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Title:
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Identifying health status of wind turbines by using self organizing maps and interpretation-oriented post-processing tools
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Author:
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Blanco, Alejandro; Gibert, Karina; Marti Puig, Pere; Cusidó Roura, Jordi; Sole Casals, Jordi
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Other authors:
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Universitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa; Universitat Politècnica de Catalunya. Departament d'Enginyeria de Projectes i de la Construcció; Universitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic |
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Abstract:
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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. |
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Abstract:
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Peer Reviewed |
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Subject(s):
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-Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
-Multivariate analysis
-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
-Anàlisi multivariable
-Classificació AMS::62 Statistics::62H Multivariate analysis |
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Rights:
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Document type:
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Article - Published version
Article |
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