Unleash the efficiency of data to facilitate a bumper tea harvest

"I didn't expect you to be able to detect the fault in advance. Otherwise, if the fault occurred, my tea leaves would have been in big trouble. Thank you so much," said Li, owner of the Xinzhai Tea Factory in Wuyi county, Jinhua city, Zhejiang province. On April 11, a data specialist from State Grid Jinhua Power Supply Company successfully determined that there was a low-voltage latent fault for the user of this tea factory, with the help of the intelligent diagnosis model for low-voltage latent faults. The operation and maintenance personnel went to the site and found that the incoming line terminal in the user's meter box had a phenomenon of overheating and oxidation. Then, they immediately arranged for repair personnel to carry out defect elimination treatment. This is the 86th low-voltage latent fault successfully handled by the data specialist of the power supply station this month.

Recently, during the tea-picking season, the aroma of tea permeates the air. As an important tea base in Zhejiang province, Wuyi county has seen tea farmers began picking spring tea leaves, and tea factories are welcoming the peak production period for tea frying. The local tea garden area is nearly 125,800 mu (8,386.67 hectares). It is expected that about 20,300 tons of tea will be produced this year, and the output value of the whole industrial chain is expected to exceed 4 billion yuan ($546.36 million). With the vigorous development of smart agriculture in Jinhua, many tea factories are gradually modernizing and upgrading traditional tea-making processes. They have shifted from the original method of stir-frying tea in a stove to an electric tea-frying process, making a reliable power supply particularly important.

State Grid Jinhua Power Supply Company applies an AI big data model. This AI big data model takes data features such as the user's voltage transient as judgment weights, and comprehensively studies and judges by combining data such as the voltage and current of users in the transformer area, the number of low-voltage occurrences, and the user's power outage records. According to the analysis results, the operation and maintenance personnel go to the site for targeted inspections, which greatly improves work efficiency, transforms "post-fault repair" into "pre-fault management," and effectively reduces the number of fault repair reports from users with low voltage. During the tea-making season, it has significantly reduced the investment of manpower and material resources for repairs, alleviated the pressure on grassroots power supply stations to ensure power supply, and improved the level of power supply reliability.