Inverse data transformation for change detection in wind turbine diagnostics

A complex system is expected to show different nominal behaviors under different conditions, and the deviation over time from these nominal behaviors is an indicator of potential faults. The nominal behaviors are either default working states, or learned patterns from extensive historical data. Based on nominal behaviors, change detection is implemented for diagnostics, especially to help detect soft failures (which may degrade, but not preclude, equipment operation). A new technique, the inverse data transformation, is proposed in this paper, which simplifies the abnormality detection with a scaler decision threshold, and the fitting needs to be done only once; otherwise in direct deviation method, multiple curve fittings are required and the decision boundaries are curves, making the decisions on irregularly shaped decision regions difficult and inefficient. Wind turbine operational performance and power curve analysis is utilized as an application example of this technique. Th...

• Type: Conference
• Year: 2009
• Where: CCECE
• Authors: Yanjun Yan, Lisa Ann Osadciw, Glen Benson, Eric White
• Keywords: CCECE 2009, Electrical And Computer Engineering, Gaussian CDF, Inverse Data Transformation, Nominal Behavior