time implementation using a DC/DC (buck-boost) converter equipped with precise voltage and current sensors and running on a TMS320f379D board, linking theoretical knowledge with practical results. Experimental results demonstrated that temperature and irradiation significantly influence PV performance. With a 10°C increase in temperature, it resulted in a 5-10% decrease in output power, while a 100 W/m² increase in irradiation resulted in a 10-15% increase in output power. The study highlights the importance of considering both temperature and irradiation variations to optimize PV system design and operation, providing a robust method to assess PV panel quality in real-time.

Keywords


Efficiency evaluation; Electrical power output; Photovoltaic panels; Radiation impact; Real-time analysis; Temperature effects

Full Text:

PDF

References


Atiyah, H., Boukattaya, M., and Bensalem, F. (2023). Principal component analysis-based shading defect identification and categorization in standalone PV systems using I-V curves. International Journal of Energetica, 8(2), 44-53.

Bharadwaj, P., Chaudhury, K., and John, V. (2016). Sequential optimization for PV panel parameter estimation. IEEE Journal of Photovoltaics, 6, 1261-1268.

Dhimish, M., Holmes, V., Mehrdadi, B., and Dales, M. (2018). Comparing Mamdani Sugeno fuzzy logic and RBF ANN network for PV fault detection. Renewable Energy, 117, 257-274.

Eskandari, A., Milimonfared, J., and Aghaei, M. (2021). Fault detection and classification for photovoltaic systems based on hierarchical classification and machine learning technique. IEEE Transactions on Industrial Electronics, 68, 12750-12759.

Fadhel, S., Delpha, C., Diallo, D., Bahri, I., Migan, A., Trabelsi, M., and Mimouni, M. F. (2019). PV shading fault detection and classification based on I–V curve using principal component analysis: Application to isolated PV system. Solar Energy, 179, 1-10.

Ghodbane, M., Bessous, N., Boumeddane, B., and Lahrech, K. (2023). Optimal tilt angle for photovoltaic panels in the Algerian region of El-Oued in the spring season: An experimental study. International Journal of Energetica, 8(1), 24-30.

Hong, Y.-Y., and Pula, R. A. (2022). Methods of photovoltaic fault detection and classification: A review. Energy Reports, 8, 5898-5929.

Ji, D., Zhang, C., Lv, M., Ma, Y., and Guan, N. (2017). Photovoltaic array fault detection by automatic reconfiguration. Energies, 10(699), 1-13.

Kattakayam, T., Khan, S., and Srinivasan, K. (1996). Diurnal and environmental characterization of solar photovoltaic panels using a PC-AT add-on plug-in card. Solar Energy Materials and Solar Cells, 44, 25-36.

Kim, G., Lee, W., Bhang, B., Choi, J., and Ahn, H. (2021). Fault detection for photovoltaic systems using multivariate analysis with electrical and environmental variables. IEEE Journal of Photovoltaics, 11, 202-212.

Largot, S., Bessous, N., Ghodbane, M., Boumeddane, B., Lahrech, K., and Aswad, R. (2024). Dust accumulation effects on the performance of photovoltaic panels: An experimental study in the Algerian region of El-Oued. International Journal of Energetica, 9(1), 01-09.

Manno, D., Cipriani, G., Ciulla, G., Di Dio, V., Guarino, S., and Lo Brano, V. (2021). Deep learning strategies for automatic fault diagnosis in photovoltaic systems by thermographic images. Energy Conversion and Management, 241, 114315.

Medekhel, L., Srairi, K., and Labiod, C. (2022). Experimental study of temperature effects on the photovoltaic solar panels performances in Algerian desert. International Journal of Energetica, 7(1), 18-22.

Momeni, H., Sadoogi, N., Farrokhifar, M., and Gharibeh, H. (2020). Fault diagnosis in photovoltaic arrays using GBSSL method and proposing a fault correction system. IEEE Transactions on Industrial Informatics, 16, 5300-5308.

Movla, H., Shahalizad, A., and Abad, A. (2015). Influence of active region thickness on the performance of bulk heterojunction solar cells: Electrical modeling and simulation. Optical and Quantum Electronics, 47, 621-632.

Paul, M., Mahalakshmi, R., Karuppasamypandiyan, M., Bhuvanesh, A., and Ganesh, R. (2016). Fault identification and islanding in DC grid connected PV system. Circuits and Systems, 7, 2904-2915.

Roger, J., and Maguin, C. (1982). Photovoltaic solar panels simulation including dynamical thermal effects. Solar Energy, 29, 245-256.

Tina, G. M., Cosentino, F., and Ventura, C. (2015). Monitoring and diagnostics of photovoltaic power plants. Renewable Energy Services Manual, 2, 505-516.

Trypanagnostopoulos, G., Kavga, A., Souliotis, Μ., and Tripanagnostopoulos, Y. (2017). Greenhouse performance results for roof installed photovoltaics. Renewable Energy, 111, 724-731.

Vinod, P. (2008). Power loss calculation as a reliable methodology to assess the ohmic losses of the planar ohmic contacts formed on the photovoltaic devices. Journal of Materials Science: Materials in Electronics, 19, 594-601.

Vinod, R. K., and Singh, S. K. (2018). Solar photovoltaic modeling and simulation: As a renewable energy solution. Energy Reports, 4, 701-712.

Yang, Y., Blaabjerg, F., and Zou, Z. (2013). Benchmarking of grid fault modes in single-phase grid-connected photovoltaic systems. IEEE Transactions on Industry Applications, 49, 2167-2176.

Zagrouba, M., Sellami, A., Bouaïcha, M., and Ksouri, M. (2010). Identification of PV solar cells and modules parameters using the genetic algorithms: Application to maximum power extraction. Solar Energy, 84, 860-866.

Zaimi, M., Achouby, H., Ibral, A., and Assaid, E. (2019). Determining combined effects of solar radiation and panel junction temperature on all model-parameters to forecast peak power and photovoltaic yield of solar panel under non-standard conditions. Solar Energy. 191, 341-359.

Zhang, Z., Ma, M., Wang, H., Wang, H., Ma, W., and Zhang, X. (2021). A fault diagnosis method for photovoltaic module current mismatch based on numerical analysis and statistics. Solar Energy, 225, 221-236.

Zhao, Y., Palma, J., Mosesian, J., Lyons, R., and Lehman, B. (2013). Line-line fault analysis and protection challenges in solar photovoltaic arrays. IEEE Transactions on Industrial Electronics, 60, 3784-3795.

Zhu, H., Wang, H., Kang, D., Zhang, L., Lu, L., Yao, J., and Hu, Y. (2019). Study of joint temporal-spatial distribution of array output for large-scale photovoltaic plant and its fault diagnosis application. Solar Energy. 181, 137-147.




DOI: https://doi.org/10.17509/ijost.v9i3.74817

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Universitas Pendidikan Indonesia

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Indonesian Journal of Science and Technology is published by UPI.
StatCounter - Free Web Tracker and Counter
View My Stats