Applications of Machine Learning Algorithms for Photovoltaic Fault Detection: a Review

Abdelilah Et-taleby; Yassine Chaibi; Mohamed Benslimane; Mohammed Boussetta

doi:10.19139/soic-2310-5070-1537

Abdelilah Et-taleby Innovative Technologies Laboratory Sidi Mohamed Ben Abdellah University Fez
Yassine Chaibi Moroccan School of Engineering Sciences, Rabat, Morocco
Mohamed Benslimane Innovative Technologies Laboratory Sidi Mohamed Ben Abdellah University, Fez
Mohammed Boussetta Innovative Technologies Laboratory Sidi Mohamed Ben Abdellah University, Fez

DOI: https://doi.org/10.19139/soic-2310-5070-1537

Keywords: Machine Learning, photovoltaic system, PV fault detection, artificial neural network

Abstract

Over the years, the boom of technology has caused the accumulation of a large amount of data, famously known as big data, in every field of life. Traditional methods have failed to analyse such a huge pile of data due to outdated techniques. In recent times, the use of photovoltaic systems has risen worldwide. The arena Photovoltaic (PV) system has witnessed the same unprecedented expansion of data owing to the associated monitoring systems. However, the faults created within the PV system cannot be detected, classified, or predicted by using conventional techniques. This necessitates the use of modern techniques such as Machine Learning. Its powerful algorithms, such as artificial neural networks (ANN), help in the accurate detection and classification of faults in the PV system. This review paper introduces and evaluates the applications of Machine Learning (ML) algorithms in PV fault detection. It provides a brief overview of Machine Learning and its concepts along with various widely used ML algorithms. This review various peer-reviewed studies to investigate various models of ML algorithms in the PV system with the main focus on its fault detection accuracy and efficiency.

References

C.N. Modi, and K. Acha, Virtualization layer security challenges and intrusion detection/prevention systems in cloud computing: a comprehensive review, the Journal of Supercomputing, vol. 73, no. 3, pp. 1192–1234, 2017.

F. Y Osisanwo, J. E. T. Akinsola, O. Awodele, J. O. Hinmikaiye, O. Olakanmi and J. Akinjobi, Supervised machine learning algorithms: classification and comparison, International Journal of Computer Trends and Technology (IJCTT), vol. 48, no. 3, pp. 128–138, 2017.

H.M. El Misilmani, and T. Naous, Machine learning in antenna design: An overview on machine learning concept and algorithms, 2019 International Conference on High Performance Computing & Simulation (HPCS), IEEE, pp. 600–607, 2019.

K.Y. Ngiam, and W. Khor, Big data and machine learning algorithms for health-care delivery, The Lancet Oncology, vol. 50, no. 5, pp. e262–e273, 2019.

S. Rodrigues, R. Torabikalaki, F. Faria, N. Cafôfo, X. Chen, A.R. Ivaki, H. Mata-Lima, and F.J. Morgado-Dias Economic feasibility analysis of small scale PV systems in different countries, Solar Energy, vol. 131, pp. 81–95, 2016.

A.Y. Appiah , X. Zhang, B.B. Ayawli , and F. Kyeremeh Review and performance evaluation of photovoltaic array fault detection and diagnosis techniques, International Journal of Photoenergy, 2019.

Y.F. Li, P.J. Lin, H..F Zhou, Z.C. Chen, L.J. Wu, S.Y. Cheng, and F.P. Su, On-line monitoring system of PV array based on internet of things technology, IOP Conference Series: Earth and Environmental Science, vol. 93, pp. 012078, 2017.

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

V.S.B. Kurukuru, A. Haque, M.A. Khan, and A.K. Tripathy, Fault classification for photovoltaic modules using thermography and machine learning techniques, 2019 International Conference on Computer and Information Sciences (ICCIS), IEEE, 2019.

M. Chen, U. Challita, W. Saad, C Yin, and M. Debbah, Machine learning for wireless networks with artificial intelligence: A tutorial on neural networks, arXiv preprint arXiv:1710.02913, vol. 9, 2017.

S. Marsland, Machine learning: an algorithmic perspective, Chapman and Hall/CRC, 2011.

S. Shalev-Shwartz,and S. Ben-David , Understanding machine learning: From theory to algorithms, Cambridge university press, 2014.

A. Et-taleby, M. Boussetta, and M. Benslimane, Faults detection for photovoltaic field based on K-Means, Elbow, and Average Silhouette Techniques through the Segmentation of a Thermal Image, International Journal of Photoenergy, vol. 2020, 2020.

A. Burkov, The hundred-page machine learning book, Andriy Burkov Canada, 2019.

A. Et-taleby, Y. Chaibi, M. Boussetta, A. Allouhi and M. Benslimane, A novel fault detection technique for PV systems based on the K-means algorithm, coded wireless Orthogonal Frequency Division Multiplexing and thermal image processing techniques, Solar Energy, vol. 237, pp. 365–376, 2022.

C. Mantel, F. Villebro, G.A. dos Reis Benatto, H.R. Parikh, S. Wendlandt, K. Hossain, P. Poulsen, S. Spataru, D. Sera, and S. Forchhammer, Machine learning prediction of defect types for electroluminescence images of photovoltaic panels, Applications of Machine Learning, International Society for Optics and Photonics, vol. 11139, pp. 1113904, 2019.

