Go top
Paper information

A health condition model for wind turbine monitoring through neural networks and proportional hazard models

P. Mazidi, M. Du, L. Bertling Tjemberg, M.A. Sanz-Bobi

In this paper, a parametric model for health condition monitoring of wind turbines (HCWT) is developed. The study is based on the assumption that a wind turbine’s (WT) 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 timeseries 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 WT. In addition, a dynamic threshold for the model is developed to facilitate and add more insight in performance monitoring aspect. The HCWT model has capability of evaluating real-time and overall health condition of a WT 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.

Keywords: Wind Turbine, Condition Monitoring, Prognostics, Maintenance Management, Neural Networks

Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability. Volume: 231 Issue: 5 Pages: 481-494

JCR Impact Factor and Scopus quartile: 1.373 - Q2 (2017)

DOI reference: DOI icon 10.1177/1748006X17707902    

Published on paper: October 2017. Published on-line: May 2017.

    Topics research:
  • *Forecasting and data mining
  • *Modeling, simulation and optimization

PDF  Preview
Request Request the author to send the document

Aviso legal  |  Política de cookies |  Poítica de Privacidad

© Universidad Pontificia Comillas, Escuela Técnica Superior de Ingeniería - ICAI, Instituto de Investigación Tecnológica

Calle de Santa Cruz de Marcenado, 26 - 28015 Madrid, España - Tel: (+34) 91 5422 800