Optimized Placement of Sensors on Large Wind Turbine Blades Based on Weighted Partition and Effective Independence Method

ABSTRACT Non‐destructive health monitoring is crucial for ensuring the safe operation of large wind turbine blades. This requires advanced sensor placement algorithms to monitor their structural behavior effectively. Existing optimization techniques often cause measurement points to cluster locally. This results in redundancy and complicates sensor placement and damage localization. This study partitions the blade surface using the k‐means clustering algorithm and calculates regional weights with the modal kinetic energy method to optimize the sensor distribution. It further optimizes sensor placement within each region by maximizing Fisher information matrix values through the effective independence method. The sensor placement strategy developed in this study offers a robust framework for non‐destructive monitoring of large wind turbine blades. Simulation results validate its ability to prevent the clustering of measurement points, minimize redundant information, and maximize the utilization of modal information under limited sensor resources.