Jun Shin Lee

A Novel Method and its Field Tests for Monitoring and Diagnosing Blade Health for Wind Turbines

A new diagnostic method is proposed to efficiently monitor the structural health and detect damages in wind turbine blades. A high-resolution real-time blade condition monitoring system that considers the harsh turbine operating environment and uses optical sensors and a wireless network is presented. A hybrid algorithm, which merges probabilistic analysis, design loads, and real-time load estimates, is introduced to enhance operational safety and reliability. Moreover, the alarm limits are updated every 10 min through a learning algorithm to further improve reliability. The proposed algorithm was implemented in a blade monitoring system. The effectiveness of the proposed algorithm was demonstrated for a 3-MW wind turbine in the Yeongheung wind farm.

Blade health monitoring and diagnosis method to enhance operational safety of wind turbine

In order to monitor blade health and detect any damage efficiently, a new diagnosis method for wind turbine blades was proposed. In consideration of harsh environments of a wind turbine rotor, high-resolution real-time blade condition monitoring was realized with the use of optic sensors and a wireless network. A hybrid algorithm, which merges a statistical method with model information, was introduced to overcome the weakness of each method. In addition, alarm limits are determined through a machine learning algorithm to enhance its reliability. The proposed algorithm was embedded in the Blade Health Monitoring and Integrity Evaluation System and was verified at a 3MW wind turbine of the Yeongheung wind farm.