Universidad Autónoma del Estado de Morelos, Centro de Investigación en Ingeniería y Ciencias Aplicadas; Cuernavaca, Morelos, Mexico
Abstract
Los fallos en los sistemas fotovoltaicos son un problema de gran importancia ya que provocan un deterioro en la producción de energía eléctrica, entre ellos se encuentra el polvo en la superficie del sistema fotovoltaico. En este trabajo se propone un método para detectar polvo en la superficie de un sistema fotovoltaico en configuración serie. Además, se demuestra mediante inspección visual que la característica IV de un panel fotovoltaico es igual a la característica IV de un sistema fotovoltaico. Para obtener los resultados se utilizaron 120 señales, 60 para el diseño del método y el resto para la validación del método. El método propuesto sólo arrojó 2 falsos positivos de 30 señales en las que no había presencia de fallo.
Keywords
detección de fallos,sistema fotovoltaico,exponente de Lipschitz,detección de polvo
How to Cite
Seuret-Jiménez, D., & Trutié-Carrero, E. (2020). Detección no coherente de polvo en sistemas fotovoltaicos en configuración serie mediante el exponente de Lipschitz. Renewable Energy, Biomass & Sustainability, 2(2), 37–43. https://doi.org/10.56845/rebs.v2i2.27
📄Belboula, A., Taleb, R., Bachir, G., & Chabni, F. (2019). Comparative Study of Maximum Power Point Tracking Algorithms for Thermoelectric Generator. Lecture Notes in Networks and Systems, 62(1), 329–338. https://doi.org/10.1007/978-3-030-04789-4_36
📄Bhattacharya, M., Paramati, S. R., Ozturk, I., & Bhattacharya, S. (2016). The effect of renewable energy consumption on economic growth: Evidence from top 38 countries. Applied Energy, 162, 733–741. https://doi.org/10.1016/j.apenergy.2015.10.104
📄Chaibi, Y., Malvoni, M., Chouder, A., Boussetta, M., & Salhi, M. (2019). Simple and efficient approach to detect and diagnose electrical faults and partial shading in photovoltaic systems. Energy Conversion and Management, 196, 330–343. https://doi.org/10.1016/j.enconman.2019.05.086
📄Chouay, Y., & Ouassaid, M. (2018). An intelligent method for fault diagnosis in photovoltaic systems. Proceedings of 2017 International Conference on Electrical and Information Technologies, ICEIT 2017, 2018-Janua, 1–5. https://doi.org/10.1109/EITech.2017.8255225
📄Das, S., Hazra, A., & Basu, M. (2018). Metaheuristic optimization based fault diagnosis strategy for solar photovoltaic systems under non-uniform irradiance. Renewable Energy, 118, 452–467. https://doi.org/10.1016/j.renene.2017.10.053
📄Dhimish, M., Holmes, V., Mehrdadi, B., Dales, M., & Mather, P. (2017). Photovoltaic fault detection algorithm based on theoretical curves modelling and fuzzy classification system. Energy, 140, 276–290. https://doi.org/10.1016/j.energy.2017.08.102
📄Fadhel, S., Diallo, D., Delpha, C., Migan, A., Bahri, I., Trabelsi, M., & Mimouni, M. F. (2020). Maximum power point analysis for partial shading detection and identification in photovoltaic systems. Energy Conversion and Management, 224, 113374. https://doi.org/10.1016/j.enconman.2020.113374
📄Fezai, R., Mansouri, M., Trabelsi, M., Hajji, M., Nounou, H., & Nounou, M. (2019). Online reduced kernel GLRT technique for improved fault detection in photovoltaic systems. Energy, 179, 1133–1154. https://doi.org/10.1016/j.energy.2019.05.029
📄Garoudja, E., Harrou, F., Sun, Y., Kara, K., Chouder, A., & Silvestre, S. (2017). Statistical fault detection in photovoltaic systems. Solar Energy, 150, 485–499. https://doi.org/10.1016/j.solener.2017.04.043
📄Griffel, D. H., & Daubechies, I. (1995). Ten Lectures on Wavelets. In The Mathematical Gazette (Vol. 79, Issue 484). Siam. https://doi.org/10.2307/3620105
📄Hajji, M., Harkat, M. F., Kouadri, A., Abodayeh, K., Mansouri, M., Nounou, H., & Nounou, M. (2020). Multivariate feature extraction based supervised machine learning for fault detection and diagnosis in photovoltaic systems. European Journal of Control. https://doi.org/10.1016/j.ejcon.2020.03.004
📄Harrou, F., Taghezouit, B., & Sun, Y. (2019). Robust and flexible strategy for fault detection in grid-connected photovoltaic systems. Energy Conversion and Management, 180, 1153–1166. https://doi.org/10.1016/j.enconman.2018.11.022
