CNN Based Fault Classification and Predition of 33kw Solar PV System with IoT Based Smart Data Collection Setup

CNN PV Fault IoT

Authors

  • K. Punitha P.S.R. Engineering College
  • G. Sivapriya Ramco Institute of Technology
  • T. Jayachitra Sharda University image/svg+xml

DOI:

https://doi.org/10.4108/ew.6074

Keywords:

CNN, Solar PV fault Classification, ESP32, Sensors, Google Sheet

Abstract

A Solar Photovoltaic (PV) System is an energy conversion system that uses the photovoltaic effect to convert sunlight into electricity. A fault in a Solar Photovoltaic (PV) system refers to any abnormal condition or defect that disrupts the normal operation and performance of the solar system. These faults can arise from a variety of factors, including environmental conditions, manufacturing defects, installation errors, and wear and tear of the components. Fault diagnosis in solar PV systems involves the detection, identification, and rectification of faults or abnormalities that can occur due to various reasons. By detecting and addressing faults early, systems can maintain optimal performance levels. Machine Learning (ML) in Solar Photovoltaic (PV) systems refers to the application of algorithms and statistical models that enable computers to perform specific tasks without using explicit instructions, relying instead on patterns and inference. In the context of solar PV systems, ML is used to analyse and interpret vast amounts of data generated by these systems to enhance their efficiency, predict energy production, detect and diagnose faults, and optimize maintenance and operation. By analysing data from sensors and system logs, ML algorithms can identify patterns indicative of faults or inefficiencies, such as shading, soiling, or equipment malfunctions, often before they become serious issues. Convolutional Neural Networks (CNNs) are a class of deep learning algorithms most commonly applied. They are particularly powerful for tasks involving data recognition, classification, and analysis due to their ability to automatically and adaptively learn spatial hierarchies of features. This research presents a unique machine learning model based fault diagnosis and detection method for a 33 KW solar PV system at P.S.R. Engineering College, Sivakasi. The real-time data from the PV system for five years, covering 23,000 instances of eight types of faults such as Cell Cracks or Hot Spots, Partial Shading, sensor fault, Module failure, Ground Faults, Communication Errors, Environmental Factors, Grid Connectivity Issues are collected. CNN is applied to the data and analysed their performance in terms of accuracy, precision, and standard deviation (SD)-score. It is found that CNN achieved the best results, with an accuracy of 98.7% a precision of 95%, a recall of 98%, and an F1 score of 96.5%. Therefore, CNN is used as the fault prediction also. The model is implemented using Python programming language and demonstrated its effectiveness on test cases. The smart data gathering system was achieved utilizing an ESP32 node with several sensors. The obtained data was stored in an authorized Google Sheet and compared to predetermined threshold ranges. When any parameter deviates from its threshold value, the ESP32 node starts a cooling and dust cleaning procedure with a water pump and drip pipe configuration. If the divergence persists, the ESP32 node activates a camera to capture an image of the panel and sends it to the Google Sheet via a connection for further analysis and fault correction.

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References

[1] 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 I-V characteristics. Applied Energy, 204, 912–931. https://doi.org/10.1016/j.apenergy.2017.05.034

[2] Mustafa, Z., Awad, A. S. A., Azzouz, M. A., & Azab, A. (2023). Fault identification for photovoltaic systems using a multi-output deep learning approach. Expert Systems With Applications, 211, 118551. https://doi.org/10.1016/j.eswa.2022.118551

[3] Oviedo, E. H. S., Travé-Massuyès, L., Subias, A., Pavlov, M., & Alonso, C. (2023b). Fault diagnosis of photovoltaic systems using artificial intelligence: A bibliometric approach. Heliyon, 9(11), e21491. https://doi.org/10.1016/j.heliyon.2023.e21491

[4] Van Every, P. M., Rodriguez, M., Jones, C., Mammoli, A., & Martínez‐Ramón, M. (2017). Advanced detection of HVAC faults using unsupervised SVM novelty detection and Gaussian process models. Energy and Buildings, 149, 216–224. https://doi.org/10.1016/j.enbuild.2017.05.053

[5] Gao, Y., Han, H., Ren, Z., Gao, J., Jiang, S., & Yang, Y. (2021). Comprehensive study on sensitive parameters for chiller fault diagnosis. Energy and Buildings, 251, 111318. https://doi.org/10.1016/j.enbuild.2021.111318

[6] Benkercha, R., & Moulahoum, S. (2018). Fault detection and diagnosis based on C4.5 decision tree algorithm for grid connected PV system. Solar Energy, 173, 610–634. https://doi.org/10.1016/j.solener.2018.07.089

