Modal analysis of an operational offshore wind turbine using enhanced Kalman filter‐based subspace identification

Operational modal analysis (OMA) is an essential tool for understanding the structural dynamics of offshore wind turbines (OWTs). However, classical OMA algorithms require excitation structure to be stationary white noise, which often not case operational OWTs due presence periodic caused by rotor rotation. To address this issue, several solutions have been proposed in literature, including Kalman filter-based stochastic subspace identification (KF-SSI) method eliminates harmonics through estimation and orthogonal projection. In paper, enhanced version KF-SSI presented that involves a concatenation step, allowing multiple datasets with similar environmental conditions used process, resulting higher precision. This framework applied OWT compared other methods, such as modified least-squares complex exponential PolyMAX. Using field data from multi-megawatt OWT, it shown able accurately distinguish first three bending modes more stable estimates lower variance original algorithm follows trend approaches.