当前位置：
X-MOL 学术
›
Ocean Eng.
›
论文详情
Our official English website, www.x-mol.net, welcomes your
feedback! (Note: you will need to create a separate account there.)
Structural health monitoring of offshore wind power structures based on genetic algorithm optimization and uncertain analytic hierarchy process
Ocean Engineering ( IF 4.6 ) Pub Date : 2020-12-01 , DOI: 10.1016/j.oceaneng.2020.108201 Lin Zhou , Pengxiang Huang , Shukai Chi , Ming Li , Hu Zhou , Hongbin Yu , Hongda Cao , Kai Chen
Ocean Engineering ( IF 4.6 ) Pub Date : 2020-12-01 , DOI: 10.1016/j.oceaneng.2020.108201 Lin Zhou , Pengxiang Huang , Shukai Chi , Ming Li , Hu Zhou , Hongbin Yu , Hongda Cao , Kai Chen
Abstract Global offshore wind power is rapidly developing and has a broad market. China has the advantage of developing offshore wind power, which is the direction of future development of China's power generation industry. Because of the high maintenance and repair costs as well as the large sizes of wind power structures, damage to them could cause loss of lives and property. In this paper, a health monitoring method for analyzing offshore wind power structures based on a genetic algorithm and an uncertain analytic hierarchy process (AHP) is proposed. The uncertain analytic hierarchy process (AHP) is used to establish the hierarchical model. After calculating the weight range of each part, the optimal weight is obtained through the training and optimization of genetic algorithm, and the comprehensive weight table is obtained. Finally, the grading of health condition of the whole structure can be obtained by inputting the health indicators of each part based on the statistical distribution for weighted calculation. Based on the qualitative analysis of the uncertain analytic hierarchy process and the quantitative analysis of the genetic algorithm, the method is shown to reliably monitor the health of offshore wind power structures, reduce maintenance costs, and ensure staff safety. Both simulation and actual measurement experiments are performed in this study. The simulation based on the vibration data proves that the proposed structural health monitoring method for offshore wind power structures can evaluate the grading of health condition rapidly and accurately, using data in real time. Through the training and verification of simulation data, the accuracy of prediction after training can exceed 98%. Through a verification experiment using actual measured data, the vibration data measured by the offshore wind power structure during the healthy service period are evaluated. The evaluation results are found to be consistent with the actual operation state of the wind power structure. The proposed method can be used for quick, real-time evaluations.
更新日期：2020-12-01
京公网安备 11010802027423号