Intelligent Fault Management for Photovoltaic System using Cross-Correlation and Machine Learning
DOI:
https://doi.org/10.63746/njtd.v22i4.3085Keywords:
Photovoltaic, Bayesian, Adam, hybrid optimization, fault, cross correlation.Abstract
This paper presents a fault management model for the protection of off-grid PV system. Data was collected from an off-grid 4.2 kW PV system installed at Edugen technologies Ltd, located at Nsukka, Enugu State, Nigeria. An adaptive feature extractor using a cross-correlation approach was used to identify and extract intricate and high dimensional features of PV characteristics. This data was then used to train a neural network. For the training of the network, Bayesian optimization (Bayes) and adaptive movement estimation (Adam), were used individually and as hybrid. The models generated were evaluated experimentally, considering Mean Square Error (MSE), Coefficient of determination (), and Root Mean Square Error (RMSE). Comparative analysis of the three training algorithms showed that the hybrid optimization algorithm recorded better of 0.9824, MSE of 9.5673e-9, and RMSE of 9.781e-5 when compared to Bayes and Adam. In conclusion, the hybrid optimization-based PV fault detection model was the most suitable for PV protection and is therefore recommended for the protection of off-grid standalone PV systems to ensure power sustainability.
References
Adrian-Martinez, S., & Ardid, M., (2015). Acoustic Signal detection Through the Cross-correlation Methods in Experiments with Different Signal to Noise Ratio and Reverberation Conditions. Journal of Instrumentation and Detectors.
Al-Katheri, A.A., Al-Ammar, E.A., Alotaibi, M.A., Ko, W., Park, S., & Choi, H.-J. (2022). Application of Artificial Intelligence in PV Fault Detection. Sustainability 2022, 14, 13815. https://doi.org/10.3390/su142113815
Appiah, A.Y., Zhang, X., Ayawli, B.B.K., & Kyeremeh, F. (2019). Review and Performance Evaluation of Photovoltaic Array Fault Detection and Diagnosis Techniques. Int. J. Photoenergy 2019, 2019, 6953530.
Assran, M., Loizou, N., Ballas, N., & Rabbat, M. (2019). Stochastic gradient push for distributed deep learning. 36th Int Conf Mach learn ICML 2019 2019-June:514–523. https://proceedings.mlr.press/v97/assran19a.html
Begum, M., Shorif, S.B., Uddin, M.S., Ferdush, J., Jan, T., Barros, A., & Whaiduzzaman, M. (2024). Image Watermarking Using Discrete Wavelet Transform and Singular Value Decomposition for Enhanced Imperceptibility and Robustness. Algorithms 17, 32. https://doi.org/10.3390/a17010032
Bishop, C.M. (1995). Neural Networks for Pattern Recognition: Oxford University Press.
Bose, R., Samanta, K., & Chatterjee, S. (2016). Cross-correlation based feature extraction from EMG signals for classification of neuro-muscular diseases. International Conference on Intelligent Control Power and Instrumentation (ICICPI), Kolkata, India, 2016, pp. 241-245, doi: 10.1109/ICICPI.2016.7859710.
Brochu, E., Cora, V.M., & De Freitas N (2010). A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcementlearning.arXivPreprarXiv10122599. https://doi.org/10.48550/arXiv.1012.2599
Camacho, N. J., Ruiz, M., Villamizar, R., Mujica, L., & Quiroga, J. (2018). Features of Cross-Correlation Analysis in a Data-Driven Approach for Structural Damage Assessment. Sensors 2018, 18, 1571. https://doi.org/10.3390/s18051571
Chen, D., Wan, S., & Bao, F.S. (2017). Epileptic Focus Localization Using Discrete Wavelet Transform Based on Interictal Intracranial EEG. IEEE Trans Neural Syst Rehabil Eng. 25(5):413-425. doi: 10.1109/TNSRE.2016.2604393. Epub 2016 Aug 30. PMID: 28113594.
Chen, L., Han, W., & Cao, X. (2020). Long distance wireless fault diagnosis for photovoltaic modules based on back propagation neural network. Int. Journal of Electrical EngEducat. https://doi.org/10.1177/ 0020720920940601
Chen, Z., Chen, Y., Wu, L., Cheng, S., & Lin, P. (2019). Deep Residual Network Based Fault Detection and Diagnosis of Photovoltaic Arrays Using Current-Voltage Curves and Ambient Conditions. Energy Convers. Manag. 2019, 198, 111793.
Chidi, E. U., Udanor, C. N., & Anoliefo, E. (2024). Exploring the Depths of Visual Understanding: A Comprehensive Review on Real-Time Object of Interest Detection Techniques. Preprints 2024, 2024020583. https://doi.org/10.20944/preprints202402.0583.v1
Chine,W., Mellit, A., Lughi, V., Malek, A., Sulligoi, G., & Pavan, A.M. (2016). A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks. Renew. Energy 90, 501–512.
