Robustness Analysis of ELM-based Fault Detection in PV Systems

Halah Sabah Alnafee

Abstract


 Recently, the research emphasize the importance of professional inspection and repair in case of suspected faults in Photovoltaic (PV) systems. By leveraging electrical and environmental features, many machine learning models can provide valuable insights into the operational status of PV systems. In this study, a different machine learning models for PV fault detection using a simulated 0.25 MW PV power system was developed and evaluated. The training and testing datasets encompassed normal operation and various fault scenarios, including string-to-string, on-string, and string-to-ground faults. Multiple electrical and environmental variables were measured and exploited as features, such as current, voltage, power, temperature, and irradiance. Four algorithms (Tree, LDA, SVM, and ANN) were tested using 5-fold cross-validation to identify errors in the PV system. The performance evaluation of the models revealed promising results, with all algorithms demonstrating high accuracy. The Tree and LDA algorithms exhibited the best performance, achieving accuracies of 99.544% on the training data and 98.058% on the testing data. LDA achieved perfect accuracy (100%) on the testing data, while SVM and ANN achieved 95.145% and 89.320% accuracy, respectively. These findings underscore the potential of machine learning algorithms in accurately detecting and classifying various types of PV faults.

Keywords


Photovoltaic (PV), Fault detection, Extreme Learning Machine (ELM), Grid-connected system

Full Text:

PDF

References


Solar Electricity - Photovoltaic Systems and Components, Grid-Connected Solar Electric

Systems, Off-Grid (Stand Alone) Solar Electric Systems, PV Modules, PV Inverters, PV

Chargers, PV Battery, PV Mounting, Small Solar Electric Devices. URL: https://www.

solardirect.com/archives/pv/systems/systems.htm.

Luis Hernández-Callejo, Sara Gallardo-Saavedra, and Víctor Alonso-Gómez. A review of photovoltaic systems: Design, operation and maintenance, 8 2019. doi:10.1016/j. solener.2019.06.017.

H. Mekki, A. Mellit, and H. Salhi. Artificial neural network-based modelling and faultdetection of partial shaded photovoltaic modules. Simulation Modelling Practice and Theory, 67:1–13, 9 2016. doi:10.1016/j.simpat.2016.05.005.

Labar Hocine, Kelaiaia Mounia Samira, Mesbah Tarek, Necaibia Salah, and Kelaiaia Samia. Automatic detection of faults in a photovoltaic power plant based on the observation of degradation indicators. Renewable Energy, 164:603–617, 2 2021. doi:10.1016/j.renene. 2020.09.094.

Kais Abdulmawjood, Shady S. Refaat, and Walid G. Morsi. Detection and prediction offaults in photovoltaic arrays: A review. In Proceedings - 2018 IEEE 12th InternationalConference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG2018, pages 1–8. Institute of Electrical and Electronics Engineers Inc., 6 2018. doi:10. 1109/CPE.2018.8372609.

Fazai, R., Abodayeh, K., Mansouri, M., Trabelsi, M., Nounou, H., Nounou, M., & Georghiou, G. E. (2019). Machine learning-based statistical testing hypothesis for fault detection in photovoltaic systems. Solar Energy, 190, 405-413.?

Hajji, M., Harkat, M. F., Kouadri, A., Abodayeh, K., Mansouri, M., Nounou, H., & Nounou, M. (2021). Multivariate feature extraction based supervised machine learning for fault detection and diagnosis in photovoltaic systems. European Journal of Control, 59, 313-321.?

Pahwa, K., Sharma, M., Saggu, M. S., & Mandpura, A. K. (2020, February). Performance evaluation of machine learning techniques for fault detection and classification in PV array systems. In 2020 7th International Conference on Signal Processing and Integrated Networks (SPIN) (pp. 791-796). IEEE.?

Cabeza, R. T., & Potts, A. S. (2021). Fault diagnosis and isolation based on Neuro-Fuzzy models applied to a photovoltaic system. IFAC-PapersOnLine, 54(14), 358-363.?

Pahwa, K., Sharma, M., Saggu, M. S., & Mandpura, A. K. (2020, International Conference on Signal Processing and Integrated Networks (SPIN) (pp. 791-796). IEEE.?

Mellit, A., & Kalogirou, S. (2021). Artificial intelligence and internet of things to improve efficacy of diagnosis and remote sensing of solar photovoltaic systems: Challenges, recommendations and future directions. Renewable and Sustainable Energy Reviews, 143, 110889.?

Mustafa, Z., Awad, A. S., Azzouz, M., & Azab, A. (2023). Fault identification for photovoltaic systems using a multi-output deep learning approach. Expert Systems with Applications, 211, 118551.?

Huang, G. B., et al. (2006). Extreme learning machine: Theory and applications. Neurocomputing, 70(1-3), 489-501.

Wu, Y., Chen, Z., Wu, L., Lin, P., Cheng, S., & Lu, P. (2017). An intelligent fault diagnosis approach for PV array based on SA-RBF kernel extreme learning machine. Energy Procedia, 105, 1070-1076.?

Chen, Z., Wu, L., Cheng, S., Lin, P., Wu, Y., & Lin, W. (2017). Intelligent fault diagnosis of photovoltaic arrays based on optimized kernel extreme learning machine and IV characteristics. Applied energy, 204, 912-931.?

Sun, Y., Wang, J., Yang, Q., Li, X., & Yan, W. (2018). Fault Diagnosis of Photovoltaic Module Based on Extreme Learning Machine Technique. Proceedings of the IMCIC.?

Ahmadipour, M., Othman, M. M., Alrifaey, M., Bo, R., & Ang, C. K. (2022). Classification of faults in grid-connected photovoltaic system February). Performance evaluation of machine learning techniques for fault detection and classification in PV array systems. In 2020 7th based on wavelet packet transform and an equilibrium optimization algorithm-extreme learning machine. Measurement, 197, 111338.?

