Repositorio Institucional de Documentos

Resumen: The need for cost effective operation and maintenance (O&M;) strategies in wind farms has risen significantly with the growing wind energy sector. In order to decrease costs, current practice in wind farm O&M; is switching from corrective and preventive strategies to rather predictive ones. Anticipating wind turbine (WT) failures requires sophisticated models to understand the complex WT component degradation processes and to facilitate maintenance decision making. Environmental conditions and their impact on WT reliability play a significant role in these processes and need to be investigated profoundly. This paper is presenting a framework to assess and correlate weather conditions and their effects on WT component failures. Two approaches, using (a) supervised and (b) unsupervised data mining techniques are applied to pre-process the weather and failure data. An apriori rule mining algorithm is employed subsequently, in order to obtain logical interconnections between the failure occurrences and the environmental data, for both approaches. The framework is tested using a large historical failure database of modern wind turbines. The results show the relation between environmental parameters such as relative humidity, ambient temperature, wind speed and the failures of five major WT components: gearbox, generator, frequency converter, pitch and yaw system. Additionally, the performance of each technique, associating weather conditions and WT component failures, is assessed.
Idioma: Inglés
DOI: 10.1016/j.ress.2017.10.004
Año: 2018
Publicado en: RELIABILITY ENGINEERING & SYSTEM SAFETY 169 (2018), 554-559
ISSN: 0951-8320
Factor impacto JCR: 4.039 (2018)
Categ. JCR: OPERATIONS RESEARCH & MANAGEMENT SCIENCE rank: 11 / 84 = 0.131 (2018) - Q1 - T1
Categ. JCR: ENGINEERING, INDUSTRIAL rank: 5 / 46 = 0.109 (2018) - Q1 - T1
Factor impacto SCIMAGO: 1.944 - Applied Mathematics (Q1) - Safety, Risk, Reliability and Quality (Q1) - Industrial and Manufacturing Engineering (Q1)

Financiación: info:eu-repo/grantAgreement/EC/H2020/642108/EU/Advanced Wind Energy Systems Operation and Maintenance Expertise/AWESOME
Tipo y forma: Artículo (PostPrint)
Área (Departamento): Área Ingeniería Eléctrica (Dpto. Ingeniería Eléctrica)

Debe reconocer adecuadamente la autoría, proporcionar un enlace a la licencia e indicar si se han realizado cambios. Puede hacerlo de cualquier manera razonable, pero no de una manera que sugiera que tiene el apoyo del licenciador o lo recibe por el uso que hace. No puede utilizar el material para una finalidad comercial. Si remezcla, transforma o crea a partir del material, no puede difundir el material modificado.

Exportado de SIDERAL (2020-01-08-09:31:28)


Visitas y descargas

Este artículo se encuentra en las siguientes colecciones:
Artículos