Wind turbine performance assessment using multi-regime modeling approach

Prognostics & health management system is an integral component of any wind energy program to ensure high turbine availability and reliability. Traditional vibration-based condition monitoring practices have been proposed to be utilized with wind turbines as they have demonstrated varying degrees of success with other rotary machinery. However, high-frequency data such as vibration and acoustic emission signals, generally, are not collected and recorded due to limitations with data storage capacities. In addition, the highly dynamic operating conditions of a wind turbine pose a challenge to conventional frequency domain analysis tools. Thus, a systematic framework that utilizes multi-regime modeling approach is proposed to consider the dynamic working conditions of a wind turbine. Three methods were developed, and they were evaluated using SCADA (supervisory control and data acquisition) data only that have been collected from a large-scale on-shore wind turbine for 27 months. Empirical observations from the results of the three methods indicate the ability of the approach to trend and assess turbine degradation prior to known downtime occurrences.

[1]  David G. Stork,et al.  Pattern Classification , 1973 .

[2]  E. J. Wiggelinkhuizen,et al.  Assessment of Condition Monitoring Techniques for Offshore Wind Farms , 2008 .

[3]  Andrew Kusiak,et al.  Models for monitoring wind farm power , 2009 .

[4]  J. Rafiee,et al.  INTELLIGENT CONDITION MONITORING OF A GEARBOX USING ARTIFICIAL NEURAL NETWORK , 2007 .

[5]  Teuvo Kohonen,et al.  The self-organizing map , 1990, Neurocomputing.

[6]  Linxia Liao.  An adaptive modeling for robust prognostics on a reconfigurable platform , 2010 .

[7]  Y. Amirat,et al.  Condition Monitoring and Fault Diagnosis in Wind Energy Conversion Systems: A Review , 2018 .

[8]  Sung-Hoon Ahn,et al.  Condition monitoring and fault detection of wind turbines and related algorithms: A review , 2009 .

[9]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[10]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[11]  James F. Manwell,et al.  Condition monitoring and prognosis of utility scale wind turbines , 2006 .

[12]  Ahmet Serdar Yilmaz,et al.  Pitch angle control in wind turbines above the rated wind speed by multi-layer perceptron and radial basis function neural networks , 2009, Expert Syst. Appl..

[13]  J. C. Lemm,et al.  Mixtures of Gaussian process priors , 1999 .

[14]  G. McLachlan.  Discriminant Analysis and Statistical Pattern Recognition , 1992 .

[15]  Vasile Palade,et al.  Computational Intelligence in Fault Diagnosis , 2010 .

[16]  Yaoyu Li,et al.  A review of recent advances in wind turbine condition monitoring and fault diagnosis , 2009, 2009 IEEE Power Electronics and Machines in Wind Applications.