The low-voltage metering box is a critical piece of equipment in the power supply system. The automated inspection of metering boxes is important in their production, transportation, installation, operation and maintenance. In this work, an automated type identification and size measurement method for low-voltage metering boxes based on RGB-D images is proposed. The critical components, including the door shell and window, connection terminal block, and metering compartment in the cabinet, are segmented first using the Mask-RCNN network. Then the proposed Sub-Region Closer-Neighbor algorithm is used to estimate the number of connection terminal blocks. Combined with the number of metering compartments, the type of metering box is classified. To refine the borders of the metering box components, an edge correction algorithm based on the Depth Difference (Dep-D) Constraint is presented. Finally, the automated size measurement is implemented based on the proposed Equal-Region Averaging algorithm. The experimental results show that the accuracies of the automated type identification and size measurement of the low-voltage metering box reach more than 92%.
There are many drawbacks and inconveniences in the application of human power in the energy meter calibration line. In order to achieve a standardized level of operation and to improve the efficiency and quality of the automated meter testing line, this paper applies the intelligent inspection robot to the automated meter testing line and discusses the key technologies involved. Based on the texture characteristics of the screws on the energy meter cover, a screw coordinate positioning method based on the texture center of gravity method is designed as a machine vision technique for intelligent inspection robots. Based on the feedforward controller transfer function and feedback system open-loop transfer function, combined with PI controller, a feedforward-feedback composite servo position control strategy is designed to complete the release action of the robot end controller. Pressure sensor on the robot end claw controller, integrated servo drive with current sensor. The Kalman filter method of static estimation is used to fuse and process multi-source data information to realize the grasping action of the robot end controller. The test results of the key technical performance and economic and time benefits of the robot show that the recognition success rate and grasping success rate of the robot for energy meters are as high as 100%, and it takes a total of 54s to complete each grasping and releasing action. The maximum error in each direction is 4.9mm, 5.2mm and 5.1mm respectively, and the maximum error in angle is only 1.25 degrees. The working manpower is reduced by as much as 93.16%, the average expenditure of inspection cost is only 1.24 yuan, and the floor space is reduced to 700 square meters. In summary, the study can ensure a high level of consistency in the quality of energy meters, improve the efficiency of calibration and production, and create greater economic benefits while providing a solid technical guarantee for the large-scale construction and stable and reliable operation of the power grid.
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