Effects of the pre-processing algorithms in fault diagnosis of wind turbines
The wind sectors pends roughly 2200M€ in repair the wind turbines failures. These failures do not contribute to the goal of reducing greenhouse gases emissions. The 25–35% of the generation costs are operation and maintenance services. To reduce this amount, the wind turbine industry is backing on t...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2018 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/336039 |
| Acceso en línea: | https://hdl.handle.net/2117/336039 https://dx.doi.org/10.1016/j.envsoft.2018.05.002 |
| Access Level: | acceso abierto |
| Palabra clave: | System failures (Engineering) Wind turbines--Maintenance and repair Machine learning Supervisory control systems Wind farms SCADA data Pre-processing Outliers Fault diagnosis Renewable energy Aerogeneradors -- Manteniment i reparació Avaries Aprenentatge automatic Àrees temàtiques de la UPC::Energies::Energia eòlica::Aerogeneradors Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
| Sumario: | The wind sectors pends roughly 2200M€ in repair the wind turbines failures. These failures do not contribute to the goal of reducing greenhouse gases emissions. The 25–35% of the generation costs are operation and maintenance services. To reduce this amount, the wind turbine industry is backing on the Machine Learning techniques over SCADA data. This data can contain errors produced by missing entries, uncalibrated sensors or human errors. Each kind of error must be handled carefully because extreme values are not always produced by data reading errors or noise. This document evaluates the impact of removing extreme values (outliers) applying several widely used techniques like Quantile, Hampel and ESD with the recommended cut-off values. Experimental results on real data show that removing outliers systematically is not a good practice. The use of manually defined ranges (static and dynamic) could be a better filtering strategy. |
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