[7] Pa, M., & Kazemi, A. (2022). A Fault Detection Scheme Utilizing Convolutional Neural Network for PV Solar Panels with High Accuracy. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2210.09226

[8] Lu, X., Lin, Y., Lin, P., He, X., Fang, G., Cheng, S., Chen, Z., & Wu, L. (2023). Efficient fault diagnosis approach for solar photovoltaic array using a convolutional neural network in combination of generative adversarial network under small dataset. Solar Energy, 253, 360–374. https://doi.org/10.1016/j.solener.2022.12.037

[9] Yokwana, X., Yusuff, A. A., Ntombela, M., & Mosetlhe, T. C. (2021). Fault detection scheme for a large-scale photovoltaic installation based on frequency response analysis. 2021 IEEE 6th International Forum on Research and Technology for Society and Industry (RTSI). https://doi.org/10.1109/rtsi50628.2021.9597310

[10] Ruiz-Moreno, S., Gallego, A. J., Sánchez, A., & Camacho, E. F. (2023b). A cascade neural network methodology for fault detection and diagnosis in solar thermal plants. Renewable Energy, 211, 76–86. https://doi.org/10.1016/j.renene.2023.04.051

[11] Jadidi, S., Badihi, H., & Zhang, Y. (2020). Fault Diagnosis in Microgrids with Integration of Solar Photovoltaic Systems:A Review. IFAC-PapersOnLine, 53(2), 12091–12096. https://doi.org/10.1016/j.ifacol.2020.12.763

[12] Khaled Osmani, Ahmad Haddad, Thierry Lemenand, Bruno Castanier, Mohammad Alkhedher, Mohamad Ramadan, A critical review of PV systems’ faults with the relevant detection methods, Energy Nexus,Volume 12, 2023, 100257, ISSN 2772-4271, https://doi.org/10.1016/j.nexus.2023.100257

[13] Faure, G., Vallée, M., Paulus, C., & Tran, Q. T. (2020b). Fault detection and diagnosis for large solar thermal systems: A review of fault types and applicable methods. Solar Energy, 197, 472–484. https://doi.org/10.1016/j.solener.2020.01.027

[14] 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

[15] Liu, Z., Liu, Y., Zhang, D., Cai, B., & Zheng, C. (2015). Fault diagnosis for a solar assisted heat pump system under incomplete data and expert knowledge. Energy, 87, 41–48. https://doi.org/10.1016/j.energy.2015.04.090

[16] Z. Ksira, A. Mellit, N. Blasuttigh and A. Massi Pavan, "A Novel Embedded System for Real-Time Fault Diagnosis of Photovoltaic Modules," in IEEE Journal of Photovoltaics, vol. 14, no. 2, pp. 354-362, March 2024, doi: 10.1109/JPHOTOV.2024.3359462.

[17] P, S. L., S, S. S., & R, M. S. (2023). IoT based solar panel fault and maintenance detection using decision tree with light gradient boosting. Measurement. Sensors, 27, 100726. https://doi.org/10.1016/j.measen.2023.100726

[18] Tradacete-Ágreda, M., Santiso-Gómez, E., Rodríguez-Sánchez, F. J., Hueros-Barrios, P. J., Jiménez-Calvo, J. A., & Santos, C. (2024). High-performance IoT Module for real-time control and self-diagnose PV panels under working daylight and dark electroluminescence conditions. Internet of Things, 25, 101006. https://doi.org/10.1016/j.iot.2023.101006

[19] Inomoto, R. S., Filho, A. J. S., Monteiro, J. R., & Costa, E. (2024). Genetic algorithm based tuning of sliding mode controllers for a boost converter of PV system using internet of things environment. Results in Control and Optimization, 14, 100389. https://doi.org/10.1016/j.rico.2024.100389

[20] Rajagopalan, A., Swaminathan, D., Bajaj, M., Damaj, I., Rathore, R. S., Singh, A. R., Blazek, V., & Prokop, L. (2024). Empowering power distribution: Unleashing the synergy of IoT and cloud computing for sustainable and efficient energy systems. Results in Engineering, 101949. https://doi.org/10.1016/j.rineng.2024.101949

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Published

12-12-2024

How to Cite

1.
Punitha K, Sivapriya G, Jayachitra T. CNN Based Fault Classification and Predition of 33kw Solar PV System with IoT Based Smart Data Collection Setup: CNN PV Fault IoT. EAI Endorsed Trans Energy Web [Internet]. 2024 Dec. 12 [cited 2024 Dec. 21];12. Available from: https://publications.eai.eu/index.php/ew/article/view/6074