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Health Condition Model for Wind Turbine Monitoring through Neural Networks and Proportional Hazard Models
KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems. Comillas Pontifical Unviersity.ORCID iD: 0000-0002-0396-3326
KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.ORCID iD: 0000-0003-4763-9429
Comillas Pontifical University.
2017 (English)In: Journal of Risk and Reliability, ISSN 1748-006X, E-ISSN 1748-0078Article in journal, Editorial material (Refereed) Published
Abstract [en]

In this article, a parametric model for health condition monitoring of wind turbines is developed. The study is based on the assumption that a wind turbine’s health condition can be modeled through three features: rotor speed, gearbox temperature and generator winding temperature. At first, three neural network models are created to simulate normal behavior of each feature. Deviation signals are then defined and calculated as accumulated time-series of differences between neural network predictions and actual measurements. These cumulative signals carry health condition–related information. Next, through nonlinear regression technique, the signals are used to produce individual models for considered features, which mathematically have the form of proportional hazard models. Finally, they are combined to construct an overall parametric health condition model which partially represents health condition of the wind turbine. In addition, a dynamic threshold for the model is developed to facilitate and add more insight in performance monitoring aspect. The health condition monitoring of wind turbine model has capability of evaluating real-time and overall health condition of a wind turbine which can also be used with regard to maintenance in electricity generation in electric power systems. The model also has flexibility to overcome current challenges such as scalability and adaptability. The model is verified in illustrating changes in real-time and overall health condition with respect to considered anomalies by testing through actual and artificial data.

Place, publisher, year, edition, pages
2017.
Keywords [en]
Wind turbine, condition monitoring, prognostics, maintenance management, neural networks
National Category
Engineering and Technology
Research subject
Electrical Engineering; Planning and Decision Analysis
Identifiers
URN: urn:nbn:se:kth:diva-219629ISI: 000411218300003Scopus ID: 2-s2.0-85029321409OAI: oai:DiVA.org:kth-219629DiVA, id: diva2:1164281
Funder
EU, European Research Council
Note

QC 20171218

Available from: 2017-12-11 Created: 2017-12-11 Last updated: 2018-03-12Bibliographically approved

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Mazidi, PeymanBertling, Lina

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