Irradiance Acquisition in Real Time With Long Term Data Logger and Postprocessing Using Data Mining Methods
Keywords:Solar Energy, Data Acquisition, Data Mining, Photovoltaic panels, Irradiance, Temperature
Nowadays, there has been an increase in the usage of renewable energies, due to the environmental situation in our planet. Among the existing renewable energies, the photovoltaic energy is an attractive alternative. However, it has an intermittent production due to the environmental factors involved in its process. Particularly partial shading can make photovoltaic arrays consume energy instead of producing it. Also, it is useful to register the environmental factors, in order to implement control measurements. For this reason, in order to measure and register real-time temperature and irradiance precisely, a low-cost Data Acquisition System (DAS) is proposed. The implemented DAS has a wireless communication system, which allows the user to have a database through an interface. Subsequently the available power is calculated through a simplified math model, and used along with the irradiance and temperature as attributes. Finally, the data is classified through Data Mining techniques, such as k-means and ID3, and later used to predict whether the bypass diode remains connected or not.
Á. A. Bayod Rújula, Energías renovables: Sistemas fotovoltaicos. Zaragoza: Prensas Universitarias de Zaragoza, 2009.
J. Choi, M. Choi, Y. Shin, and I.-W. Lee, “Design of Web-based Monitoring System for Solar Photovoltaic Power Plants,” in 2020 International Conference on Information Networking (ICOIN), pp. 784–786, 2020.
M. B. Campanelli and C. R. Osterwald, “Effective Irradiance Ratios to Improve I–V Curve Measurements and Diode Modeling Over a Range of Temperature and Spectral and Total Irradiance,” IEEE Journal of Photovoltaics, vol. 6, no. 1, pp. 48–55, 2016.
B. H. Hamadani and M. B. Campanelli, “Photovoltaic Characterization Under Artificial Low Irradiance Conditions Using Reference Solar Cells,” IEEE Journal of Photovoltaics, vol. 10, no. 4, pp. 1119–1125,
A. Shakya, S. Michael, C. Saunders, D. Armstrong, P. Pandey, S. Chalise, and R. Tonkoski, “Solar Irradiance Forecasting in Remote Microgrids Using Markov Switching Model,” IEEE Transactions on Sustainable Energy, vol. 8, no. 3, pp. 895–905, 2017.
M. Muttillo, T. de Rubeis, D. Ambrosini, G. Barile, and G. Ferri, “Sensor monitoring system for PV plant with active load,” in 2019 IEEE 8th International Workshop on Advances in Sensors and Interfaces (IWASI), pp. 124–127, 2019.
A. C. Killam, J. F. Karas, A. Augusto, and S. G. Bowden, “Monitoring of Photovoltaic System Performance Using Outdoor Suns-VOC,” Joule, vol. 5, no. 1, pp. 210–227, 2021.
J. Walters, S. Guo, E. Schneller, H. Seigneur, and M. Boyd, “PV module loss analysis using system in-situ monitoring data,” in 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC 34th EU PVSEC), pp. 2204–2208, 2018.
A. Eschenbach, G. Yepes, C. Tenllado, J. I. Gómez-Pérez, L. Piñuel, L. F. Zarzalejo, and S. Wilbert, “Spatio-Temporal Resolution of Irradiance Samples in Machine Learning Approaches for Irradiance Forecasting,” IEEE Access, vol. 8, pp. 51518–51531, 2020.
E. Basha, R. Jurdak, and D. Rus, “In-Network Distributed Solar Current Prediction,” ACM Trans. Sen. Netw., vol. 11, Dec. 2015.
A. López-Vargas, M. Fuentes, M. V. García, and F. J. Muñoz-Rodríguez, “Low-Cost Datalogger Intended for Remote Monitoring of Solar Photovoltaic Standalone Systems Based on Arduino™,” IEEE Sensors Journal, vol. 19, no. 11, pp. 4308–4320, 2019.
