Evaluation and Application of Strain Expansion-Reduction Approach for Local Damage Identification of Offshore Wind Turbine Substructures / 應用應變響應擴展於離岸風機支撐結構局部破壞識別之可行性評估

碩士 / 國立臺灣大學 / 工程科學及海洋工程學研究所 / 107 / In order to achieve the energy policy of 2025 nuclear-free homeland, Taiwan has actively developed offshore wind power in recent years. The offshore wind turbine is located in the sea, the load is greater than that of the onshore wind turbine. It uses the support structure to carry various environmental loads and transmission forces. Once the support structure is damaged, it will be catastrophic failure. In order to monitor the health of offshore wind turbines and detect damage earlier, a reliable Structural Health Monitoring System (SHMS) is the focus of research. In current damage identification method, Modal parameter monitoring method is used in most cases, but the shortcoming of this method is that it cannot identify the small damage. This study proposes a method for damage identification based on Strain Expansion-Reduction Approach, which provides the ability to identify small damages in structures. The experiment was carried out through the acrylic frame test, and the artificial damage was made by cutting the notch, which proved the feasibility of the method and verified with the finite element simulation results. Finally, a case demonstration was carried out with a finite element model of a 3.6 MW offshore wind turbine jacket substructure to confirm the feasibility of this method in the real structure. The innovative damage identification method proposed by this research can effectively identify the occurrence of small damage and locate the location of the damage. It will be able to identify the damage early on the offshore wind turbine support structure, which is beneficial to improve the service life of the wind turbine.

Identiferoai:union.ndltd.org:TW/107NTU05345004
Date January 2019
CreatorsShang-En Zheng, 鄭尚恩
ContributorsHsin-Haou Huang, 黃心豪
Source SetsNational Digital Library of Theses and Dissertations in Taiwan
Languagezh-TW
Detected LanguageEnglish
Type學位論文 ; thesis
Format73

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