Emerging Challenges and Technologies in Signal Processing for Prognostics and Health Management in Wind Energy

Preston Johnson
Submission Type: 
Full Paper
phmc_10_053.pdf1.18 MBSeptember 30, 2010 - 12:13pm

The use of condition monitoring (CM) in wind energy machines continues to evolve as wind energy machines grow in size and move offshore. Early and smaller wind generation machines offered little financial incentives for condition monitoring, justifying only simple and inexpensive health monitoring technologies. Today, multi-megawatt wind machines are more complex, more difficult to physically reach, and generate more revenue than previous models. This paper reviews challenges and candidate technologies for next generation condition monitoring in Wind Energy.

Larger wind turbines typically employ Doubly-Fed induction generators with gearbox based drive trains or direct drive generators with multi pole rotors and fixed stators. Both configurations employ variable speed wind driven rotors, variable due to wind speed. Fixed rotor speed signal processing techniques no longer work in a variable speed environment. Synchronous sampling, order analysis, wavelet filters, Cepstrum and related frequency analysis of sensor waveforms are examples of advanced feature extraction tools now available for up-tower condition monitoring systems to address the variable speed nature of modern wind turbines. These signal processing tools operate to reduce and preprocess sensory data prior to information extraction. With extracted features, performance prediction and health diagnostics analytics are able to produce machine degradation rate and levels.

This paper provides a tutorial of signal processing techniques for analysis of sensory information from variable speed rotary machines. The paper concludes with a discussion of prediction and diagnostics techniques which consume analysis results of previously mentioned signal processing techniques.

Publication Control Number: 
Submission Keywords: 
signal processing
Wind Turbine
Order Analysis
Time Synchronous Averaging
data driven prognostics
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