Spatial Visualization Monitoring Method of Tower Tilt of Isolated Transmission Lines Based on PSO-GRNN

• Proposes a PSO-GRNN-based method for spatial visualization of tower inclination in isolated transmission lines. • Integrates 3D laser scanning with collinear segment merging to derive tower central axes for robust model inputs. • Achieves 94–98% tilt detection accuracy (<4s) via PSO-optimized GRNN, outperforming existing methods (75–89%). • Generalization curves stabilize within 9 iterations (fitness=0.001), confirming high stability and rapid convergence. • Validates practical applicability for structural health monitoring with industrial-grade precision and speed. To provide spatial visualization of tower inclination, this study proposes a PSO-GRNN-based monitoring method for isolated transmission lines. A 3D laser scanner acquires point cloud data of the towers to reconstruct a 3D model. Tower segments are extracted using detection algorithms, and the central axis is determined by merging collinear segments and computing the primary contour. The axis data is then used as the network input. The GRNN network estimates the inclination, while the PSO algorithm optimizes the smoothing factor to improve prediction accuracy and generalization capability. Experimental results demonstrate that the PSO-GRNN model's generalization curve exhibits negligible fluctuations and converges after 9 iterations (fitness = 0.001). For the 14 test samples, the detected tilt angles align with the actual values, achieving 94-98% accuracy (detection time < 4 s), outperforming Reference [ 9 ] (81-89%) and [ 10 ] (75-80%). The proposed method exhibits high stability and measurement precision.