To simulate sheet metal forming processes precisely, an in-house dynamic explicit code was developed to apply a new solid-shell element to sheet metal forming analyses, with a corotational coordinate system utilized to simplify the nonlinearity and to integrate the element with anisotropic constitutive laws. The enhancing parameter of the solid-shell element, implemented to circumvent the volumetric and thickness locking phenomena, was condensed into an explicit form. To avoid the rank deficiency, a modified physical stabilization involving the B-bar method and reconstruction of transverse shear components was adopted. For computational efficiency of the solid-shell element in numerical applications, an adaptive mesh subdivision scheme was developed, with element geometry and contact condition taken as subdivision criteria. To accurately capture the anisotropic behavior of sheet metals, material models with three different anisotropic yield functions were incorporated. Several numerical examples were carried out to validate the accuracy of the proposed element and the efficiency of the adaptive mesh subdivision.

Regarding the typical operation tasks in high-voltage transmission line environment, in order to effectively eliminate or reduce the disadvantageous effects on the robot operation reliability caused by the close chain internal forces. According to the motion status of manipulator operation, the dual-arm coordination control of the mobile operation robot can be divided into three different modes, which are dual-arm independent operation mode, dual-arm fully constrained mode and dual-arm partially constrained mode. The dynamic model between dual-arm and operation objects for the three different models are established, respectively. The manipulator position and force are set as the control targets, a unified dual-arm coordination controller is designed for the three operation modes. Through the mutual compensation of force error and position error, the close chain internal force can be dynamically allocated in each joints, so that the robot joints force can be balanced during the operation, and the internal force in the close chain can be minimized during the operation process so as to ensure the operation reliability and safety. Finally, the validity and engineering practicability is verified by MATLAB simulation experiment and 220 kV living damper replacement and drainage plate bolt tightening operation experiment, respectively.

Purpose: To improve the operational efficiency and intelligence of live operation robots in dynamic-unstructured operation environments, this paper aims to propose a fuzzy logic-based method for the autonomous search and visual localization control of a manipulator end effector applied to a drainage plate bolt on a high-voltage transmission line. The proposed approach is based on a four-way video image information output from a dual-operation manipulator. Design/methodology/approach: First, based on the structural characteristics of the drainage line, an autonomous search method for the drainage plate bolt and a mapping relationship between the autonomous search control parameters and the relative posture of the operation manipulator-drainage line are proposed. The posture control parameters of the dual manipulators can then be obtained, and a two-dimensional fuzzy controller is designed with the posture offset distance and the posture offset angle as its input signals. This enables the localization control of the bolt and nut alignment to be realized through a visual process. Findings: The proposed fuzzy control algorithm is used for bolt location control, and its performance is compared with that of the conventional approach. The simulation results indicate that the fuzzy control algorithm greatly improves the localization accuracy and operational efficiency of live operation robots. Originality/value: Field operation experiments on actual transmission lines verify that the fuzzy control-based visual localization control of the robot manipulator has great engineering practicality. Therefore, the proposed method further improves operational intelligence compared with conventional algorithms.

Aiming at the problem that insulator image backgrounds are complex and fault types are diverse, which makes it difficult for existing deep learning algorithms to achieve accurate insulator fault diagnosis, an insulator fault diagnosis method based on multi-mechanism optimization YOLOv8-DCP is proposed. Firstly, a feature extraction and fusion module, named CW-DRB, was designed. This module enhances the C2f structure of YOLOv8 by incorporating the dilation-wise residual module and the dilated re-param module. The introduction of this module improves YOLOv8’s capability for multi-scale feature extraction and multi-level feature fusion. Secondly, the CARAFE module, which is feature content-aware, was introduced to replace the up-sampling layer in YOLOv8n, thereby enhancing the model’s feature map reconstruction ability. Finally, an additional small-object detection layer was added to improve the detection accuracy of small defects. Simulation results indicate that YOLOv8-DCP achieves an accuracy of 97.7% and an mAP@0.5 of 93.9%. Compared to YOLOv5, YOLOv7, and YOLOv8n, the accuracy improved by 1.5%, 4.3%, and 4.8%, while the mAP@0.5 increased by 3.0%, 4.3%, and 3.1%. This results in a significant enhancement in the accuracy of insulator fault diagnosis.

In complex smart grid, the power maintenance robot is important equipment to ensure the reliable operation of high-voltage lines and it is a useful exploration to realize high-quality power transmission. In view of the increasingly prominent contradiction between the robot single operation function and the diversification of power grid maintenance operations, additional with the robot weak autonomous operation and intelligent behavior ability, this paper proposes a new configuration of a reconfigurable power robot with terminal functions and its autonomous operation behavior control method for the three typical tasks which are the high-voltage transmission line insulators, drainage plates, and dampers maintenance. Through the analysis and planning of the robot operation behavior, the robot finite state machine (FSM) model in the three operation states has been established. Through the introduction of the state transfer function in the FSM, the automatic switching control between the robot key operation states can be realized, and the robot motion planning can be optimized. The movement and working flow of the robot improve the robot operation intelligence and operation efficiency. Based on this, the robot autonomous operation control system has been designed and the robot physical prototype has been developed for three maintenance tasks of insulators, drainage plates, and dampers. Finally, simulation experiments and field operation tests verify the effectiveness and engineering practicability of the proposed method. Compared with traditional manual control, the autonomous behavior control method can significantly improve the robot operational efficiency and operational intelligence. At the same time, the robot multitask function and autonomous behavior control under different tasks can be realized and the method has strong versatility for different task objects and different line environments. The research and its promotion have important theoretical significance and practical application value for the power system operation and maintenance integration management.