Bazi, Y., Alajlan, N., Melgani, F., AlHichri, H., Malek, S. and Yager, R. R., (2014). “Differential Evolution Extreme Learning Machine for the Classification of Hyperspectral Images.” IEEE Geoscience and Remote Sensing Letters 11(6):1066–1070.

Abd Shehab, M., & Kahraman, N. (2018). Optimum, projected, and regularized extreme learning machine methods with singular value decomposition and Tikhonov regularization. Turkish Journal of Electrical Engineering and Computer Science, 26(4), 1685-1697.?

Abd Shehab, M., & Kahraman, N. (2020). A weighted voting ensemble of efficient regularized extreme learning machine. Computers & Electrical Engineering, 85, 106639.?

Sovilj, D., Sorjamaa, A., Yu, Q., Miche, Y. and Séverin, E., (2010). “OPELM and OPKNN in Long-Term Prediction of Time Series using Projected Input Data.” Neurocomputing 73(10–12):1976–1986.

Butcher, J. B., Verstraeten, D., Schrauwen, B., Day, C. R. and Haycock, P. W., (2013). “Reservoir Computing and Extreme Learning Machines for Non-Linear Time-Series Data Analysis.” Neural Networks 38:76–89.

Wang, X. and Han, M., (2014). “Multivariate Time Series Prediction Based on Multiple Kernel Extreme Learning Machine.” Pp. 198–201 in Neural Networks (IJCNN), 2014 International Joint Conference on IEEE.

Hong-Li, Z. X. W., (2011). “Incremental Regularized Extreme Learning Machine Based on Cholesky Factorization and Its Application to Time Series Prediction [J].” Acta Physica Sinica 11:1.

Yang, H., Xu, W., Zhao, J., Wang, D. and Dong, Z., (2011). “Predicting the Probability of Ice Storm Damages to Electricity Transmission Facilities Based on ELM and Copula Function.” Neurocomputing 74(16):2573–2581.

Martínez-Rego, D., Fontenla-Romero, O., Pérez-Sánchez, B. and Alonso-Betanzos, A., (2010). “Fault Prognosis of Mechanical Components Using on-Line Learning Neural Networks.” Pp. 60–66 in International Conference on Artificial Neural Networks. Springer.

Pal, M., Maxwell, A. E. and Warner, T. A., (2013). “Kernel-Based Extreme Learning Machine for Remote-Sensing Image Classification.” Remote Sensing Letters 4(9):853–862.

Ahmed, O. A., Habeeb, W. H., Mahmood, D. Y., Jalal, K. A., & Sayed, H. K. (2019). Design and performance analysis of 250 kw grid-connected photovoltaic system in iraqi environment using pvsyst software. Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 7(3), 415-421.

S. R. Madeti and S. N. Singh, “A comprehensive study on different types of faults and detection techniques for solar photovoltaic system,” Solar Energy, vol. 158, pp. 161–185, Dec. 2017, doi: https://doi.org/10.1016/j.solener.2017.08.069.

Ghoneim, S. S., Rashed, A. E., & Elkalashy, N. I. (2021). Fault Detection Algorithms for Achieving Service Continuity in Photovoltaic Farms. Intelligent Automation & Soft Computing, 30(2).?

A. Triki-Lahiani, A. Bennani-Ben Abdelghani, and I. Slama-Belkhodja, “Fault detection and monitoring systems for photovoltaic A review,” Renewable and Sustainable Energy Reviews, vol. 82, pp. 2680–2692, Feb. 2018, doi: https://doi.org/10.1016/j.rser.2017.09.101.

Z. Yi and A. H. Etemadi, “Fault Detection for Photovoltaic Systems Based on Multi-Resolution Signal Decomposition and Fuzzy Inference Systems,” IEEE Transactions on Smart Grid, vol. 8, no. 3, pp. 1274–1283, May 2017, doi: https://doi.org/10.1109/tsg.2016.2587244.

M. H. Ali, A. Rabhi, A. E. Hajjaji, and G. M. Tina, “Real Time Fault Detection in Photovoltaic Systems,” Energy Procedia, vol. 111, pp. 914–923, Mar. 2017, doi: https://doi.org/10.1016/j.egypro.2017.03.254.

Ramón Fernando Colmenares-Quintero, E. Rojas, F. Macho-Hernantes, K. E. Stansfield, and Juan Carlos Colmenares, “Methodology for automatic fault detection in photovoltaic arrays from artificial neural networks,” vol. 8, no. 1, Sep. 2021, doi: https://doi.org/10.1080/23311916.2021.1981520.

Jiang, C., & Yang, Z. (2015). CKNNI: an improved knn-based missing value handling technique. In Advanced Intelligent Computing Theories and Applications: 11th International Conference, ICIC 2015, Fuzhou, China, August 20-23, 2015. Proceedings, Part III 11 (pp. 441-452). Springer International Publishing.?

Huang, G. B., Ding, X., & Zhou, H. (2010). Optimization method based extreme learning machine for classification. Neurocomputing, 74(1-3), 155-163.??

Connelly, L. M. (2021). Introduction to analysis of variance (ANOVA). Medsurg Nursing, 30(3), 218-158.?




DOI (PDF): https://doi.org/10.20508/ijsmartgrid.v7i4.311.g300

Refbacks

  • There are currently no refbacks.


www.ijsmartgrid.com; www.ijsmartgrid.org

iilhcol@gmail.com; ijsmartgrid@nisantasi.edu.tr

Online ISSN: 2602-439X

Publisher: ilhami COLAK (istanbul Nisantasi Univ)

Cited in Google Scholar and CrossRef