i-manager Publications

Photovoltaic Module Failure Detection using Machine Vision and Lazy Learning Technique

Judith Jancy D.*

CSI Institute of Technology, Thovalai, Kanyakumari, Tamil Nadu, India.

Periodicity:January - June'2024

Abstract

Solar module efficiency and dependability can be improved by detecting faults and monitoring their condition. This study examines the limitations and challenges of diagnosing solar module malfunctions. The various issues associated with solar module failure are thoroughly discussed. A monitoring tool that combines thermography and intelligent computing is developed to detect issues in photovoltaic cells while filtering out trivial anomalies based on a review of relevant studies. Given the rapid growth of solar energy, the Photovoltaic (PV) component's fault detection plays a crucial role in enhancing the reliability of the entire solar power system and identifying fault types whenever a system issue arises. As a result, this paper presents a hybrid strategy that employs the Chaotic Synchronization Detection Method (CSDM) and a Convolutional Neural Network (CNN) for PV module fault detection. With the explosive growth of the global Photovoltaic (PV) market, problem identification and resolution in PV systems have become equally important. Early issue detection can improve the efficiency, performance, and lifespan of a solar system. If PV flaws are not identified and addressed promptly, the plant's electricity production will be severely affected. Both on-site and online monitoring are possible for defect detection. Additionally, certain faults, such as ground faults, arc faults, line-to-line errors, and points of ignition, may pose fire hazards. Recently, researchers have proposed various methods for diagnosing and detecting PV issues. This paper provides an in-depth discussion of significant photovoltaic defects. It also highlights the distinctions, benefits, and drawbacks of the various fault detection approaches proposed in the available literature. A brief discussion of different solar cell modeling techniques is also included.

Keywords

Photovoltaic Module, Failure Detection, Machine Vision, Lazy Learning Technique, Solar Module Malfunctions, Solar Energy, Problem Identification.

How to Cite this Article?

Jancy, D. J. (2024). Photovoltaic Module Failure Detection using Machine Vision and Lazy Learning Technique. i-manager’s Journal on Circuits and Systems, 12(1), 27-35.

References

[1]. Aghaei, M., Madukanya, E. U., de Oliveira, A. K. V., & Rüther, R. (2018, December). Fault inspection by aerial infrared thermography in a pv plant after a meteorological tsunami. In Congresso Brasileiro de Energia Solar-CBENS (pp. 1-9).

[2]. Alajmi, M., Aljahdali, S., Alsaheel, S., Fattah, M., & Alshehri, M. (2019, September). Machine learning as an efficient diagnostic tool for fault detection and localization in solar photovoltaic arrays. In Proceedings of 32nd international conference on Computer Applications in Industry and Engineering, 63, 21-33.

[3]. Alfakih, M. A., Abdelbaky, M. A., & Saif, M. A. A. (2020). Photovoltaic Failure Diagnosis Using Sequential Probabilistic Neural Network Model. IEEE Access, 8, 220507-220522.

[4]. Ali, M. H., Rabhi, A., El Hajjaji, A., & Tina, G. M. (2017). Real time fault detection in photovoltaic systems. Energy Procedia, 111, 914-923.

[5]. Appiah, A. Y., Zhang, X., Ayawli, B. B. K., & Kyeremeh, F. (2019). Review and performance evaluation of photovoltaic array fault detection and diagnosis techniques. International Journal of Photoenergy, 2019(1), 6953530.

[6]. Dhimish, M., Holmes, V., & Dales, M. (2017). Parallel fault detection algorithm for grid-connected photovoltaic plants. Renewable Energy, 113, 94-111.

[7]. Ganeshprabu, B., & Geethanjali, M. (2016). Dynamic monitoring and optimization of fault diagnosis of photo voltaic solar power system using ANN and memetic algorithm. Circuits and Systems, 7(11), 3531-3540.

[8]. 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, 8, 159493-159510.

[9]. Haque, A., Bharath, K. V. S., Khan, M. A., Khan, I., & Jaffery, Z. A. (2019). Fault diagnosis of photovoltaic modules. Energy Science & Engineering, 7(3), 622-644.

[10]. Hu, Y., & Cao, W. (2016). Theoretical Analysis and Implementation of Photovoltaic Fault Diagnosis. Renewable Energy-Utilisation and System Integration.

[11]. Hu, Y., Cao, W., Ma, J., Finney, S. J., & Li, D. (2014). Identifying PV module mismatch faults by a thermography-based temperature distribution analysis. IEEE Transactions on Device and Materials Reliability, 14(4), 951-960.

[12]. Ji, D., Zhang, C., Lv, M., Ma, Y., & Guan, N. (2017). Photovoltaic array fault detection by automatic reconfiguration. Energies, 10(5), 699.

[13]. Kamath, H. G., Ekins-Daukes, N. J., Araki, K., & Ramasesha, S. K. (2018). Performance analysis and fault diagnosis method for concentrator photovoltaic modules. IEEE Journal of Photovoltaics, 9(2), 424-430.

[14]. Kim, G. G., Lee, W., Bhang, B. G., Choi, J. H., & Ahn, H. K. (2020). Fault detection for photovoltaic systems using multivariate analysis with electrical and environmental variables. IEEE Journal of Photovoltaics, 11(1), 202-212.

[15]. Kim, I. S. (2011, January). Fault detection algorithm of the photovoltaic system using wavelet transform. In India International Conference on Power Electronics 2010 (IICPE2010) (pp. 1-6). IEEE.