📄Hu, L., Ye, J., Chang, S., Li, H., & Chen, H. (2017). A novel fault diagnostic technique for photovoltaic systems based on cascaded forest. SmartIoT 2017 - Proceedings of the Workshop on Smart Internet of Things, 1–5. https://doi.org/10.1145/3132479.3132482
📄Kumar, B. P., Ilango, G. S., Reddy, M. J. B., & Chilakapati, N. (2018). Online fault detection and diagnosis in photovoltaic systems using wavelet packets. IEEE Journal of Photovoltaics, 8(1), 257–265. https://doi.org/10.1109/JPHOTOV.2017.2770159
📄Livera, A., Theristis, M., Makrides, G., & Georghiou, G. E. (2019). Recent advances in failure diagnosis techniques based on performance data analysis for grid-connected photovoltaic systems. Renewable Energy, 133, 126–143. https://doi.org/10.1016/j.renene.2018.09.101
📄Lu, S., Sirojan, T., Phung, B. T., Zhang, D., & Ambikairajah, E. (2019). DA-DCGAN: An Effective Methodology for DC Series Arc Fault Diagnosis in Photovoltaic Systems. IEEE Access, 7, 45831–45840. https://doi.org/10.1109/ACCESS.2019.2909267
📄Mallat, S. (2009). A Wavelet Tour of Signal Processing. In A Wavelet Tour of Signal Processing (Third). Elsevier. https://doi.org/10.1016/B978-0- 12-374370-1.X0001-8
📄Mansouri, M., Al-khazraji, A., Hajji, M., Harkat, M. F., Nounou, H., & Nounou, M. (2018). Wavelet optimized EWMA for fault detection and application to photovoltaic systems. Solar Energy, 167, 125–136. https://doi.org/10.1016/j.solener.2018.03.073
📄Mekki, H., Mellit, A., & Salhi, H. (2016). Artificial neural network-based modelling and fault detection of partial shaded photovoltaic modules. Simulation Modelling Practice and Theory, 67, 1–13. https://doi.org/10.1016/j.simpat.2016.05.005
📄Mellit, A., Tina, G. M., & Kalogirou, S. A. (2018). Fault detection and diagnosis methods for photovoltaic systems: A review. Renewable and Sustainable Energy Reviews, 91, 1–17. https://doi.org/10.1016/j.rser.2018.03.062
📄Mrabti, T., Ouariachi, M. El, Kassmi, K., & Tidahf, B. (2010). Characterization and modelling of the optimal performances of the marketed photovoltaic panels . Moroccan Journal of Condensed Matter, 12(1).
📄Perraki, V., & Kounavis, P. (2016). Effect of temperature and radiation on the parameters of photovoltaic modules. Journal of Renewable and Sustainable Energy, 8(1), 13102. https://doi.org/10.1063/1.4939561
📄Platon, R., Pelland, S., & Poissant, Y. (2012). Modelling the Power Production of a Photovoltaic System: Comparison of Sugeno-Type Fuzzy Logic and PVSAT-2 Models. Europe Solar Conference (ISES).
📄Rouani, L., Harkat, M. F., Kouadri, A., & Mekhilef, S. (2021). Shading fault detection in a grid-connected PV system using vertices principal component analysis. Renewable Energy, 164, 1527–1539. https://doi.org/10.1016/j.renene.2020.10.059
📄Shahbaz, M., Raghutla, C., Chittedi, K. R., Jiao, Z., & Vo, X. V. (2020). The effect of renewable energy consumption on economic growth: Evidence from the renewable energy country attractive index. Energy, 207, 118162. https://doi.org/10.1016/j.energy.2020.118162
📄Sowthily, C., Senthil Kumar, S., & Brindha, M. (2021). Detection and Classification of Faults in Photovoltaic System Using Random Forest Algorithm. In Advances in Intelligent Systems and Computing (Vol. 1176, pp. 765–773). Springer. https://doi.org/10.1007/978-981-15-5788-0_72
📄Takashima, T., Yamaguchi, J., Otani, K., Oozeki, T., Kato, K., & Ishida, M. (2009). Experimental studies of fault location in PV module strings. Solar Energy Materials and Solar Cells, 93(6–7), 1079–1082. https://doi.org/10.1016/j.solmat.2008.11.060
📄Trutié-Carrero, E., Cabrera-Hernández, Y., Hernández-González, A., & Ramírez-Beltrán, J. (2020). Automatic detection of burst in water distribution systems by Lipschitz exponent and Wavelet correlation criterion. Measurement: Journal of the International Measurement Confederation, 151. https://doi.org/10.1016/j.measurement.2019.107195
📄Woyte, A., Richter, M., Moser, D., Mau, S., Reich, N., & Jahn, U. (2013). Monitoring of Photovoltaic Systems: Good Practices and Systematic Analysis. Journal of Chemical Information and Modeling, 53(9), 1689–1699. https://doi.org/10.1017/CBO9781107415324.004
📄Woyte, Achim, Nijs, J., & Belmans, R. (2003). Partial shadowing of photovoltaic arrays with different system configurations: Literature review and