Coleman, A., & Zalewski, J. (2011). Intelligent Fault Detection and Diagnostics. In Proceedings of the The 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, Prague, Czech Republic, 15–17 September 2011
Dau, A., & Salim, N. (2020). Recommendation system based on deep learning methods: a systematic review and new directions. Artif Intel Rev. 2020;53(4):2709–48.
Defazio, A., & Jelassi, S. (2022). Adaptivity without compromise: a momentumized, adaptive, dual averaged gradient method for stochastic optimization. J Mach Learn Res 23:1–34
Demuth, H., & Beale, M. (2000). Neural Network Toolbox User’s Guide Version 4; The Math Works Inc.: Natick, MA, USA, pp. 5–22.
Deng, L. (2014) A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA Trans Signal Inf Process. 2014; p. 3.
Ebere, U., Anoliefo, E., Udanor, C., Chijindu, T., & Nwobodo, L. (2025). A Blind navigation guide model for obstacle avoidance using distance vision estimation based YOLO-V8n; Journal of the Nigerian Society of Physical Sciences, 2292-229; https://doi.org/10.46481/jnsps.2025.2292
El-Banby, M., Moawad, M., & Abouzalm, A. (2023). Photovoltaic system fault detection techniques: a review. Neural Computing&Application 35, 24829–24842. https://doi.org/10.1007/s00521-023-09041-7
El-Rashidy, M.A. (2022). An efficient and portable solar cell defect detection system. Neural Comput Appl 34:18497–18509. https:// doi.org/10.1007/s00521-022-07464-2
Et-Taleby, A., Chaibi, Y., Benslimane, M., & Boussetta M., (2023) Applications of Machine Learning Algorithms for Photovoltaic Fault Detection: a Review. Statistics, Optimization and Information Computing Stat., Optim. Inf. Comput., Vol. 11, January 2023, pp 168–177. DOI: 10.19139/soic-2310-5070-1537
Firth, S., Lomas, K., & Rees, S. A. (2010).Simple model of PV system performance and its use in fault detection. Sol. Energy2010, 84, 624–635.
Gao, W., & Wai, R.J. (2020). A Novel Fault Identification Method for Photovoltaic Array via Convolutional Neural Network and Residual Gated Recurrent Unit. IEEE Access 2020, 8, 159493–159510
Garnett, R. (2023). Bayesian Optimization; Cambridge University Press: Cambridge, UK.
Garoudja, E., Harrou, F., Sun, Y., Kara, K., Chouder, A., & Silvestre, S. (2017). Statistical fault detection in photovoltaic systems. Solar Energy 2017, 150, 485–499.
Global Cumulative Installed Solar PV Capacity 2019|Statista. Available online: https://www.statista.com/statistics/280220 /global-cumulative-installed-solar-pv-capacity (accessed on 10 September 2024).
Habtemariam, E.T., Kekeba, K., Martínez-Ballesteros, M., & Martínez-Álvarez, F. (2023). A Bayesian Optimization-Based LSTM Model for Wind Power Forecasting in the Adama District, Ethiopia. Energies 16, 2317. https://doi.org/10.3390/en16052317
Harbor, M.C., Eneh, I.I., & Ebere U.C. (2021). Nonlinear dynamic control of autonomous vehicle under slip using improved back-propagation algorithm. International Journal of Research and Innovation in Applied Science (IJRIAS); Vol. 6; Issue 9; https://rsisinternational.org/journals/ijrias/DigitalLibrary/volume-6-issue-9/62-68.pdf
Hassan, E., Shams, M.Y., & Hikal, N.A. (2023). The effect of choosing optimizer algorithms to improve computer vision tasks: a comparative study. Multimed Tools Appl 82, 16591–16633. https://doi.org/10.1007/s11042-022-13820-0
Heinrich Haberlin, (2016). ‘‘Photovoltaic system design and practice’’ second Edition , John Wiley & Sons, Ltd.
Hong, Y.Y., & Pula, R.A. (2022). Methods of Photovoltaic Fault Detection and Classification: A Review. Energy Rep. 2022, 8, 5898–5929.
Ibrahim, B. M., Taha Nagy, I., Alsharef, M., Sabiha, N. A., Metwaly, M. K., & Elattar, E. E. (2020). Investigation of diode dynamic effect on fault detection of photovoltaic systems. Sustainable Energy, Grids and Networks, 23, 100389. https://doi.org/10.1016/j.segan.2020.100389
Kachhwaha, A., Rashed, G. I., Garg, A. R., Mahela, O. P., Khan, B., Shafik, M. B., & Hussien, M. G. (2022). Design and Performance Analysis of Hybrid Battery and Ultracapacitor Energy Storage System for Electrical Vehicle Active Power Management. Sustainability, 14(2), 776.