P. Ayala, C. Muñoz, N. Osorio, C. Hernández, F. Zurita, V. Gutierrez, G. Ramirez, F. Mancilla, P. Valdivia, F. Cuevas, and P. Ferrada, “Bifacial Technology Performance Compared With Three Commercial Monofacial PV Technologies under Outdoor High Irradiance Conditions at the Atacama Desert,” in 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC 34th EU PVSEC), pp. 0672–0675, 2018.
K. Achuthan, J. D. Freeman, P. Nedungadi, U. Mohankumar, A. Varghese, A. M. Vasanthakumari, S. P. Francis, and V. K. Kolil, “Remote Triggered Dual-Axis Solar Irradiance Measurement System,” IEEE Transactions on Industry Applications, vol. 56, no. 2, pp. 1742–1751, 2020.
M. Dhaoui and H. Najjari, “Remote control and monitoring of the Aguereb-Jemna Kébili photovoltaic pumping station,” in 2017 International Conference on Green Energy Conversion Systems (GECS), pp. 1–6, 2017.
E. Kabalci and Y. Kabalci, “Remote monitoring system design for photovoltaic panels,” in 2017 10th International Symposium on Advanced Topics in Electrical Engineering (ATEE), pp. 888–891, 2017.
M. Sikimi´c, M. Amovi´c, V. Vujovi´c, B. Suknovi´c, and D. Manjak, “An Overview of Wireless Technologies for IoT Network,” in 2020 19th International Symposium INFOTEH-JAHORINA (INFOTEH), pp. 1–6, 2020.
DIGI, “Explore the Digi XBee Ecosystem 2021,” 2021.
A. Avila, P. R. Vizcaya, and R. Diez, “Daily irradiance test signal for photovoltaic systems by selection from long-term data,” Renewable Energy, vol. 131, pp. 755–762, 2019.
A. Prastiantono, M. Fadhil, A. Rahardjo, F. H. Jufri, and F. Husnayain, “Design of Solar Irradiance Measurement Based On Analitycal Data Using Microcontroller,” in 2019 IEEE Conference on Energy Conversion (CENCON), pp. 137–141, 2019.
B. Weber, R. Magaña-López, I. G. Martínez Cienfuegos, M. D. Durán-García, and E. A. Stadlbauer, “Current status of photovoltaic plants in Mexico – An analysis based on online monitoring,” Energy for
Sustainable Development, vol. 57, pp. 48–56, 2020.
M. Colak, M. Yesilbudak, and R. Bayindir, “Very Short-Term Estimation of Global Horizontal Irradiance Using Data Mining Methods,” in 7th International IEEE Conference on Renewable Energy Research and Applications, ICRERA 2018, vol. 7, (Paris), pp. 1472–1476, IEEE, 2018.
T. Stoffel and A. Andreas, “Nevada Power: Clark Station. Las Vegas, Nevada (Data),” 2006.
B. Zhou, W. Zhao, X. Su, S. Lu, T. Wang, W. Yao, P. Xie, T. Mao, L. Guan, and Y. Lv, “PV Power Characteristic Modeling based on Multiscale Clustering and Its Application in Generation Prediction,” IEEE Power and Energy Society General Meeting, vol. 2018-Augus, pp. 1–5, 2018.
X. Wu, V. Kumar, Q. J. Ross, J. Ghosh, Q. Yang, H. Motoda, G. J. McLachlan, A. Ng, B. Liu, P. S. Yu, Z. H. Zhou, M. Steinbach, D. J. Hand, and D. Steinberg, Top 10 algorithms in data mining. Springer, 2008.
M. Alfonso Galipienso, M. Cazorla Quevedo, O. Colomina Pardo, F. Escolano Ruiz, and M. Lozano Ortega, Inteligencia artificial: Modelos, Técnicas y Áreas de Aplicación. Ediciones Paraninfo S.A., 2003.
D. Rekioua and E. Matagne, Optimization of photovoltaic power systems: Modelization, Simulation and Control, vol. 102. London: Springer, 2012.