Key Information Extraction of Power Line Point Cloud Data Based on <scp>WOA</scp> ‐ <scp>NFP</scp> Algorithm

To enable fast and accurate extraction of power line information from transmission corridor point cloud data, an adaptive key information extraction method based on the WOA‐NFP algorithm is proposed. The method begins with the preprocessing of the point cloud data, followed by the extraction of power line feature points using WOA‐NFP. A clustering algorithm is then applied to classify the feature points, and connectivity analysis is conducted to isolate the point cloud of individual power lines. Spatial equations of the power lines are fitted from both the lead plane and the horizontal plane to facilitate key information extraction. To enhance the robustness of the fitting process, a catenary equation with the suspension point as the coordinate origin is adopted and validated. Experimental results show that the proposed method reduces the maximum arc sag error and maximum stress error by 71.4% and 96.2%, respectively, compared to traditional clustering‐based methods. Moreover, it improves time efficiency by 40.55% and reduces the mean peak error by 67.99% compared to existing approaches. These findings demonstrate that the proposed method significantly improves both the accuracy and efficiency of power line information extraction from point cloud data. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.