Abstract
Renewal energies are key to face the challenges of climate change. The power generation using Wind Turbines (WT) is among the technologies with higher growth during the last year. The Operation and Maintenance (OM) of WT using condition-monitoring (CM) to minimize failures determine the cost of the produced energy and therefore its efficiency. In this work, we present the design of a Fault Detection System (FDS) for WTCM using Complex Event Processing (CEP) technology to analyze the data streams of a wind farm in real-time. Data streams are provided by the sensors and the Supervisory Control and Data Acquisition (SCADA) system installed in the WT farms. This information is analyzed to determine failures using the stability in the produced power. Changes in this stability are detected by CEP patterns deployed in a CEP engine. A real case scenario is used to illustrate this design. It consists of 30 WTs operated by a private company and shows how this approach can help to plan operation and maintenance actions.
This work was supported in part by the Spanish Ministry of Science and Innovation and the European Union FEDER Funds under grants PID2021-122215NB-C32, and the UCLM group research grant cofinanced with European Union FEDER funds with reference 2022-GRIN-34113.
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
Similar content being viewed by others
Notes
- 1.
In this paper we use ESPER EPL to define and process the patterns.
- 2.
This diagram has been designed using images from Flaticon.com.
References
CPN Tools. http://www.cpntools.org/. Accessed 15 Mar 2023
Ait-Alla, A., Lütjen, M., Lewandowski, M., Freitag, M., Thoben, K.D.: Real-time fault detection for advanced maintenance of sustainable technical systems. Procedia CIRP 41, 295–300 (2016). https://doi.org/10.1016/j.procir.2016.01.015
Betz, A.: Wind-Energie und Ihre Ausnutzung durch Windmühlen. Göttingen, Germany (1926)
Boubeta-Puig, J., Ortiz, G., Medina-Bulo, I.: MEdit4CEP: a model-driven solution for real-time decision making in SOA 2.0. Knowl.-Based Syst. 89, 97–112 (2015). https://doi.org/10.1016/j.knosys.2015.06.021
Brazález, E., Maciá, H., Díaz, G., Baeza_Romero, M., Valero, E., Valero, V.: Fume: an air quality decision support system for cities based on cep technology and fuzzy logic. Appl. Soft Comput. 129, 109536 (2022). https://doi.org/10.1016/j.asoc.2022.109536
Chandy, K., Schulte, W.: Event Processing: Designing IT Systems for Agile Companies, 1st edn. McGraw-Hill Inc., New York (2010)
Cugola, G., Margara, A.: Processing flows of information: from data stream to complex event processing. ACM Comput. Surv. 44(3), 1–62 (2012). https://doi.org/10.1145/2187671.2187677
EsperTech: Esper EPL Online. http://esper-epl-tryout.appspot.com/epltryout/index.html. Accessed 15 Mar 2023
Fischer, K., Besnard, F., Bertling, L.: Reliability-centered maintenance for wind turbines based on statistical analysis and practical experience. IEEE Trans. Energy Conv. 27(1), 184–195 (2012). https://doi.org/10.1109/TEC.2011.2176129
Khodabakhsh, A., Ari, I., Bakir, M., Ercan, A.O.: Multivariate sensor data analysis for oil refineries and multi-mode identification of system behavior in real-time. IEEE Access 6, 63489–64405 (2018). https://doi.org/10.1109/ACCESS.2018.2877097
Luckham, D.C.: The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems. Addison-Wesley Longman Publishing Co., Inc., Boston (2001)
Ma, J., Yuan, Y.: Application of SCADA data in wind turbine fault detection - a review. Sensor Rev. 43(1), 1–11 (2023). https://doi.org/10.1108/SR-06-2022-0255
Marti-Puig, P., Cusidó, J., Lozano, F.J., Serra-Serra, M., Caiafa, C.F., Solé-Casals, J.: Detection of wind turbine failures through cross-information between neighbouring turbines. Appl. Sci. (Switzerland) 12(19), 9491 (2022). https://doi.org/10.3390/app12199491
Mehdi, G., et al.: Model-based approach to automated calculation of key performance indicators for industrial turbines. In: Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM, pp. 632–639 (2015). https://doi.org/10.36001/phmconf.2015.v7i1.2599
Méndez, M., Merayo, M.G., Núñez, M.: Long-term traffic flow forecasting using a hybrid cnn-bilstm model. Eng. Appl. Artif. Intell. 121, 106041 (2023). https://doi.org/10.1016/j.engappai.2023.106041
Pandit, R., AstolfC;, D., Hong, J., Infield, D., Santos, M.: SCADA data for wind turbine data-driven condition/performance monitoring: a review on state-of-art, challenges and future trends. Wind Eng. I(20), 0309524X221124031 (2022). https://doi.org/10.1177/0309524X221124031
Tchakoua, P., Wamkeue, R., Ouhrouche, M., Slaoui-Hasnaoui, F., Tameghe, T.A., Ekemb, G.: Wind turbine condition monitoring: state-of-the-art review, new trends, and future challenges. Energies 7(4), 2595–2630 (2014). https://doi.org/10.3390/en7042595
Valero, V., Díaz, G., Boubeta-Puig, J., Macià, H., Brazález, E.: A compositional approach for complex event pattern modeling and transformation to colored petri nets with black sequencing transitions. IEEE Trans. Softw. Eng. 48(7), 2584–2605 (2022). https://doi.org/10.1109/TSE.2021.3065584
Verbruggen, T.W.: Wind Turbine Operation and Maintenance based on Condition Monitoring WT-omega. Final report. Technical Report ECN-C-03-047 (2003). https://www.osti.gov/etdeweb/biblio/20376548
Vidal, Y.: Artificial intelligence for wind turbine condition monitoring. Energies 16(4), 1632 (2023). https://doi.org/10.3390/en16041632
Wen, W., Liu, Y., Sun, R., Liu, Y.: Research on anomaly detection of wind farm scada wind speed data. Energies 15(16), 5869 (2022). https://doi.org/10.3390/en15165869
Yang, W., Court, R., Jiang, J.: Wind turbine condition monitoring by the approach of scada data analysis. Renew. Energy 53, 365–376 (2013). https://doi.org/10.1016/j.renene.2012.11.030
Acknowledgement
Thanks to Ingeteam Power Technology S.A for its collaboration, specially regarding the data used in this work. This work was envisioned and performed during Enrique Brazález’s intership at this company.
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
Brazález, E., Díaz, G., Macià, H., Valero, V. (2023). Designing a Fault Detection System for Wind Turbine Control Monitoring Using CEP. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2023. Lecture Notes in Computer Science, vol 14135. Springer, Cham. https://doi.org/10.1007/978-3-031-43078-7_25
Download citation
DOI: https://doi.org/10.1007/978-3-031-43078-7_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43077-0
Online ISBN: 978-3-031-43078-7
eBook Packages: Computer ScienceComputer Science (R0)