Non-coherent detection of dust in photovoltaic systems in series configuration using Lipschitz exponent

DOI:
https://doi.org/10.56845/rebs.v2i2.27Keywords:
fault detection, photovoltaic system, Lipschitz exponent, dust detectionAbstract
Failures in photovoltaic systems are a problem of great importance because they cause a deterioration in the production of electrical energy, among which is the dust on the surface of the photovoltaic system. This paper proposes a method to detect dust on the surface of a photovoltaic system in series configuration. In addition, shows by visual inspection that the IV characteristic of a photovoltaic panel is equal to the IV characteristic of a photovoltaic system. To obtain the results, 120 signals were used, 60 for the design of the method and the rest for the validation of the method. The proposed method only yielded 2 false positives out of 30 signals where there was no fault present.
References
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
field test results. Solar Energy, 74(3), 217–233. https://doi.org/10.1016/S0038-092X(03)00155-5
Xiao, C., Yu, X., Yang, D., & Que, D. (2014). Impact of solar irradiance intensity and temperature on the performance of compensated crystalline
silicon solar cells. Solar Energy Materials and Solar Cells, 128, 427–434. https://doi.org/10.1016/j.solmat.2014.06.018
Yi, Z., & Etemadi, A. H. (2017). Fault detection for photovoltaic systems based on multi-resolution signal decomposition and fuzzy inference
systems. IEEE Transactions on Smart Grid, 8(3), 1274–1283. https://doi.org/10.1109/TSG.2016.2587244
Zhao, Y., Li, D., Lu, T., Lv, Q., Gu, N., & Shang, L. (2020). Collaborative Fault Detection for Large-Scale Photovoltaic Systems. IEEE Transactions on
Sustainable Energy, 11(4), 2745–2754. https://doi.org/10.1109/TSTE.2020.2974404