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Purpose Overhead high-voltage transmission line (HVTL) inspection robots are used to inspect the transmission lines and/or maintain the infrastructures of a power transmission grid. One of the most serious problems is that the load on the front wheel is much larger than that on the back one when the robot travels along a sloping earth wire. Thus, ongoing operation of the inspection robot mainly depends on the front wheel motor’s ability. This paper aims to extend continuous operation time of the HVTL inspection robots. Design/methodology/approach By introducing a traction force model, the authors have established a dynamic model of the robot with slip. The total load is evenly distributed to both wheels. According to the traction force model, the desired wheel slip is calculated to achieve the goal of load balance. A wheel slip controller was designed based on second-order sliding-mode control methodology. Findings This controller accomplishes the control objective, such that the actual wheel slip tracks the desired wheel slip. A simulation and experiment verify the feasibility of the load balance control system. These results indicate that the loads on both wheels are generally equal. Originality/value By balancing the loads on both wheels, the inspection robot can travel along the earth wire longer, improving its efficiency.

Manually monitoring the safety of personnel working on high-voltage lines is difficult and unreliable. To overcome this problem, this paper proposes an online high-voltage live safety monitoring and early warning system based on spatial crossborder prevention. To develop the proposed system, following analysis of high-voltage typical live working modes and the minimum approach distance, we divide the safe operation space and use a time-of-flight (TOF) depth camera to measure the live working distance in real time. This enables us realize safety early warning by detecting the boundary crossing and intrusion of the safe operation space. Subsequently, we analyze the ambient light and noise that affect the imaging quality of the TOF camera, evaluate the confidence of the depth image pixels, and apply a purpose-designed adaptive filter guided by a confidence map to repair the depth images. The results of field experiments conducted verify that the proposed system accurate and reliable of this system through field experiments. Compared with manual observations, the system can significantly improve the effectiveness of real-time live working safety monitoring, and can effectively prevent the occurrence of live working personal injury accidents.