State assessment of 110–220 kV intelligent substation based on multisensor fusion algorithm control and image vision

Abstract

With the development of modern information technology, intelligent substation technology has been widely used, which greatly promotes the development of power grid. The information integration platform of intelligent substations realizes panoramic data collection and data sharing of substations. With the continuous development of the economy and society, it is crucial to improve the power regulation ability of intelligent substations, make scientific use of intelligent technology, and optimize the patrol level of intelligent substation patrol robots to better meet the operation needs of substations. In the actual substation construction, the flexibility of equipment utilization in intelligent substations should be fully considered. In addition, the number of substations should be controlled to improve the utilization efficiency of patrol robots. This paper mainly summarizes the monitoring technology of intelligent substations, the positioning technology of inspection robots, and the multi-sensor control technology to lay a foundation for the follow-up inspection work of intelligent substations.

GNSS-Based Narrow-Angle UV Camera Targeting: Case Study of a Low-Cost MAD Robot

“…For the last decade, multiple robots for the non-destructive inspection of high-voltage power equipment have been introduced [ 1 , 2 , 3 , 4 , 5 ]. Most of these robots combine a mobile platform and a pan-and-tilt rotation device to target one or several cameras.…”

Research on stitching technology based on enhanced images of substations

“…Image stitching is one of the key methods in image processing [4] . High-quality splicing effect map can help operators to correctly deal with the problems encountered in substation operation, realize the visual monitoring of important locations, and ensure the safe and stable operation of substations [5] . This paper proposes a stitching algorithm based on image enhancement, firstly, the image of the substation at night is gamma corrected to enhance the detail information, and then the image is panned and rotated by FM to obtain the overlapping area, which reduces the area of feature extraction and the feature extraction time; the feature points are coarsely matched using the Fast Approximate Nearest Neighbour (FLANN) matching algorithm, and Progressive Sample Consensus (PROSAC) to eliminate false matches, which effectively improves the matching accuracy; finally, the stitched images are weighted and fused.…”