[16]. Kurukuru, V. B., Haque, A., & Khan, M. A. (2019, September). Fault classification for photovoltaic modules using thermography and image processing. In 2019 IEEE Industry Applications Society Annual Meeting (pp. 1-6). IEEE.

[17]. Kurukuru, V. S. B., Blaabjerg, F., Khan, M. A., & Haque, A. (2020). A novel fault classification approach for photovoltaic systems. Energies, 13(2), 308.

[18]. Li, Y. F., Lin, P. J., Zhou, H. F., Chen, Z. C., Wu, L. J., Cheng, S. Y., & Su, F. P. (2017, November). On-line monitoring system of PV array based on internet of things technology. In IOP Conference Series: Earth and Environmental Science, 93 (1), 012078. IOP Publishing.

[19]. Ma, M., Zhang, Z., Yun, P., Xie, Z., Wang, H., & Ma, W. (2021). Photovoltaic module current mismatch fault diagnosis based on IV data. IEEE Journal of Photovoltaics, 11(3), 779-788.

[20]. Muttillo, M., Nardi, I., Stornelli, V., de Rubeis, T., Pasqualoni, G., & Ambrosini, D. (2020). On field infrared thermography sensing for PV system efficiency assessment: Results and comparison with electrical models. Sensors, 20(4), 1055.

[21]. Pillai, D. S., & Rajasekar, N. (2018). A comprehensive review on protection challenges and fault diagnosis in PV systems. Renewable and Sustainable Energy Reviews, 91, 18-40.

[22]. Pillai, D. S., Blaabjerg, F., & Rajasekar, N. (2019). A comparative evaluation of advanced fault detection approaches for PV systems. IEEE Journal of Photovoltaics, 9(2), 513-527.

[23]. Rocky, A., Burhanzoi, M., Kenta, O., Ikegami, T., & Kawai, S. (2019). Photovoltaic module fault detection using integrated magnetic sensors. IEEE Journal of Photovoltaics, 9(6), 1783-1789.

[24]. Roy, S., Alam, M. K., Khan, F., Johnson, J., & Flicker, J. (2017). An irradiance-independent, robust ground-fault detection scheme for PV arrays based on spread spectrum time-domain reflectometry (SSTDR). IEEE Transactions on Power Electronics, 33(8), 7046-7057.

[25]. Sabbaghpur Arani, M., & Hejazi, M. A. (2016). The comprehensive study of electrical faults in PV arrays. Journal of Electrical and Computer Engineering, 2016(1), 8712960.

[26]. Schirone, L., Califano, F. P., Moschella, U., & Rocca, U. (1994, December). Fault finding in a 1 MW photovoltaic plant by reflectometry. In Proceedings of 1994 IEEE 1st World Conference on Photovoltaic Energy Conversion-WCPEC (A Joint Conference of PVSC, PVSEC and PSEC), 1, 846-849. IEEE.

[27]. Sun, J., Sun, F., Fan, J., & Liang, Y. (2017). Fault diagnosis model of photovoltaic array based on least squares support vector machine in Bayesian framework. Applied Sciences, 7(11), 1199.

[28]. Takashima, T., Yamaguchi, J., Otani, K., Kato, K., & Ishida, M. (2006, May). Experimental studies of failure detection methods in PV module strings. In 2006 IEEE 4th World Conference on Photovoltaic Energy Conference, 2, 2227-2230. IEEE.

[29]. Triki-Lahiani, A., Abdelghani, A. B. B., & Slama-Belkhodja, I. (2018). Fault detection and monitoring systems for photovoltaic installations: A review. Renewable and Sustainable Energy Reviews, 82, 2680-2692.

[30]. Wang, Z., & Balog, R. S. (2013, June). Arc fault and flash detection in DC photovoltaic arrays using wavelets. In 2013 IEEE 39th Photovoltaic specialists conference (PVSC) (pp. 1619-1624). IEEE.

[31]. Wang, Z., McConnell, S., Balog, R. S., & Johnson, J. (2014, June). Arc fault signal detection-Fourier transformation vs. wavelet decomposition techniques using synthesized data. In 2014 IEEE 40th Photovoltaic Specialist Conference (PVSC) (pp. 3239-3244). IEEE.

[32]. 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.

[33]. Yi, Z., & Etemadi, A. H. (2017). Line-to-line fault detection for photovoltaic arrays based on multiresolution signal decomposition and two-stage support vector machine. IEEE Transactions on Industrial Electronics, 64(11), 8546-8556.

[34]. Zhou, S., Mao, M., Zhou, L., Wan, Y., & Xi, X. (2020). A shadow fault diagnosis method based on the quantitative analysis of photovoltaic output prediction error. IEEE Journal of Photovoltaics, 10(4), 1158-1165.

[35]. Zhu, H., Lu, L., Yao, J., Dai, S., & Hu, Y. (2018). Fault diagnosis approach for photovoltaic arrays based on unsupervised sample clustering and probabilistic neural network model. Solar Energy, 176, 395-405.

If you have access to this article please login to view the article or kindly login to purchase the article

Single Article

	North Americas,UK, Middle East,Europe		India	Rest of world
	USD	EUR	INR	USD-ROW
Online	15	15

ADD TO CART

Options for accessing this content:

If you would like institutional access to this content, please recommend the title to your librarian.
If you already have i-manager's user account: Login above and proceed to purchase the article.
New Users: Please register, then proceed to purchase the article.