A high-fidelity stress sensing method of wind turbine tower via deformation function superposition with optimal strain gauge locations

A high-fidelity stress sensing method was developed in this study via linear superposition of quasi-static and dynamic deformation functions to address the bottleneck of existing stress field identification methods for wind turbine towers in balancing measurement accuracy and sensor economy. In this method, the principal components of structural response were clarified through wind load and structural modal analysis, i.e. the structural response contains quasi-static and low-frequency dynamic components. Then, the initial mathematical model for stress sensing of wind turbine tower was established via six static and eight dynamic deformation functions superposition and represented in the form of a system of linear equations, in which a large number of candidate strain gauge locations were considered. On this basis, the initial mathematical model was subjected to row dimension reduction through quantum genetic algorithm to reduce the number of strain gauges and eliminate the ill-posedness of the mathematical model. Additionally, the similarity of deformation functions was analyzed to eliminate approximately identical deformation functions, thereby reducing the column dimension of the initial mathematical model. Consequently, a high-fidelity stress sensing model can be obtained, in which only six single-phase strain gauges are used. Finally, numerical examples show that the high-fidelity stress sensing model can provide almost zero error results without measurement noise interference. When the measured noise is 30 dB, the maximum error of stress reconstruction results for wind turbine towers is significantly lower than existing methods at low wind speeds and low stress amplitudes. Therefore, this study provides a new and effective technological path to address the challenges of accuracy, cost, and reliability in online stress monitoring of wind turbine tower.