S. Rao, A. Spanias, and C. Tepedelenlioglu, Solar array fault detection using neural networks, International Conference on Industrial Cyber Physical Systems (ICPS), IEEE, pp. 196–200, 2019.

F. Khondoker, S. Rao, A. Spanias, and C. Tepedelenlioglu, Photovoltaic array simulation and fault prediction via multilayer perceptron models, 2018 9th International Conference on Information, Intelligence, Systems and Applications (IISA), IEEE, pp. 1–5, 2018.

G. Muniraju, S. Rao, S. Katoch, A. Spanias, C. Tepedelenlioglu, P. Turaga, M.K. Banavar, and D. Srinivasan, A cyberphysical photovoltaic array monitoring and control system, Cyber Warfare and Terrorism: Concepts, Methodologies, Tools, and Applications, IGI Global, pp. 786–807, 2020.

U.S. Shanthamallu, A. Spanias, C. Tepedelenlioglu, and M. Stanley, A brief survey of machine learning methods and their sensor and IoT applications, 8th International Conference on Information, Intelligence, Systems & Applications (IISA), IEEE, pp. 1–8, 2017.

B. Basnet, H. Chun, and J. Bang, An intelligent fault detection model for fault detection in photovoltaic systems, Journal of Sensors, vol. 2020, 2020.

A.E. Lazzaretti, C.H. Costa, M.P. Rodrigues, G.D. Yamada, G. Lexinoski, G.L. Moritz, E. Oroski, R.E. Goes, R.R. Linhares, P.C. Stadzisz, and J.S. Omori, A monitoring system for online fault detection and classification in photovoltaic plants, Sensors, vol.20, no.17, pp.4688, 2020.

C.H. da Costa, M.P. Rodrigues, G.D. Yamada, G.V. Rodrigues, X. Jiayu, G.L. Moritz , E. Oroski, E. Rafael, A.E. Lazzaretti, and P.C. Stadzisz, A Monitoring and Management System for Medium-Scale Photovoltaic Plants, Innovative Smart Grid Technologies Conference-Latin America (ISGT Latin America), IEEE, pp.1–6, 2019.

A. Eskandari, J. Milimonfared, and M. Aghaei, Line-line fault detection and classification for photovoltaic systems using ensemble learning model based on IV characteristics, Solar Energy, vol.211, pp.354–365, 2020.

M. Dhimish, V. Holmes, B. Mehrdadi, and M. Dales, Comparing Mamdani Sugeno fuzzy logic and RBF ANN network for PV fault detection, Renewable energy, vol.117, pp.257–274, 2018.

K. Moloi, Y. Hamam, and J.A. Jordaan, Fault detection in power system integrated network with distribution generators using machine learning algorithms, 6th International Conference on Soft Computing & Machine Intelligence (ISCMI), IEEE, pp.18–22, 2019.

Á.H. Herraiz, A.P. Marugán, and F.P.G. Márquez, Photovoltaic plant condition monitoring using thermal images analysis by convolutional neural network-based structure, Renewable Energy, vol.153, pp.334–348, 2020.

M.W. Akram, G. Li, Y. Jin, X. Chen, C. Zhu, X. Zhao, A. Khaliq, M. Faheem, and A. Ahmad, CNN based automatic detection of photovoltaic cell defects in electroluminescence images, Energy, vol.189, pp.116319, 2019.

R. Pierdicca, E.S. Malinverni, F. Piccinini, M. Paolanti, A. Felicetti, and P. Zingaretti, DEEP CONVOLUTIONAL NEURAL NETWORK FOR AUTOMATIC DETECTION OF DAMAGED PHOTOVOLTAIC CELLS., International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, vol.42, no.2, 2018.

A.R. Espinosa, M. Bressan, and L.F. Giraldo, Failure signature classification in solar photovoltaic plants using RGB images and convolutional neural networks, Renewable Energy, vol.162, pp.249–256, 2020.

C.H. da Costa, G.L. Moritz, and A.E. Lazzaretti, A comparison of machine learning-based methods for fault classification in photovoltaic systems, Innovative Smart Grid Technologies Conference-Latin America (ISGT Latin America), IEEE, pp.1–6, 2019.

S.R. Madeti, and S.N. Singh, Modeling of PV system based on experimental data for fault detection using kNN method, Solar Energy, vol. 173, pp.139–151, 2018.

H.P.C. Hwang, C.C.Y. Ku, and J.C.C. Chan, Detection of Malfunctioning Photovoltaic Modules Based on Machine Learning Algorithms, IEEE Access, vol. 9, pp.37210–37219, 2021.

C.N. Modi, and K. Acha, Virtualization layer security challenges and intrusion detection/prevention systems in cloud computing: a comprehensive review, the Journal of Supercomputing, vol.73, no.3, pp.1192–1234, 2017.

A. Et-taleby, Y. Chaibi, A. Allouhi, M. Boussetta and M. Benslimane, A combined convolutional neural network model and support vector machine technique for fault detection and classification based on electroluminescence images of photovoltaic modules, Sustainable Energy, Grids and Networks, vol.32, pp.100946, 2022.