Abstract
Data clustering combined with multiobjective optimization has become attractive when the structure and the number of clusters in a dataset are unknown. Data clustering is the main task of exploratory data mining and a standard statistical data analysis technique used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. This project analyzes data to extract possible failure patterns in Solar Photovoltaic (PV) Panels. When managing PV Panels, preventive maintenance procedures focus on identifying and monitoring potential equipment problems. Failure patterns such as soiling, shadowing, and equipment damage can disturb the PV system from operating efficiently. We propose a multiobjective evolutionary algorithm that uses different distance functions to explore the conflicts between different perspectives of the problem. By the end, we obtain a non-dominated set, where each solution carries out information about a possible clustering structure. After that, we pursue a-posteriori analysis to exploit the knowledge of non-dominated solutions and enhance the fault detection process of PV panels.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ali, H.M.: Recent advancements in PV cooling and efficiency enhancement integrating phase change materials based systems-a comprehensive review. Sol. Energy 197, 163–198 (2020)
Dhimish, M., Holmes, V., Mehrdadi, B., Dales, M., Mather, P.: Photovoltaic fault detection algorithm based on theoretical curves modelling and fuzzy classification system. Energy 140, 276–290 (2017)
Zhu, H., Lu, L., Yao, J., Dai, S., Hu, Y.: Fault diagnosis approach for photovoltaic arrays based on unsupervised sample clustering and probabilistic neural network model. Sol. Energy 176, 395–405 (2018)
Tina, G.M., Cosentino, F., Ventura, C.: Monitoring and Diagnostics of Photovoltaic Power Plants. Springer International Publishing (2016)
Mellit, A., Tina, G., Kalogirou, S.: Fault detection and diagnosis methods for photovoltaic systems: a review. Renew. Sustain. Energy Rev. 91(February), 1–17 (2018)
Lazzaretti, A.E., da Costa, C.H., Rodrigues, M.P., Yamada, G.D., Lexinoski, G., Moritz, G.L., Oroski, E., de Goes, R.E., Linhares, R.R., Stadzisz, P.C., Omori, J.S., dos Santos, R.B.: A monitoring system for online fault detection and classification in photovoltaic plants. Sens. (Basel, Switz.) 20(17), 4688 (2020)
Et-taleby, A., Boussetta, M., Benslimane, M.: Faults detection for photovoltaic field based on k-means, elbow, and average silhouette techniques through the segmentation of a thermal image. Int. J. Photoenergy 2020, 1–7 (2020)
Cai, Y., Lin, P., Lin, Y., Zheng, Q., Cheng, S., Chen, Z., Wu, L.: Online photovoltaic fault detection method based on data stream clustering. IOP Conf. Ser. Earth Environ. Sci. 431(1), 012,060 (2020)
Zhao, Y., Ball, R., Mosesian, J., de Palma, J.F., Lehman, B.: Graph-based semi-supervised learning for fault detection and classification in solar photovoltaic arrays. IEEE Trans. Power Electron. 30(5), 2848–2858 (2015)
Zhu, X.J.: Semi-supervised learning literature survey. Tech. Rep. TR-1530, Dept. Comput. Sci., Univ. Wisconsin-Madison, Madison, WI, USA (2005)
Van Engelen, J.E., Hoos, H.H.: A survey on semi-supervised learning. Mach. Learn. 109(2), 373–440 (2020)
Mahela, O.P., Shaik, A.G.: Recognition of power quality disturbances using s-transform based ruled decision tree and fuzzy c-means clustering classifiers. Appl. Soft Comput. 59, 243–257 (2017)
Handl, J., Knowles, J.: An evolutionary approach to multiobjective clustering. IEEE Trans. Evol. Comput. 11(1), 56–76 (2007)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
MacQueen, J., et al.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297. Oakland, CA, USA (1967)
Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: an Introduction to Cluster Analysis. Wiley Series in Probability and Mathematical Statistics. Applied Probability and Statistics, vol. 344, pp. 68–125 (1990)
Kruskal, J.B.: On the shortest spanning subtree of a graph and the traveling salesman problem. Proc. Am. Math. Soc. 7(1), 48–50 (1956)
Rampazzo, P.C.B., Yamakami, A., de França, F.O.: Evolutionary approaches for the multi-objective reservoir operation problem. J. Control. Autom. Electr. Syst. 26(3), 297–306 (2015)
Mukhopadhyay, A., Maulik, U., Bandyopadhyay, S.: A survey of multiobjective evolutionary clustering. ACM Comput. Surv. (CSUR) 47(4), 1–46 (2015)
Acknowledgements
This work is financed by the ERDF—European Regional Development Fund, through the Operational Programme for Competitiveness and Internationalisation—COMPETE 2020 Programme under the Portugal 2020 Partnership Agreement, within project SmartPV, with reference POCI-01-0247-FEDER-068919.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Yamada, L., Rampazzo, P., Yamada, F., Guimarães, L., Leitão, A., Barbosa, F. (2023). Multiobjective Evolutionary Clustering to Enhance Fault Detection in a PV System. In: Almeida, J.P., Alvelos, F.P.e., Cerdeira, J.O., Moniz, S., Requejo, C. (eds) Operational Research. APDIO 2022. Springer Proceedings in Mathematics & Statistics, vol 437. Springer, Cham. https://doi.org/10.1007/978-3-031-46439-3_16
Download citation
DOI: https://doi.org/10.1007/978-3-031-46439-3_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-46438-6
Online ISBN: 978-3-031-46439-3
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)