Kayri, M. (2016). Predictive Abilities of Bayesian Regularization and Levenberg–Marquardt Algorithms in Artificial Neural Networks: A Comparative Empirical Study on Social Data. Math. Comput. Appl. 21, 20. https://doi.org/10.3390/mca21020020
Kekong, P.E, Ajah, I.A., & Ebere, U.C. (2019). Real-time drowsy driver monitoring and detection system using deep learning based behavioural approach. International Journal of Computer Sciences and Engineering 9 (1), 11-21; http://www.ijcseonline.isroset.org/pub_paper/2-IJCSE-08441-18.pdf
Khelil, C.K.M., Amrouche, B., Kara, K., & Chouder, A. (2021). The impact of the ANN’s choice on PV systems diagnosis quality. Energy Convers. Manag. 240, 114278.
Kontges, M., Kurtz, S., Packard, C., Jahn, U., Berger, K., Kato, K., Friesen, T., Liu, H., Iseghem, M.V., Wohlgemuth, J., Miller, D., Kempe, M., Hacke, P., Reil, F., Bogdansk, N., Herrmann, W., Buerhop-Lutz, C., Razongles, G., & Friesen G (2014). Review of failures of photovoltaic modules—iea-pvps [www document]. https://iea-pvps.org
Lakshmi, P., Sivagamasundari, S., & Rayudu, M. (2023) IoT based solar panel fault and maintenance detection using decision tree with light gradient boosting. Measurement: Sensors 27 (2023) 100726 https://doi.org/10.1016/j.measen.2023.100726
Lee, H., & Yun, S. (2024). Strategies for Imputing Missing Values and Removing Outliers in the Dataset for Machine Learning-Based Construction Cost Prediction. Buildings 14, 933. https://doi.org/10.3390/buildings14040933
Lee, S. (2020). Performance Comparison of Imputation Algorithms for Missing Data. Master’s Thesis, Graduate School of Korea University, Seoul, Republic of Korea.
Lee, S., & Jeon, S. (2009). An Empirical Comparison of Imputation Methods for Missing Data. KIIS Spring Conf. 19, 250–251.
Li, B., Delpha, C., Diallo, D., & Migan-Dubois A. (2019). Application of Artificial Neural Networks to photovoltaic fault detection and diagnosis: A review. Renewable and Sustainable Energy Reviewshttps://doi.org/10.1016/j.rser.2020.110512
Li, Z., Pan, Z., Li, Y., Yang, X., Geng, C., & Li, X. (2023). Advanced root mean square propagation with the warm-up algorithm for fiber coupling. Opt. Express, 31, 23974–23989.
Lian, J., & Douglas, L. (2015). Automatic Fault Detection and Diagnosis for Photovoltaic Systems using Combined Artificial Neural Network and Analytical Based Methods”; 978-1-4799-1959-8/15/ IEEE
Liu, H., Perera, A., Al-Naji, A., Boubaker, S., Kamel, S., Ghazouani, N., & Mellit, A. (2023). Assessment of Machine and Deep Learning Approaches for Fault Diagnosis in Photovoltaic Systems Using Infrared Thermography. Remote Sens. 2023, 15, 1686
Liu, Y., Ding, K., Zhang, J., Li, Y., Yang, Z., Zheng, W., & Chen, X. (2021). Fault Diagnosis Approach for Photovoltaic Array Based on the Stacked Auto-Encoder and Clustering with I-V Curves. Energy Convers. Manag. 2021, 245, 114603.
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, vol.133, no. 11, pp. 126–143.
Mekki, H., Mellit, A., & Salhi, H. (2016). Artificial neural network-based modelling and fault detection of partial shaded photo-voltaic modules. Simul. Model. Pract. Theory 67, 1–13.
Mellit, A., Tina, G.M., & Kalogirou, S.A. (2018). Fault detection and diagnosis methods for photovoltaic systems: a review. Renew Sustain Energy Rev 91:1–17
Mellit, A., Benghanem, M., Kalogirou, S., & Pavan A. (2023). An embedded system for remote monitoring and fault diagnosis of photovoltaic arrays using machine learning and the internet of things. Renewable Energy 208 (2023) 399–408 https://doi.org/10.1016/j.renene.2023.03.096
Mouaad, B., Samir, B., & Mohand, A. (2024). Dynamic Modeling for Fault Diagnosis in PV Systems Utilizing AI Techniques Based on Multilayer Perceptron. Conference: 2024 international conference in emerging technologies for sustainability and intelligent systems (icetsis 2024); doi: 10.1109/ICETSIS61505.2024.10459490
Rafique, H., & Rafique, S. (2016). Review of cross-correlated based algorithm or image and signal processing or pattern identification. International journal of Geomate; Vol 11; Issue 27; pp. 2695-2703.
