Data-driven learning framework for associating weather conditions and wind turbine failures

•A novel framework is presented to analyse big data and obtain meaningful rules.•Apriori rule mining and k-means clustering techniques are employed.•A case study is presented based on real operational wind turbine data.•The effect of weather conditions on component failures is discussed.•Gearbox, ge...

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Bibliographic Details
Published in:	Reliability engineering & system safety Vol. 169; pp. 554 - 569
Main Authors:	Reder, Maik, Yürüşen, Nurseda Y., Melero, Julio J.
Format:	Journal Article
Language:	English
Published:	Barking Elsevier Ltd 01-01-2018 Elsevier BV
Subjects:	Agricultural practices Ambient temperature Artificial intelligence Association rule mining Big data Data mining Data processing Decision making Environmental conditions Environmental impact Failure Failure analysis Frequency converters k-means clustering Machine learning Operation & maintenance Pitch (inclination) Relative humidity Reliability aspects Reliability engineering Turbines Weather Wind farms Wind power Wind speed Wind turbine Wind turbines Yaw Weather Association rule mining k-means clustering Operation & maintenance Machine learning Big data Data mining Wind turbine Failure
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Summary:	•A novel framework is presented to analyse big data and obtain meaningful rules.•Apriori rule mining and k-means clustering techniques are employed.•A case study is presented based on real operational wind turbine data.•The effect of weather conditions on component failures is discussed.•Gearbox, generator, converter, pitch and yaw system are analysed. 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.
ISSN:	0951-8320 1879-0836
DOI:	10.1016/j.ress.2017.10.004