Statistic Discrepancy Oriented Cyclo-Non-Stationary Indicator for Wind Turbine Condition Monitoring Under Varying...

Area of Science:

• Mechatronics
• Mechanical Engineering
• Signal Processing

Background:

• Wind turbines (WTs) are complex mechatronic systems crucial for reliable energy generation.
• Operating in variable wind conditions leads to environmental interference, impacting health monitoring.
• Existing indicators often suffer from false or missed alarms due to coupled condition interference.

Purpose of the Study:

• To develop a novel statistic discrepancy oriented cyclo-non-stationary (CNS) indicator for improved WT health state assessment.
• To enhance the reliability of mechatronic systems operating in dynamically varying environments.
• To address the deficiencies of current indicators in managing false alarms.

Main Methods:

• A multiparametric model was used to characterize degradation samples, with consistency verified by hypothesis testing.
• A speed-dependent slicing (SDS) operator was designed to mitigate varying-speed-induced modulation interference.
• A CNS indicator was developed using a resampling-based statistic discrepancy mechanism with the SDS operator.

Main Results:

• The proposed method effectively characterizes the health state of WT transmission parts.
• The CNS indicator demonstrates adaptability to dynamically varying operating environments.
• The SDS operator successfully alleviates modulation interference caused by speed variations.

Conclusions:

• The novel CNS indicator offers a reliable solution for monitoring WT health under fluctuating conditions.
• The developed method improves the accuracy and robustness of health state assessment in complex mechatronic systems.
• This approach enhances the overall reliability and sustained service of wind turbines.