Rao, S., Spanias, A., & Tepedelenlioglu, C. (2019). Solar array fault detection using neural networks. In Proceedings of the 2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS), Taipei, Taiwan, 6–9 May 2019; pp. 196–200.
Rouani, L., Harkat, M., Kouadri, A., & Mekhilef S. (2021). Shading fault detection in a grid-connected PV system using vertices principal component analysis. Renewable Energy 164 (2021) 1527e1539 https://doi.org/10.1016/j.renene.2020.10.059
Sairam, S., Seshadhri, S., Marafioti, G., Srinivasan, S., Mathisen, G., & Bekiroglu K., (2022). Edge-based Explainable Fault Detection Systems for photovoltaic panels on edge nodes. Renewable Energy 185 (2022) 1425e1440 https://doi.org/10.1016/j.renene.2021.10.063
Sarker, I.H. (2021). Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions. SN COMPUT. SCI. 2, 420 (2021). https://doi.org/10.1007/s42979-021-00815-1
Stähler, S.C., Sens-Schönfelder, C., & Niederleithinger, E. (2011). Monitoring stress changes in a concrete bridge with coda wave interferometry.J. Acoust. Soc. Am., vol. 129, no. 4, pp. 1945–1952, 2011., http://doi.org/10.1121/1.3553226.
Tina, G.M., Cosentino, F., &Ventura, C. (2016). Monitoring and diagnostics of photovoltaic power plants. In Renewable Energy in the Service of Mankind Volume II; Springer: Berlin, Germany, 2016; pp. 505–516.
Ul-Haq, A., Sindi, H.F., Gul, S., & Jalal, M. (2020). Modeling and fault categorization in thin-film and crystalline PV arrays through multilayer neural network algorithm. IEEE Access 8, 102235–102255.
Utama, C., Meske, C., Schneider, J., Schlatmann, R., & Ulbrich, C. (2023). Explainable artificial intelligence for photovoltaic fault detection: A comparison of instruments. Solar Energy 249 (2023) 139–151https://doi.org/10.1016/j.solener.2022.11.018
Wang, P., Li, C., Liang, R., Yoon, S., Mu, S., & Liu, Y. (2023). Fault detection and calibration for building energy system using Bayesian inference and sparse autoencoder: A case study in photovoltaic thermal heat pump system. Energy & Buildings 290 (2023) 113051 https://doi.org/10.1016/j.enbuild.2023.113051
Wang, W., Liu, A. C. -F., Chung, H. S. -H., R. Lau, W. -H., Zhang, J., & Lo, A. W. -L. (2016). Fault Diagnosis of Photovoltaic Panels Using Dynamic Current–Voltage Characteristics. IEEE Transactions on Power Electronics, vol. 31, no. 2, pp. 1588-1599, Feb. 2016,doi: 10.1109/TPEL.2015.2424079.
Weidong X. (2017). ‘‘Photovoltaic Power System Modelling, Design, and Control’’, 1st, Edition, John Wiley& Sons Ltd.
Yuan, Z., Xiong, G., & Fu, X. (2022). Artificial Neural Network for Fault Diagnosis of Solar Photovoltaic Systems: A Survey. Energies 2022, 15, 8693. https://doi.org/10.3390/ en15228693
Zhang, Y., Abraham, O., Grondin, F., Loukili, A., Tournat, V., Le Duff, A., Lascoup, B., & Durand, O. (2012). Study of stress-induced velocity variation in concrete under direct tensile force and monitoring of the damage level by using thermally-compensated coda wave interferometry. Ultrasonics, vol. 52, no. 8, pp. 1038–1045, 2012., http://dx.doi.org/10.1016/j.ultras.2012.08.011.
Zhang, Z., Ma, M., Ma, W., Zhang, R., & Wang, J. (2022). A data-driven photovoltaic string current mismatch fault diagnosis method based on I-V curve. Microelectronics Reliability 138 (2022) 114705 https://doi.org/10.1016/j.microrel.2022.114705
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Nigerian Journal of Technological Development
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
In accordance with the Copyright Act of 1976, which became effective January 1, 1978, the following statement signed by each author must accompany the manuscript submitted: "I, the undersigned author, transfer all copyright ownership of the manuscript referenced above to the Nigerian Journal of Technological Development, in the event the work is published. I warrant that the article is original, does not infringe upon any copyright or other proprietary right of any third party, is not under consideration by another journal, and has not been published previously. I have reviewed and approved the submitted version of the manuscript and agree to its publication in the Nigerian Journal of Technological Development." A copyright transfer form can be downloaded from the NJTD Website (http://njtd.com.ng/index.php/njtd). Author(s) will be consulted, whenever possible, regarding republication of material. All authors must have access to the data presented, and the authors and sponsor (if applicable) must agree to share original data with the editor if requested.
