Fuzzy Optimization Method for Wind Load Monitoring of Fixed Offshore Wind Turbines

Abstract

Fixed offshore wind turbines generally use statistical parameters of sea conditions in or near the target sea area as design benchmarks during the design phase. However, these sea state statistical parameters vary substantially from the actual sea state parameters experienced by offshore wind turbines. This situation brings uncertainty to the structural safety of offshore wind turbines. Online monitoring of wind loads exerted on the wind tower topside, which uses monitoring the structural response of the wind tower to determine wind load online, provides another technical approach for accurately measuring the wind loads exerted on the wind tower topside. However, online monitoring of wind loads can be affected by factors such as the ill-posedness of mathematical models and errors in monitoring data. The mathematical model for wind load monitoring is usually optimized only with the ill-posedness index as the objective function to reduce or overcome model ill-posedness. The error level of monitoring data is not considered in optimization, which likely leads to a high proportion of monitoring data error and then reduces the quality of monitoring data. Hence, a fuzzy optimization method was developed in this study to reduce the ill-posedness index of mathematical models and the influence of monitoring data errors on the monitoring results, in which the ill-posedness index of the mathematical models is weighted and unified with the error index of the monitoring data. In addition, the effectiveness and reliability of the method developed were demonstrated through numerical examples of jacket wind turbines, and structural damage can be accurately analyzed using the monitored wind loads.

Keywords

Wind load monitoring / Fixed offshore wind turbines / Fuzzy optimization / Ill-posedness index of mathematical models / Error index of monitoring data

RIGHTS & PERMISSIONS

Harbin Engineering University and Springer-Verlag GmbH Germany, part of Springer Nature