Compensation of apparent strain data due to temperature gradients in a full-scale static test of a composite tidal turbine blade

Composite blades play a crucial role in a tidal turbine’s performance, and rigorous testing is required for certification before mass production. This study proposes a methodology for compensating strain measurements affected by temperature gradients using multivariate statistical regressions. This research utilises data analysis and signal processing techniques on strain and temperature datasets obtained from full-scale blade tests at FastBlade, the world’s first regenerative fatigue testing facility for tidal turbine blades. The multivariate regression model contains derived coefficients that separate
temperature-induced strains from mechanical strains based on strain gauges coupled to their nearest thermocouples available. The linear model’s performance is assessed against static test datasets with different temperature conditions and scenarios. The proposed compensation method accurately isolated mechanical strains from high temperature fluctuations from different sources acting on a large structure, enhancing the reliability of full-scale onshore tidal turbine blade testing.