Power grid model classification identification method and system, electronic equipment and storage medium

Abstract

The invention discloses a power grid model classification identification method and system, electronic equipment and a storage medium, and the method comprises the following steps: SS1, maintaining stock model classification tags, generating classification tags and wiring diagrams, and storing the classification tags and the wiring diagrams into a training sample model library; step SS2, performing feature extraction on the training sample model library to form a model category feature library; and step SS3, performing model classification identification on the incremental primary equipment wiring diagram to generate a type label of the primary equipment model. According to the invention, training and feature extraction are carried out on a stock power system primary equipment wiring diagram with classification labels, classification features of various power system primary equipment models are identified from the diagram, a power grid model classification feature library is formed, and an effective power grid model classification and identification mechanism is established. Cost waste caused by manual data maintenance can be effectively avoided, data maintenance efficiency and accuracy are improved, and power grid operation safety is improved.

Description

technical field [0001] The invention relates to a grid model classification and identification method, system, electronic equipment and storage medium, belonging to the technical field of electric power automation. Background technique [0002] With the continuous expansion of the scale of the power grid, the types of power equipment and equipment continue to increase, and the maintenance work of the power primary equipment model is becoming increasingly heavy. It plays a very important role in the application. At present, the equipment classification information in the dispatching information system basically depends on the manual maintenance of the staff, which not only increases the workload of the dispatchers, but also affects the data accuracy of the classification information maintenance. The deep learning-based power grid model classification and identification method adopted in the present invention trains and extracts features from the wiring diagrams of the primar...

AI Technical Summary

Problems solved by technology

[0004] At present, the data maintenance of the primary equipment model of electric power is completed in the stage of the new investment process of the equipment. The classification information of the primary equipment such as the bus bar and the switch basically depends on the manual maintenance of the staff, resulting in a huge waste of labor costs and time, and may also be caused by human understanding deviations. Model correspondence errors may even affect the safety of power grid operations

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