Damage severity assessment of a laboratory wind turbine blade using virus optimisation algorithm

This paper focuses on the development of a structural health monitoring (SHM) method for damage severity assessment of wind turbine blades. The methodology entails finite element model updating (FEMU) of a laboratory scale blade, accounting for uncertainty arising from measurement and modelling errors through incorporation of non-probabilistic fuzzy uncertainty quantification techniques. SHM involves continuous monitoring of the structure in operation, with dynamic responses obtained from the operational structure compared to those of the baseline with deviations in behaviour attributed to damage. In this research, the baseline model was calibrated through construction and minimisation of an objective function using a global optimisation algorithm (GOA) known as virus optimisation algorithm (VOA). Uncertainty in the blade Young’s modulus and shear modulus was quantified through the fuzzy FEMU process. Then, damage severity assessment was conducted through simulating multiple damage scenarios by addition of variable masses to the structure considered to cause localised changes whilst preventing permanent structural modification. In total, four single and one concurrent scenario were considered with 50g, 100g, 200g and 400g added individually to the trailing edge, with the last scenario adding 200g to both the trailing and the leading edge. This work investigated the use of VOA for fuzzy FEMU with optimal parameters of the algorithm considered and utilised for updating. The research was able to identify all five damage scenarios simulated on the structure, with sufficient accuracy and through uncertainty quantification the potential variation associated with these parameters were unrevealed.