Identifying Health Status of Wind Turbines by Using Self Organizing Maps and Interpretation-Oriented Post-Processing Tools

Alejandro Blanco-M., Karina Gibert, Pere Marti-Puig, Jordi Cusidó and Jordi Solé-Casals
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Alejandro Blanco-M.: Data and Signal Processing Group, U Science Tech, University of Vic-Central University of Catalonia, 08500 Vic, Catalonia, Spain
Karina Gibert: Department of Statistics and Operations Research, Universitat Politècnica de Catalunya-BarcelonaTech, Knowledge Engineering and Machine Learning Research group at Intelligent Data Science and Artificial Intelligence Research Center, UPC, 08034 Barcelona, Catalonia, Spain
Pere Marti-Puig: Data and Signal Processing Group, U Science Tech, University of Vic-Central University of Catalonia, 08500 Vic, Catalonia, Spain
Jordi Cusidó: Smartive Wind Turbine’s Diagnosis Solutions, 08204 Sabadell, Barcelona, Catalonia, Spain
Jordi Solé-Casals: Data and Signal Processing Group, U Science Tech, University of Vic-Central University of Catalonia, 08500 Vic, Catalonia, Spain

Energies, 2018, vol. 11, issue 4, 1-21

Abstract: Background: Identifying the health status of wind turbines becomes critical to reduce the impact of failures on generation costs (between 25–35%). This is a time-consuming task since a human expert has to explore turbines individually. Methods: To optimize this process, we present a strategy based on Self Organizing Maps, clustering and a further grouping of turbines based on the centroids of their SOM clusters, generating groups of turbines that have similar behavior for subsystem failure. The human expert can diagnose the wind farm health by the analysis of a small each group sample. By introducing post-processing tools like Class panel graphs and Traffic lights panels, the conceptualization of the clusters is enhanced, providing additional information of what kind of real scenarios the clusters point out contributing to a better diagnosis. Results: The proposed approach has been tested in real wind farms with different characteristics (number of wind turbines, manufacturers, power, type of sensors, ...) and compared with classical clustering. Conclusions: Experimental results show that the states healthy , unhealthy and intermediate have been detected. Besides, the operational modes identified for each wind turbine overcome those obtained with classical clustering techniques capturing the intrinsic stationarity of the data.

Keywords: wind farms; Supervisory Control and Data Acquisition(SCADA) data; self organizing maps (SOM); clustering; fault diagnosis; renewable energy; interpretation oriented tools; post- processing; data science (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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