To mitigate the impact of the conductor’s inherent tension–torsion coupling effect on conductor quality during tension stringing, a method for tension–torsion analysis and structural parameter optimization of conductors is proposed based on the radial basis function neural network (RBFNN) surrogate model. The layer-wise lay ratios of conductors are selected as the structural parameters. Using the tension–torsion coupling computational method for conductors, the layer-wise lay ratios are sampled by Latin hypercube sampling (LHS) to construct the sample data by computing conductor torque under different combinations. The RBFNN surrogate model is trained with the data, and its shape parameter is optimized through Leave-One-Out Cross-Validation (LOOCV), achieving a coefficient of determination R² close to 1 with minimal errors. Targeting torque minimization, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is employed to identify the optimal combination of conductor lay ratio parameters, reducing conductor torque by approximately 18% under the same axial tension. For practical applications, prioritize the optimal combination for JL/G1A-630/45-45/7 and analogous conductors, and adopt the RBFNN model for rapid torque prediction. The proposed method also serves as a reference for design optimization of conductor structural parameters.

This paper investigates system identification algorithms for modal identification of frame structures, such as a suspension bridge and an overhead transmission line-crossing frame, using ambient vibration measurements. The modal identification procedures include two novel blind source separation (BSS) algorithms, complexity pursuit method (CP) and generalized eigen decomposition method (GED), based on modern signal processing technology. Here, the frequency response function (FRF) method is introduced as an important reference to verify the effectiveness of the CP algorithm and GED algorithm. The effectiveness and accuracy of both types of algorithms are verified by numerical simulations and experiments on a suspension bridge. In addition, an engineering application of these two BSS methods is successfully implemented in an overhead transmission line-crossing frame. The results show that the two novel BSS learning rules (CP and GED) are capable of successfully identifying modal parameters of the civil structure under ambient excitation.

The spacer is an important component of a transmission line and can effectively prevent wires from whipping each other and inhibit vibration. Given the complex installation conditions of multi-split lines, the installation of spacers is mainly achieved through manual work, which has the disadvantage of heavy labor intensity and a high risk factor. The robots that install two-split and four-split spacer bars cannot be applied to the complex operating conditions of six-split transmission lines. In order to improve the installation efficiency of spacers and reduce operating costs and risks, a new type of spacer-installing robot was researched based on the six-split transmission lines in this paper. Through the theoretical analysis of the wire’s arc sag, the moving device of the robot was designed. In order to improve the operating efficiency of the robot, the storage and feeding device of the six spacers was designed. A planar arm with the ability to assemble the spacer was designed. The overall design of the robot was completed by integrating the design of each unit. Through the experimental test, the results indicated that the robot was capable of installing six spacers at once, the maximum moving slope was 15 degrees, and the error rate in the spacer installation was 2.33%, which matched the manual installation of the spacers. The robot provided new ideas for the design of new transmission line engineering equipment and expanded the scope of the application of robots in the power industry.