Ms. Yu Ting Huang Profile

Yu Ting Huang

at NTU

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Proceedings Article | 8 March 2014 Paper

Application of image analysis and time-frequency analysis for tracking the rotating blades vibration

Yu-Ting Huang, Wan-Ying Hsiung, Yuan-Shen Yang, Chin-Hsiung Loh

Proceedings Volume 9061, 906111 (2014) https://doi.org/10.1117/12.2044353

KEYWORDS: 3D image processing, Sensors, Motion measurement, Image processing, Cameras, 3D metrology, Particles, Sensing systems, Image analysis, Wind turbine technology

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The objective of this paper is to investigate the application of the photogrammetric approach to measuring the vibration of a research-scale wind turbine blade model (both damage and undamaged blade). In order to control the excitation (rotation of the wind turbine blade), a motor was used to spin the blades at controlled angular velocities. Two cameras are set in front of the turbine to tape the video images. Through a sequence of stereo image pairs acquired by high speed camera, the images are studied. The camera we used is the BASLER acA2000-340km (2048x1088, 340FPS). Before taking the photos camera calibration was conducted which include lens distortion and skew factor is examined. To analyze the displacement of the motion target on the turbine blade, after loading the 3D calibration, the 3D positions are calculated by using a stereo triangulation technique. Then the displacement fields by image template matching can be calculated. Application of the technique to track the 3D motion of the rotating wind turbine blade is demonstrated by using data from the research-scale wind turbine. Different from the image processing technique data from the contact sensors (accelerometers) is also used. Through Rodrigues' rotation formula to remove the rotation frequency it is easy to extract the out-of-plane motion of the blade, from which the model frequency of the blade can be identified.

Proceedings Article | 8 March 2014 Paper

Analyzing the dynamic response of rotating blades in small-scale wind turbines

Wan-Ying Hsiung, Yu-Ting Huang, Chin-Hsiung Loh, Kenneth Loh, Robert Kamisky, Danny Nip, Cornelis van Dam

Proceedings Volume 9061, 90612A (2014) https://doi.org/10.1117/12.2044351

KEYWORDS: Stochastic processes, Earthquakes, Matrices, Data modeling, Spectrum analysis, Sensors, System identification, Signal processing, Lithium, Wind turbine technology

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The objective of this study was to validate modal analysis, system identification and damage detection of small-scale rotating wind turbine blades in the laboratory and in the field. Here, wind turbine blades were instrumented with accelerometers and strain gages, and data acquisition was achieved using a prototype wireless sensing system. In the first portion of this study conducted in the laboratory, sensors were installed onto metallic structural elements that were fabricated to be representative of an actual wind blade. In order to control the excitation (rotation of the wind blade), a motor was used to spin the blades at controlled angular velocities. The wind turbine was installed on a shaking table for testing under rotation of turbine blades. Data measured by the sensors were recorded while the blade was operated at different speeds. On the other hand, the second part of this study utilized a small-scale wind turbine system mounted on the rooftop of a building. The main difference, as compared to the lab tests, was that the field tests relied on actual wind excitations (as opposed to a controlled motor). The raw data from both tests were analyzed using signal processing and system identification techniques for deriving the model response of the blades. The multivariate singular spectrum analysis (MSSA) and covariance-driven stochastic subspace identification method (SSI-COV) were used to identify the dynamic characteristics of the system. Damage of one turbine blade (loose bolts connection) in the lab test was also conducted. The extracted modal properties for both undamaged and damage cases under different ambient or forced excitations (earthquake loading) were compared. These tests confirmed that dynamic characterization of rotating wind turbines was feasible, and the results will guide future monitoring studies planned for larger-scale systems.

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