ZAOZHUANG, China, Sept. 29, 2024 /PRNewswire/ -- The traditional manual detection is likely to be made towards the phase A in the lower layer; while the intelligent airborne detection is actually made towards the phase A in the upper layer. This represents the comparison result for the discharge hidden danger of the No. 23 tower insulator of the 10 kV cement plant line in the 110 kV Tendong Substation outgoing line by different detection methods, yet the accurate judgment brought by the innovative application of unmanned aerial vehicle airborne ultrasonic partial discharge detection technology.
By the end of August 12, the application of the self-developed UAV airborne ultrasonic partial discharge detection technology by State Grid Zaozhuang Power Supply Company has reached a year, during which, a total of 450 unmanned aerial vehicles were detected, 63 hidden hazards of partial discharge were identified, leading to a reduction of 37 equipment failures, the reduction of the power distribution network fault outage rate by 68%, and improving the power supply reliability rate to 99.982%.
According to Zhang Jianhua, Director of the Operation and Maintenance Department of Zaozhuang Power Supply Company, this technology is initiated in China, rewriting the tradition and passivity of power distribution network partial discharge fault investigation by hearing voice manually over a long time, and leaping into the era of intelligent imaging diagnosis. As the capillaries of the large power grid connecting thousands of households, the current average height of the distribution network tower is 15 to 18 meters, and both the insulators and cable heads on the top of these towers are important detection parts, the improvement in traditional manual detection methods is badly needed. To this end, they, by boldly integrating UAV with local imaging inspection technology, used the advantages of UAV multi-angle close-range inspection to carry out partial discharge inspection, innovated and broadened the technical dimension of aerial patrol, took the lead in enabling accurate collection of voiceprint local release data, and completed demonstration of putting the technology into practical application.
Innovation is not as simple as one plus one, the technology research took a year. Since June 2022, by means of hardware structure transformation and multi-algorithm fusion optimization, they have successively overcome a range of problems such as the inability of traditionally partial discharge inspection to lock the discharge part, the partial discharge detection of UAV propeller noise interference, and the geographical conditions of inspection, and enabled the high-quality and efficient partial discharge imaging detection of the power distribution network. In July 2023, the technology was put into trial use, and later in December of the same year, it was inspected and accepted by the State Grid Shandong Electric Power Company.
During the trial use, the Zao Zhuang Power Supply Company, by giving full play to its advantages as being directly managed and operated by State Grid Corporation of China, coordinated 162 power distribution network lines, and allocated 35 UAVs for the seven power supply centers affiliated to it in a unified manner, and trained 26 drone pilots. Beyond that, it repeatedly carried out technical verification and optimization in the trial use, reducing the time to inspect the base tower 1 from 25 minutes to 15 minutes, indicating an efficiency improvement by 1.8 times compared to the traditional manual inspection, making the accuracy reach 100%.
Instead of revolving around the tower, staring at the equipment for a long time, and being anxious but unable to do anything, Li Yanlin, the specialist staff from Operation and Maintenance Department of Zaozhuang Power Supply Company expressed the pleasure that thanks to the intelligent airborne detection technology, the partial discharge failures found in the power distribution network could be eliminated as soon as they are identified, leading to the great transformation of the operation and maintenance of distribution network from "eliminating present problems" to "preventing them before they are present", and the formation of a sound situation of intelligent operation and maintenance.
Read The Rest at :
