MARC

LEADER 00000nab a2200000uu 4500
001 3194618479
003 UK-CbPIL
022 |a 2077-1312 
024 7 |a 10.3390/jmse13040629  |2 doi 
035 |a 3194618479 
045 2 |b d20250101  |b d20251231 
084 |a 231479  |2 nlm 
100 1 |a Kuang Renfei 
245 1 |a Research on Parameter Influence of Offshore Wind Turbines Based on Measured Data Analysis 
260 |b MDPI AG  |c 2025 
513 |a Journal Article 
520 3 |a Offshore wind turbines are prone to structural damage over time due to environmental factors, which increases operational costs and the risk of accidents. Early detection of structural damage through monitoring systems can help reduce maintenance costs. However, under complex external conditions and varying structural parameters, existing methods struggle to accurately and quickly detect damage. Understanding the factors that influence structural health is critical for effective long-term monitoring, as these factors directly affect the accuracy and timeliness of damage identification. This study comprehensively analyzed 5 MW offshore wind turbine measurement data, including constructing a digital twin model, establishing a surrogate model, and performing a sensitivity analysis. For monopile-based turbines, sensors in x and y directions were installed at four heights on the pile foundation and tower. Via Bayesian optimization, the finite element model’s structural parameters were updated to align its modal parameters with sensor data analysis results. The update efficiencies of different objective functions and the impacts of neural network hyperparameters on the surrogate model were examined. The sensitivity of the turbine’s structural parameters to modal parameters was studied. The results showed that the modal flexibility matrix is more effective in iteration. A 128-neuron, double-hidden-layer neural network balanced computational efficiency and accuracy well in the surrogate model for modal analysis. Flange damage and soil degradation near the pile mainly impacted the turbine’s health. 
651 4 |a East China Sea 
651 4 |a China 
653 |a Parameters 
653 |a Finite element method 
653 |a Offshore 
653 |a Environmental degradation 
653 |a Wind power 
653 |a Sensitivity analysis 
653 |a Operating costs 
653 |a Optimization 
653 |a Damage detection 
653 |a Monitoring systems 
653 |a Modal analysis 
653 |a Structural damage 
653 |a Structural health monitoring 
653 |a Damage 
653 |a Turbines 
653 |a Data analysis 
653 |a Construction 
653 |a Bayesian analysis 
653 |a Accuracy 
653 |a Renewable resources 
653 |a Soil degradation 
653 |a Maintenance costs 
653 |a Environmental factors 
653 |a Alternative energy sources 
653 |a Probability theory 
653 |a Turbine engines 
653 |a Piles 
653 |a Parameter sensitivity 
653 |a Flexibility matrix 
653 |a Data processing 
653 |a Pile foundations 
653 |a Wind measurement 
653 |a Neural networks 
653 |a Digital twins 
653 |a Sensors 
653 |a High rise buildings 
653 |a Wind turbines 
653 |a Economic 
653 |a Environmental 
700 1 |a Zhao, Jinhai 
700 1 |a Zhang, Tuo 
700 1 |a Li Chengyang 
773 0 |t Journal of Marine Science and Engineering  |g vol. 13, no. 4 (2025), p. 629 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3194618479/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch 
856 4 0 |3 Full Text + Graphics  |u https://www.proquest.com/docview/3194618479/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3194618479/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch