Contaminants on wind turbine blades, whether organic (insects, birds, plant matter) or inorganic (soil, sand, salt), can negatively impact turbine performance.

Standoff laser ablation and LIBS in the vacuum ultraviolet (VUV) and ultraviolet visible (UV-Vis) spectral ranges were employed to investigate and detect contamination on wind turbine blades.

Study found that SVM performed the best among the tested ML algorithms, followed by PLS-DA, CNN, and CL. SVM demonstrated high accuracy and precision, making the LIBS spectra linearly separable when projected via principal component analysis (PCA).

Impact: AI has better chances of control for contaminants clear, and is way efficient.